A survey of music similarity and recommendation from music context data
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paper_title: A music search engine built upon audio-based and web-based similarity measures

paper_content:

An approach is presented to automatically build a search engine for large-scale music collections that can be queried through natural language. While existing approaches depend on explicit manual annotations and meta-data assigned to the individual audio pieces, we automatically derive descriptions by making use of methods from Web Retrieval and Music Information Retrieval. Based on the ID3 tags of a collection of mp3 files, we retrieve relevant Web pages via Google queries and use the contents of these pages to characterize the music pieces and represent them by term vectors. By incorporating complementary information about acous tic similarity we are able to both reduce the dimensionality of the vector space and improve the performance of retrieval, i.e. the quality of the results. Furthermore, the usage of audio similarity allows us to also characterize audio pieces when there is no associated information found on the Web.
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paper_title: MusicSun: A New Approach to Artist Recommendation

paper_content:

MusicSun is a graphical user interface to discover artists. Artists are recommended based on one or more artists selected by the user. The recommendations are computed by combining 3 different aspects of similarity. The users can change the impact of each of these aspects. In addition words are displayed which describe the artists selected by the user. The user can select one of these words to focus the search on a specific direction. In this paper we present the techniques used to compute the recommendations and the graphical user interface. Furthermore, we present the results of an evaluation with 33 users. We asked them, for example, to judge the usefulness of the different interface components and the quality of the recommendations.
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paper_title: Scaling up music playlist generation

paper_content:

The issue of generating automatically sequences of music titles that satisfy arbitrary criteria such as user preferences has gained interest recently, because of the numerous applications in the field of electronic music distribution. All the approaches proposed so far suffer from two main drawbacks: reduced expressiveness and incapacity to handle large music catalogues. We present in this paper a system that is able to produce automatically music playlists out of large, real catalogues (up to 200000 titles), and that can handle arbitrarily complex criteria. We describe the basic algorithm and its adaptation to playlist generation, and report on experiments performed in the context of the European project Cuidado.
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paper_title: Signal + Context = Better Classification

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Typical signal-based approaches to extract musical descriptions from audio only have limited precision. A possible explanation is that they do not exploit context, which provides important cues in human cognitive processing of music: e.g. electric guitar is unlikely in 1930s music, children choirs rarely perform heavy metal, etc. We propose an architecture to train a large set of binary classifiers simultaneously, for many different musical metadata (genre, instrument, mood, etc.), in such a way that correlation between metadata is used to reinforce each individual classifier. The system is iterative: it uses classification decisions it made on some classification problems as new features for new, harder problems; and hybrid: it uses a signal classifier based on timbre similarity to bootstrap symbolic inference with decision trees. While further work is needed, the approach seems to outperform signal-only algorithms by 5% precision on average, and sometimes up to 15% for traditionally difficult problems such as cultural and subjective categories.
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paper_title: Web Services for Music Information Retrieval

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In the emerging world of networked and distributed digital libraries, the Web services framework will be a key to facilitating simple inter-application communication between them. Yet, despite the popularity of Web services in the business sector and their seemingly obvious applicability to the digital library domain, and to MIR in particular, the adoption of these new protocols has not been widespread. To demonstrate the tremendous potential of Web services for MIR, this paper presents an application using the Google and Amazon.com databases to generate clusters of related musical artists based on cultural metadata. The use of cultural metadata to determine artist relatedness is valuable and interesting because it captures emergent popular opinion about music. Starting from an initial seed artist, Amazon Listmania! lists are traversed to find potentially related artists. Google is used to determine which of these candidates are in fact related by assessing the co-occurrence of the two artists’ names on Internet web pages. A list of artists related to the seed is returned once a given number of artists is found. The positive results generated by the system illustrate the use of Web services for exploiting the vast amount of untapped data that are available today and highlight their importance for the future, when even more musical data will become available.
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paper_title: Context-aware mobile music recommendation for daily activities

paper_content:

Existing music recommendation systems rely on collaborative filtering or content-based technologies to satisfy users' long-term music playing needs. Given the popularity of mobile music devices with rich sensing and wireless communication capabilities, we present in this paper a novel approach to employ contextual information collected with mobile devices for satisfying users' short-term music playing needs. We present a probabilistic model to integrate contextual information with music content analysis to offer music recommendation for daily activities, and we present a prototype implementation of the model. Finally, we present evaluation results demonstrating good accuracy and usability of the model and prototype.
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paper_title: Web-Scale Multimedia Analysis: Does Content Matter?

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The initial success of Web-image search was based exclusively on the text around an image. Certainly we have progressed since then. But recent research results dramatically beg to differ. For example, if you want to judge the similarity of two different pieces of music, should you look at the musical notes, or should you look at what people say about the music? Similarly, how should you find the best movie to recommend to a friend? Shouldn't the genre of the movie matter? Or when tagging a photo, is it better to look at the pixels, or where the picture lives on the Internet? I want to think that content matters, but in all three cases, metadata about the content proves to be more useful. It's useful to look at several examples where content has lost out to other forms of data. These examples come from the worlds of music, movies, and images.
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paper_title: Combining audio-based similarity with web-based data to accelerate automatic music playlist generation

paper_content:

We present a technique for combining audio signal-based music similarity with web-based musical artist similarity to accelerate the task of automatic playlist generation. We demonstrate the applicability of our proposed method by extending a recently published interface for music players that benefits from intelligent structuring of audio collections. While the original approach involves the calculation of similarities between every pair of songs in a collection,we incorporate web-based data to reduce the number of necessary similarity calculations. More precisely,we exploit artist similarity determined automatically by means of web retrieval to avoid similarity calculation between tracks of dissimilar and/or unrelated artists. We evaluate our acceleration technique on two audio collections with different characteristics. It turns out that the proposed combination of audio-and text-based similarity not only reduces the number of necessary calculations considerably but also yields better results, in terms of musical quality, than the initial approach based on audio data only. Additionally, we conducted a small user study that further confirms the quality of the resulting playlists.
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paper_title: Combining audio content and social context for semantic music discovery

paper_content:

When attempting to annotate music, it is important to consider both acoustic content and social context. This paper explores techniques for collecting and combining multiple sources of such information for the purpose of building a query-by-text music retrieval system. We consider two representations of the acoustic content (related to timbre and harmony) and two social sources (social tags and web documents). We then compare three algorithms that combine these information sources: calibrated score averaging (CSA), RankBoost, and kernel combination support vector machines (KC-SVM). We demonstrate empirically that each of these algorithms is superior to algorithms that use individual information sources.
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paper_title: Automatic Generation of Social Tags for Music Recommendation

paper_content:

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of "Web2.0" recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of boosted classifiers, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the "cold-start problem" common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system.
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paper_title: Comprehensive query-dependent fusion using regression-on-folksonomies: a case study of multimodal music search

paper_content:

The combination of heterogeneous knowledge sources has been widely regarded as an effective approach to boost retrieval accuracy in many information retrieval domains. While various technologies have been recently developed for information retrieval, multimodal music search has not kept pace with the enormous growth of data on the Internet. In this paper, we study the problem of integrating multiple online information sources to conduct effective query dependent fusion (QDF) of multiple search experts for music retrieval. We have developed a novel framework to construct a knowledge space of users' information need from online folksonomy data. With this innovation, a large number of comprehensive queries can be automatically constructed to train a better generalized QDF system against unseen user queries. In addition, our framework models QDF problem by regression of the optimal combination strategy on a query. Distinguished from the previous approaches, the regression model of QDF (RQDF) offers superior modeling capability with less constraints and more efficient computation. To validate our approach, a large scale test collection has been collected from different online sources, such as Last.fm, Wikipedia, and YouTube. All test data will be released to the public for better research synergy in multimodal music search. Our performance study indicates that the accuracy, efficiency, and robustness of the multimodal music search can be improved significantly by the proposed folksonomy-RQDF approach. In addition, since no human involvement is required to collect training examples, our approach offers great feasibility and practicality in system development.
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paper_title: Annotating Music Collections: How Content-Based Similarity Helps to Propagate Labels

paper_content:

In this paper we present a way to annotate music collections by exploiting audio similarity. Similarity is used to propose labels (tags) to yet unlabeled songs, based on the content‐based distance between them. The main goal of our work is to ease the process of annotating huge music collections, by using content-based similarity distances as a way to propagate labels among songs. We present two different experiments. The first one propagateslabelsthatarerelatedwiththestyleofthepiece, whereas the second experiment deals with mood labels. On the one hand, our approach shows that using a music collection annotated at 40% with styles, the collection can be automatically annotated up to 78% (that is, 40% already annotated and the rest, 38%, only using propagation), with a recall greater than 0.4. On the other hand, for a smaller music collection annotated at 30% with moods, the collection can be automatically annotated up to 65% (e.g. 30% plus 35% using propagation).
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paper_title: Five Approaches to Collecting Tags for Music

paper_content:

We compare five approaches to collecting tags for music: conducting a survey, harvesting social tags, deploying annotation games, mining web documents, and autotagging audio content. The comparison includes a discussion of both scalability (financial cost, human involvement, and computational resources) and quality (the cold start problem & popularity bias, strong vs. weak labeling, vocabulary structure & size, and annotation accuracy). We then describe one state-ofthe-art system for each approach. The performance of each system is evaluated using a tag-based music information retrieval task. Using this task, we are able to quantify the effect of popularity bias on each approach by making use of a subset of more popular (short-head) songs and a set of less popular (long-tail) songs. Lastly, we propose a simple hybrid context-content system that combines our individual approaches and produces superior retrieval results.
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paper_title: Exploring the Similarity Space

paper_content:

Ranked queries are used to locate relevant documents in text databases. In a ranked query a list of terms is specified, then the documents that most closely match the query are returned---in decreasing order of similarity---as answers. Crucial to the efficacy of ranked querying is the use of a similarity heuristic, a mechanism that assigns a numeric score indicating how closely a document and the query match. In this note we explore and categorise a range of similarity heuristics described in the literature. We have implemented all of these measures in a structured way, and have carried out retrieval experiments with a substantial subset of these measures.Our purpose with this work is threefold: first, in enumerating the various measures in an orthogonal framework we make it straightforward for other researchers to describe and discuss similarity measures; second, by experimenting with a wide range of the measures, we hope to observe which features yield good retrieval behaviour in a variety of retrieval environments; and third, by describing our results so far, to gather feedback on the issues we have uncovered. We demonstrate that it is surprisingly difficult to identify which techniques work best, and comment on the experimental methodology required to support any claims as to the superiority of one method over another.
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paper_title: Indexing by Latent Semantic Analysis

paper_content:

A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.
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paper_title: A Simple Rule-Based Part Of Speech Tagger

paper_content:

Automatic part of speech tagging is an area of natural language processing where statistical techniques have been more successful than rule-based methods. In this paper, we present a simple rule-based part of speech tagger which automatically acquires its rules and tags with accuracy comparable to stochastic taggers. The rule-based tagger has many advantages over these taggers, including: a vast reduction in stored information required, the perspicuity of a small set of meaningful rules, ease of finding and implementing improvements to the tagger, and better portability from one tag set, corpus genre or language to another. Perhaps the biggest contribution of this work is in demonstrating that the stochastic method is not the only viable method for part of speech tagging. The fact that a simple rule-based tagger that automatically learns its rules can perform so well should offer encouragement for researchers to further explore rule-based tagging, searching for a better and more expressive set of rule templates and other variations on the simple but effective theme described below.
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paper_title: Statistical Techniques for Natural Language Parsing

paper_content:

I review current statistical work on syntactic parsing and then consider part-of-speech tagging, which was the first syntactic problem to successfully be attacked by statistical techniques and also serves as a good warm-up for the main topic-statistical parsing. Here, I consider both the simplified case in which the input string is viewed as a string of parts of speech and the more interesting case in which the parser is guided by statistical information about the particular words in the sentence. Finally, I anticipate future research directions.
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paper_title: Using cultural metadata for artist recommendations

paper_content:

Our approach to generate recommendations for similar artists follows a recent tradition of authors tackling the problem not with content-based audio analysis. Following this novel procedure we rely on the acquisition, filtering and condensing of unstructured text-based information that can be found in the Web. The beauty of this approach lies in the possibility to access so-called cultural metadata that is the agglomeration of several independent -originally subjective - perspectives about music.
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paper_title: Hierarchical organization and description of music collections at the artist level

paper_content:

As digital music collections grow, so does the need to organizing them automatically. In this paper we present an approach to hierarchically organize music collections at the artist level. Artists are grouped according to similarity which is computed using a web search engine and standard text retrieval techniques. The groups are described by words found on the webpages using term selection techniques and domain knowledge. We compare different term selection techniques, present a simple demonstration, and discuss our findings.
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paper_title: Mining Music Reviews: Promising Preliminary Results

paper_content:

In this paper we present a system for the automatic mining of information from music reviews. We demonstrate a system which has the ability to automatically classify reviews according to the genre of the music reviewed and to predict the simple one-to-five star rating assigned to the music by the reviewer. This experiment is the first step in the development of a system to automatically mine arbitrary bodies of text, such as weblogs (blogs) for musically relevant information.
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paper_title: A Simple Rule-Based Part Of Speech Tagger

paper_content:

Automatic part of speech tagging is an area of natural language processing where statistical techniques have been more successful than rule-based methods. In this paper, we present a simple rule-based part of speech tagger which automatically acquires its rules and tags with accuracy comparable to stochastic taggers. The rule-based tagger has many advantages over these taggers, including: a vast reduction in stored information required, the perspicuity of a small set of meaningful rules, ease of finding and implementing improvements to the tagger, and better portability from one tag set, corpus genre or language to another. Perhaps the biggest contribution of this work is in demonstrating that the stochastic method is not the only viable method for part of speech tagging. The fact that a simple rule-based tagger that automatically learns its rules can perform so well should offer encouragement for researchers to further explore rule-based tagging, searching for a better and more expressive set of rule templates and other variations on the simple but effective theme described below.
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paper_title: Artist Classification with Web-Based Data

paper_content:

Manifold approaches exist for organization of music by genre and/or style. In this paper we propose the use of text categorization techniques to classify artists present on the Internet. In particular, we retrieve and analyze webpages ranked by search engines to describe artists in terms of word occurrences on related pages. To classify artists we primarily use support vector machines. We present 3 experiments in which we address the following issues. First, we study the performance of our approach compared to previous work. Second, we investigate how daily fluctuations in the Internet affect our approach. Third, on a set of 224 artists from 14 genres we study (a) how many artists are necessary to define the concept of a genre, (b) which search engines perform best, (c) how to formulate search queries best, (d) which overall performance we can expect for classification, and finally (e) how our approach is suited as a similarity measure for artists.
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paper_title: A Semantic Space for Music Derived from Social Tags

paper_content:

In this paper we investigate social tags as a novel highvolume source of semantic metadata for music, using techniques from the fields of information retrieval and multivariate data analysis. We show that, despite the ad hoc and informal language of tagging, tags define a low-dimensional semantic space that is extremely well-behaved at the track level, in particular being highly organised by artist and musical genre. We introduce the use of Correspondence Analysis to visualise this semantic space, and show how it can be applied to create a browse-by-mood interface for a psychologically-motivated two-dimensional subspace representing musical emotion.
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paper_title: Indexing by Latent Semantic Analysis

paper_content:

A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.
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paper_title: The Quest for Ground Truth in Musical Artist Tagging in the Social Web Era

paper_content:

Research in Web music information retrieval traditionally focuses on the classication, clustering or categorizing of music into genres or other subdivisions. However, current community-based web sites provide richer descriptors (i.e. tags) for all kinds of products. Although tags have no well-dened semantics, they have proven to be an effective mechanism to label and retrieve items. Moreover, these tags are community-based and hence give a description of a product through the eyes of a community rather than an expert opinion. In this work we focus on Last.fm, which is currently the largest music community web service. We investigate whether the tagging of artists is consistent with the artist similarities found with collaborative ltering techniques. As the Last.fm data shows to be both consistent and descriptive, we propose a method to use this community-based data to create a ground truth for artist tagging and artist similarity.
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paper_title: Artist Classification with Web-Based Data

paper_content:

Manifold approaches exist for organization of music by genre and/or style. In this paper we propose the use of text categorization techniques to classify artists present on the Internet. In particular, we retrieve and analyze webpages ranked by search engines to describe artists in terms of word occurrences on related pages. To classify artists we primarily use support vector machines. We present 3 experiments in which we address the following issues. First, we study the performance of our approach compared to previous work. Second, we investigate how daily fluctuations in the Internet affect our approach. Third, on a set of 224 artists from 14 genres we study (a) how many artists are necessary to define the concept of a genre, (b) which search engines perform best, (c) how to formulate search queries best, (d) which overall performance we can expect for classification, and finally (e) how our approach is suited as a similarity measure for artists.
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paper_title: A Semantic Space for Music Derived from Social Tags

paper_content:

In this paper we investigate social tags as a novel highvolume source of semantic metadata for music, using techniques from the fields of information retrieval and multivariate data analysis. We show that, despite the ad hoc and informal language of tagging, tags define a low-dimensional semantic space that is extremely well-behaved at the track level, in particular being highly organised by artist and musical genre. We introduce the use of Correspondence Analysis to visualise this semantic space, and show how it can be applied to create a browse-by-mood interface for a psychologically-motivated two-dimensional subspace representing musical emotion.
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paper_title: A Web-Based Game for Collecting Music Metadata

paper_content:

Abstract We have designed a web-based game, MajorMiner, that makes collecting descriptions of musical excerpts fun, easy, useful, and objective. Participants describe 10 second clips of songs and score points when their descriptions match those of other participants. The rules were designed to encourage players to be thorough and the clip length was chosen to make judgments objective and specific. To analyse the data, we measured the degree to which binary classifiers could be trained to spot popular tags. We also compared the performance of clip classifiers trained with MajorMiner's tag data to those trained with social tag data from a popular website. On the top 25 tags from each source, MajorMiner's tags were classified correctly 67.2% of the time, while the social tags were classified correctly 62.6% of the time.
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paper_title: A Game-Based Approach for Collecting Semantic Annotations of Music

paper_content:

Games based on human computation are a valuable tool for collecting semantic information about images. We show how to transfer this idea into the music domain in order to collect high-quality semantic information about songs. We present Listen Game, a online, multiplayer game that measures the semantic relationship between music and words. In the normal mode, a player sees a list of semantically related words (e.g., instruments, emotions, usages, genres) and is asked to pick the best and worst word to describe a song. In the freestyle mode, a user is asked to suggest a new word that describes the music. Each player receives realtime feedback about the agreement amongst all players. We show that we can use the data collected during a two-week pilot study of Listen Game to learn a supervised multiclass labeling (SML) model. We show that this SML model can annotate a novel song with meaningful words and retrieve relevant songs from a database of audio content.
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paper_title: Effective music tagging through advanced statistical modeling

paper_content:

Music information retrieval (MIR) holds great promise as a technology for managing large music archives. One of the key components of MIR that has been actively researched into is music tagging. While significant progress has been achieved, most of the existing systems still adopt a simple classification approach, and apply machine learning classifiers directly on low level acoustic features. Consequently, they suffer the shortcomings of (1) poor accuracy, (2) lack of comprehensive evaluation results and the associated analysis based on large scale datasets, and (3) incomplete content representation, arising from the lack of multimodal and temporal information integration. In this paper, we introduce a novel system called MMTagger that effectively integrates both multimodal and temporal information in the representation of music signal. The carefully designed multilayer architecture of the proposed classification framework seamlessly combines Multiple Gaussian Mixture Models (GMMs) and Support Vector Machine (SVM) into a single framework. The structure preserves more discriminative information, leading to more accurate and robust tagging. Experiment results obtained with two large music collections highlight the various advantages of our multilayer framework over state of the art techniques.
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paper_title: Using Artist Similarity to Propagate Semantic Information

paper_content:

Tags are useful text-based labels that encode semantic information about music (instrumentation, genres, emotions, geographic origins). While there are a number of ways to collect and generate tags, there is generally a data sparsity problem in which very few songs and artists have been accurately annotated with a sufficiently large set of relevant tags. We explore the idea of tag propagation to help alleviate the data sparsity problem. Tag propagation, originally proposed by Sordo et al., involves annotating a novel artist with tags that have been frequently associated with other similar artists. In this paper, we explore four approaches for computing artists similarity based on different sources of music information (user preference data, social tags, web documents, and audio content). We compare these approaches in terms of their ability to accurately propagate three different types of tags (genres, acoustic descriptors, social tags). We find that the approach based on collaborative filtering performs best. This is somewhat surprising considering that it is the only approach that is not explicitly based on notions of semantic similarity. We also find that tag propagation based on content-based music analysis results in relatively poor performance.
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paper_title: Large-scale music tag recommendation with explicit multiple attributes

paper_content:

Social tagging can provide rich semantic information for large-scale retrieval in music discovery. Such collaborative intelligence, however, also generates a high degree of tags unhelpful to discovery, some of which obfuscate critical information. Towards addressing these shortcomings, tag recommendation for more robust music discovery is an emerging topic of significance for researchers. However, current methods do not consider diversity of music attributes, often using simple heuristics such as tag frequency for filtering out irrelevant tags. Music attributes encompass any number of perceived dimensions, for instance vocalness, genre, and instrumentation. Many of these are underrepresented by current tag recommenders. We propose a scheme for tag recommendation using Explicit Multiple Attributes based on tag semantic similarity and music content. In our approach, the attribute space is explicitly constrained at the outset to a set that minimizes semantic loss and tag noise, while ensuring attribute diversity. Once the user uploads or browses a song, the system recommends a list of relevant tags in each attribute independently. To the best of our knowledge, this is the first method to consider Explicit Multiple Attributes for tag recommendation. Our system is designed for large-scale deployment, on the order of millions of objects. For processing large-scale music data sets, we design parallel algorithms based on the MapReduce framework to perform large-scale music content and social tag analysis, train a model, and compute tag similarity. We evaluate our tag recommendation system on CAL-500 and a large-scale data set ($N = 77,448$ songs) generated by crawling Youtube and Last.fm. Our results indicate that our proposed method is both effective for recommending attribute-diverse relevant tags and efficient at scalable processing.
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paper_title: Automatic Generation of Social Tags for Music Recommendation

paper_content:

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of "Web2.0" recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of boosted classifiers, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the "cold-start problem" common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system.
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paper_title: Annotating Music Collections: How Content-Based Similarity Helps to Propagate Labels

paper_content:

In this paper we present a way to annotate music collections by exploiting audio similarity. Similarity is used to propose labels (tags) to yet unlabeled songs, based on the content‐based distance between them. The main goal of our work is to ease the process of annotating huge music collections, by using content-based similarity distances as a way to propagate labels among songs. We present two different experiments. The first one propagateslabelsthatarerelatedwiththestyleofthepiece, whereas the second experiment deals with mood labels. On the one hand, our approach shows that using a music collection annotated at 40% with styles, the collection can be automatically annotated up to 78% (that is, 40% already annotated and the rest, 38%, only using propagation), with a recall greater than 0.4. On the other hand, for a smaller music collection annotated at 30% with moods, the collection can be automatically annotated up to 65% (e.g. 30% plus 35% using propagation).
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paper_title: OH OH OH WHOAH! TOWARDS AUTOMATIC TOPIC DETECTION IN SONG LYRICS

paper_content:

We present an algorithm that allows for indexing music by topic. The application scenario is an information retrieval system into which any song with known lyrics can be inserted and indexed so as to make a music collection browseable by topic. We use text mining techniques for creating a vector space model of our lyrics collection and non-negative matrix factorization (NMF) to identify topic clusters which are then labeled manually. We include a discussion of the decisions regarding the parametrization of the applied methods. The suitability of our approach is assessed by measuring the agreement of test subjects who provide the labels for the topic clusters.
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paper_title: Semantic analysis of song lyrics

paper_content:

We explore the use of song lyrics for automatic indexing of music. Using lyrics mined from the Web, we apply a standard text processing technique to characterize their semantic content. We then determine artist similarity in this space. We found lyrics can be used to discover natural genre clusters. Experiments on a publicly available set of 399 artists showed that determining artist similarity using lyrics is better than random, but inferior to a state-of-the-art acoustic similarity technique. However the approaches made different errors, suggesting they could be profitably combined
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paper_title: Rauber: Rhyme and Style Features for Musical Genre Classification by Song Lyrics

paper_content:

How individuals perceive music is influenced by many different factors. The audible part of a piece of music, its sound, does for sure contribute, but is only one aspect to be taken into account. Cultural information influences how we experience music, as does the songs’ text and its sound. Next to symbolic and audio based music information retrieval, which focus on the sound of music, song lyrics, may thus be used to improve classification or similarity ranking of music. Song lyrics exhibit specific properties different from traditional text documents – many lyrics are for example composed in rhyming verses, and may have different frequencies for certain parts-of-speech when compared to other text documents. Further, lyrics may use ‘slang’ language or differ greatly in the length and complexity of the language used, which can be measured by some statistical features such as word / verse length, and the amount of repetative text. In this paper, we present a novel set of features developed for textual analysis of song lyrics, and combine them with and compare them to classical bag-of-words indexing approaches. We present results for musical genre classification on a test collection in order to demonstrate our analysis.
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paper_title: Natural language processing of lyrics

paper_content:

We report experiments on the use of standard natural language processing (NLP) tools for the analysis of music lyrics. A significant amount of music audio has lyrics. Lyrics encode an important part of the semantics of a song, therefore their analysis complements that of acoustic and cultural metadata and is fundamental for the development of complete music information retrieval systems. Moreover, a textual analysis of a song can generate ground truth data that can be used to validate results from purely acoustic methods. Preliminary results on language identification, structure extraction, categorization and similarity searches suggests that a lot of profit can be gained from the analysis of lyrics.
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paper_title: Lyric Text Mining in Music Mood Classification

paper_content:

This research examines the role lyric text can play in improving audio music mood classification. A new method is proposed to build a large ground truth set of 5,585 songs and 18 mood categories based on social tags so as to reflect a realistic, user-centered perspective. A relatively complete set of lyric features and representation models were investigated. The best performing lyric feature set was also compared to a leading audio-based system. In combining lyric and audio sources, hybrid feature sets built with three different feature selection methods were also examined. The results show patterns at odds with findings in previous studies: audio features do not always outperform lyrics features, and combining lyrics and audio features can improve performance in many mood categories, but not all of them.
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paper_title: Multimodal Music Mood Classification Using Audio and Lyrics

paper_content:

In this paper we present a study on music mood classification using audio and lyrics information. The mood of a song is expressed by means of musical features but a relevant part also seems to be conveyed by the lyrics. We evaluate each factor independently and explore the possibility to combine both, using natural language processing and music information retrieval techniques. We show that standard distance-based methods and latent semantic analysis are able to classify the lyrics significantly better than random, but the performance is still quite inferior to that of audio-based techniques. We then introduce a method based on differences between language models that gives performances closer to audio-based classifiers. Moreover, integrating this in a multimodal system (audio+text) allows an improvement in the overall performance. We demonstrate that lyrics and audio information are complementary, and can be combined to improve a classification system.
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paper_title: Probabilistic Latent Semantic Analysis

paper_content:

Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.
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paper_title: Web Services for Music Information Retrieval

paper_content:

In the emerging world of networked and distributed digital libraries, the Web services framework will be a key to facilitating simple inter-application communication between them. Yet, despite the popularity of Web services in the business sector and their seemingly obvious applicability to the digital library domain, and to MIR in particular, the adoption of these new protocols has not been widespread. To demonstrate the tremendous potential of Web services for MIR, this paper presents an application using the Google and Amazon.com databases to generate clusters of related musical artists based on cultural metadata. The use of cultural metadata to determine artist relatedness is valuable and interesting because it captures emergent popular opinion about music. Starting from an initial seed artist, Amazon Listmania! lists are traversed to find potentially related artists. Google is used to determine which of these candidates are in fact related by assessing the co-occurrence of the two artists’ names on Internet web pages. A list of artists related to the seed is returned once a given number of artists is found. The positive results generated by the system illustrate the use of Web services for exploiting the vast amount of untapped data that are available today and highlight their importance for the future, when even more musical data will become available.
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paper_title: A Web-Based Approach to Assessing Artist Similarity using CoOccurrences

paper_content:

In this paper, we present a similarity measure for music artists based on search results of Google queries. Co-occurrences of artist names on web pages are analyzed to measure how often two artists are mentioned together on the same web page. We estimate conditional probabilities using the extracted page count. These conditional probabilities give a similarity measure which is evaluated using a data set containing 224 artists from 14 genres. For evaluation, we use two different methods, intra-/intergroup-similarities and k-Nearest Neighbors classification. Furthermore, a confidence filter and combinations of the results gained from three different query settings are tested. It is shown that these enhancements can raise the performance of our similarity measure. Comparing our results to those of similar approaches show that our approach, though being quite simple, performs well and can be used as a similarity measure that incorporates “social knowledge”.
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paper_title: Web-collaborative filtering: recommending music by crawling the Web

paper_content:

Abstract We show that it is possible to collect data that are useful for collaborative filtering (CF) using an autonomous Web spider. In CF, entities are recommended to a new user based on the stated preferences of other, similar users. We describe a CF spider that collects from the Web lists of semantically related entities. These lists can then be used by existing CF algorithms by encoding them as `pseudo-users'. Importantly, the spider can collect useful data without pre-programmed knowledge about the format of particular pages or particular sites. Instead, the CF spider uses commercial Web-search engines to find pages likely to contain lists in the domain of interest, and then applies previously proposed heuristics to extract lists from these pages. We show that data collected by this spider are nearly as effective for CF as data collected from real users, and more effective than data collected by two plausible hand-programmed spiders. In some cases, autonomously spidered data can also be combined with actual user data to improve performance.
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paper_title: Artist Classification with Web-Based Data

paper_content:

Manifold approaches exist for organization of music by genre and/or style. In this paper we propose the use of text categorization techniques to classify artists present on the Internet. In particular, we retrieve and analyze webpages ranked by search engines to describe artists in terms of word occurrences on related pages. To classify artists we primarily use support vector machines. We present 3 experiments in which we address the following issues. First, we study the performance of our approach compared to previous work. Second, we investigate how daily fluctuations in the Internet affect our approach. Third, on a set of 224 artists from 14 genres we study (a) how many artists are necessary to define the concept of a genre, (b) which search engines perform best, (c) how to formulate search queries best, (d) which overall performance we can expect for classification, and finally (e) how our approach is suited as a similarity measure for artists.
---
paper_title: Web Services for Music Information Retrieval

paper_content:

In the emerging world of networked and distributed digital libraries, the Web services framework will be a key to facilitating simple inter-application communication between them. Yet, despite the popularity of Web services in the business sector and their seemingly obvious applicability to the digital library domain, and to MIR in particular, the adoption of these new protocols has not been widespread. To demonstrate the tremendous potential of Web services for MIR, this paper presents an application using the Google and Amazon.com databases to generate clusters of related musical artists based on cultural metadata. The use of cultural metadata to determine artist relatedness is valuable and interesting because it captures emergent popular opinion about music. Starting from an initial seed artist, Amazon Listmania! lists are traversed to find potentially related artists. Google is used to determine which of these candidates are in fact related by assessing the co-occurrence of the two artists’ names on Internet web pages. A list of artists related to the seed is returned once a given number of artists is found. The positive results generated by the system illustrate the use of Web services for exploiting the vast amount of untapped data that are available today and highlight their importance for the future, when even more musical data will become available.
---
paper_title: A Web-Based Approach to Assessing Artist Similarity using CoOccurrences

paper_content:

In this paper, we present a similarity measure for music artists based on search results of Google queries. Co-occurrences of artist names on web pages are analyzed to measure how often two artists are mentioned together on the same web page. We estimate conditional probabilities using the extracted page count. These conditional probabilities give a similarity measure which is evaluated using a data set containing 224 artists from 14 genres. For evaluation, we use two different methods, intra-/intergroup-similarities and k-Nearest Neighbors classification. Furthermore, a confidence filter and combinations of the results gained from three different query settings are tested. It is shown that these enhancements can raise the performance of our similarity measure. Comparing our results to those of similar approaches show that our approach, though being quite simple, performs well and can be used as a similarity measure that incorporates “social knowledge”.
---
paper_title: Web-collaborative filtering: recommending music by crawling the Web

paper_content:

Abstract We show that it is possible to collect data that are useful for collaborative filtering (CF) using an autonomous Web spider. In CF, entities are recommended to a new user based on the stated preferences of other, similar users. We describe a CF spider that collects from the Web lists of semantically related entities. These lists can then be used by existing CF algorithms by encoding them as `pseudo-users'. Importantly, the spider can collect useful data without pre-programmed knowledge about the format of particular pages or particular sites. Instead, the CF spider uses commercial Web-search engines to find pages likely to contain lists in the domain of interest, and then applies previously proposed heuristics to extract lists from these pages. We show that data collected by this spider are nearly as effective for CF as data collected from real users, and more effective than data collected by two plausible hand-programmed spiders. In some cases, autonomously spidered data can also be combined with actual user data to improve performance.
---
paper_title: Exploiting Twitter's Collective Knowledge for Music Recommendations

paper_content:

Twitter is the largest source of public opinion and also contains a vast amount of information about its users’ music favors or listening behaviour. However, this source has not been exploited for the recommendation of music yet. In this paper, we present how Twitter can be facilitated for the creation of a data set upon which music recommendations can be computed. The data set is based on microposts which were automatically generated by music player software or posted by users and may also contain further information about audio tracks.
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paper_title: Playlist prediction via metric embedding

paper_content:

Digital storage of personal music collections and cloud-based music services (e.g. Pandora, Spotify) have fundamentally changed how music is consumed. In particular, automatically generated playlists have become an important mode of accessing large music collections. The key goal of automated playlist generation is to provide the user with a coherent listening experience. In this paper, we present Latent Markov Embedding (LME), a machine learning algorithm for generating such playlists. In analogy to matrix factorization methods for collaborative filtering, the algorithm does not require songs to be described by features a priori, but it learns a representation from example playlists. We formulate this problem as a regularized maximum-likelihood embedding of Markov chains in Euclidian space, and show how the resulting optimization problem can be solved efficiently. An empirical evaluation shows that the LME is substantially more accurate than adaptations of smoothed n-gram models commonly used in natural language processing.
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paper_title: Build your own music recommender by modeling internet radio streams

paper_content:

In the Internet music scene, where recommendation technology is key for navigating huge collections, large market players enjoy a considerable advantage. Accessing a wider pool of user feedback leads to an increasingly more accurate analysis of user tastes, effectively creating a "rich get richer" effect. This work aims at significantly lowering the entry barrier for creating music recommenders, through a paradigm coupling a public data source and a new collaborative filtering (CF) model. We claim that Internet radio stations form a readily available resource of abundant fresh human signals on music through their playlists, which are essentially cohesive sets of related tracks. In a way, our models rely on the knowledge of a diverse group of experts in lieu of the commonly used wisdom of crowds. Over several weeks, we aggregated publicly available playlists of thousands of Internet radio stations, resulting in a dataset encompassing millions of plays, and hundreds of thousands of tracks and artists. This provides the large scale ground data necessary to mitigate the cold start problem of new items at both mature and emerging services. Furthermore, we developed a new probabilistic CF model, tailored to the Internet radio resource. The success of the model was empirically validated on the collected dataset. Moreover, we tested the model at a cross-source transfer learning manner -- the same model trained on the Internet radio data was used to predict behavior of Yahoo! Music users. This demonstrates the ability to tap the Internet radio signals in other music recommendation setups. Based on encouraging empirical results, our hope is that the proposed paradigm will make quality music recommendation accessible to all interested parties in the community.
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paper_title: Musical data mining for electronic music distribution

paper_content:

Music classification is a key ingredient for electronic music distribution. Because of the lack of standards in music classification (or the lack of enforcement of existing standards), there is a huge amount of unclassified music titles in the world. The authors propose a classification method based on a musical data mining technique based on co-occurrence and correlation analysis that can be used for classification. It gives a new approach to similarity between several titles of music or several artists. We study large corpora of textual information referring titles of music or artists whose names are decided by humans without particular constraints other than readability, and draw various hypotheses concerning the natural similarities that emerge from these corpora. Based on a clustering technique, we show that interesting groups can reveal specific music genres and allow classification of music titles in an objective manner.
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paper_title: Uncovering Affinity of Artists to Multiple Genres from Social Behaviour Data

paper_content:

In organisation schemes, musical artists are commonly identified with a unique ‘genre’ label attached, even when they have affinity to multiple genres. To uncover this hidden cultural awareness about multi-genre affinity, we present a new model based on the analysis of the way in which a community of users organise artists and genres in playlists. Our work is based on a novel dataset that we have elaborated identifying the co-occurrences of artists in the playlists shared by the members of a popular Web-based community, and that is made publicly available. The analysis defines an automatic social-based method to uncover relationships between artists and genres, and introduces a series of novel concepts that characterises artists and genres in a richer way than a unique ‘genre’ label would do.
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paper_title: The Quest for Ground Truth in Musical Artist Similarity

paper_content:

would be interesting and valuable to devise an automatic measure of the similarity between two musicians based only on an analysis of their recordings. To develop such a measure, however, presupposes some 'ground truth' training data describing the actual similarity between certain pairs of artists that constitute the desired output of the measure. Since artist similarity is wholly subjective, such data is not easily obtained. In this paper, we describe several attempts to construct a full matrix of similarity measures between a set of some 400 popular artists by regularizing limited subjective judgment data. We also detail our attempts to evaluate these measures by comparison with direct subjective similarity judgments collected via a web- based survey in April 2002. Overall, we find that subjective artist similarities are quite variable between users—casting doubt on the concept of a single 'ground truth'. Our best measure, however, gives reasonable agreement with the subjective data, and forms a useable stand-in. In addition, our evaluation methodology may be useful for comparing other measures of artist similarity.
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paper_title: Some methods for classification and analysis of multivariate observations

paper_content:

The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special
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paper_title: A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures

paper_content:

music similarity, acoustic measures, evaluation, ground-truth Subjective similarity between musical pieces and artists is an elusive concept, but one that must be pursued in support of applications to provide automatic organization of large music collections. In this paper, we examine both acoustic and subjective approaches for calculating similarity between artists, comparing their performance on a common database of 400 popular artists. Specifically, we evaluate acoustic techniques based on Mel-frequency cepstral coefficients and an intermediate ‘anchor space’ of genre classification, and subjective techniques which use data from The All Music Guide, from a survey, from playlists and personal collections, and from web-text mining.
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paper_title: "The way it Sounds": timbre models for analysis and retrieval of music signals

paper_content:

Electronic Music Distribution is in need of robust and automatically extracted music descriptors. An important attribute of a piece of polyphonic music is what is commonly referred to as "the way it sounds". While there has been a large quantity of research done to model the timbre of individual instruments, little work has been done to analyze "real world" timbre mixtures such as the ones found in popular music. In this paper, we present our research about such "polyphonic timbres". We describe an effective way to model the textures found in a given music signal, and show that such timbre models provide new solutions to many issues traditionally encountered in music signal processing and music information retrieval. Notably, we describe their applications for music similarity, segmentation and pattern induction.
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paper_title: Toward Evaluation Techniques for Music Similarity

paper_content:

We describe and discuss our recent work developing a database, methodology and ground truth for the evaluation of automatic techniques for music similarity. Our database consists of acoustic and textual ‘Web-mined’ data covering 400 popular artists. Of note is our technique of sharing acoustic features rather than raw audio to avoid copyright problems. Our evaluation methodology allows any data source to be regarded as ground truth and can highlight which measure forms the best collective ground truth. We additionally describe an evaluation methodology that is useful for similarity. We have successfully used our database and techniques to evaluate a number of music similarity algorithms. *
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paper_title: Lessons from the Netflix prize challenge

paper_content:

This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress prize.
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paper_title: Local implicit feedback mining for music recommendation

paper_content:

Digital music has experienced a quite fascinating transformation during the past decades. Thousands of people share or distribute their music collections on the Internet, resulting in an explosive increase of information and more user dependence on automatic recommender systems. Though there are many techniques such as collaborative filtering, most approaches focus mainly on users' global behaviors, neglecting local actions and the specific properties of music. In this paper, we propose a simple and effective local implicit feedback model mining users' local preferences to get better recommendation performance in both rating and ranking prediction. Moreover, we design an efficient training algorithm to speed up the updating procedure, and give a method to find the most appropriate time granularity to assist the performance. We conduct various experiments to evaluate the performance of this model, which show that it outperforms baseline model significantly. Integration with existing temporal models achieves a great improvement compared to the reported best single model for Yahoo! Music.
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paper_title: Music Discovery with Social Networks

paper_content:

Current music recommender systems rely on techniques like collaborative ltering on user-provided information in order to generate relevant recommendations based upon users’ music collections or listening habits. In this paper, we examine whether better recommendations can be obtained by taking into account the music preferences of the user’s social contacts. We assume that music is naturally diused through the social network of its listeners, and that we can propagate automatic recommendations in the same way through the network. In order to test this statement, we developed a music recommender application called Starnet on a Social Networking Service. It generated recommendations based either on positive ratings of friends (social recommendations), positive ratings of others in the network (nonsocial recommendations), or not based on ratings (random recommendations). The user responses to each type of recommendation indicate that social recommendations are better than non-social recommendations, which are in turn better than random recommendations. Likewise, the discovery of novel and relevant music is more likely via social recommendations than non-social. Social shue recommendations enable people to discover music through a serendipitous process powered by human relationships and tastes, exploiting the user’s social network to share cultural experiences.
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paper_title: Similarity Based on Rating Data

paper_content:

This paper describes an algorithm to measure the similarity of two multimedia objects, such as songs or movies, using users’ preferences. Much of the previous work on query-by-example (QBE) or music similarity uses detailed analysis of the object’s content. This is difficult and it is often impossible to capture how consumers react to the music. We argue that a large collection of user’s preferences is more accurate, at least in comparison to our benchmark system, at finding similar songs. We describe an algorithm based the song’s rating data, and show how this approach works by measuring its performance using an objective metric based on whether the same artist performed both songs. Our similarity results are based on 1.5 million musical judgments by 380,000 users. We test our system by generating playlists using a content-based system, our rating-based system, and a random list of songs. Music listeners greatly preferred the ratings-based playlists over the content-based and random playlists.
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paper_title: Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy

paper_content:

In the past decade large scale recommendation datasets were published and extensively studied. In this work we describe a detailed analysis of a sparse, large scale dataset, specifically designed to push the envelope of recommender system models. The Yahoo! Music dataset consists of more than a million users, 600 thousand musical items and more than 250 million ratings, collected over a decade. It is characterized by three unique features: First, rated items are multi-typed, including tracks, albums, artists and genres; Second, items are arranged within a four level taxonomy, proving itself effective in coping with a severe sparsity problem that originates from the unusually large number of items (compared to, e.g., movie ratings datasets). Finally, fine resolution timestamps associated with the ratings enable a comprehensive temporal and session analysis. We further present a matrix factorization model exploiting the special characteristics of this dataset. In particular, the model incorporates a rich bias model with terms that capture information from the taxonomy of items and different temporal dynamics of music ratings. To gain additional insights of its properties, we organized the KddCup-2011 competition about this dataset. As the competition drew thousands of participants, we expect the dataset to attract considerable research activity in the future.
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paper_title: Using Audio Analysis and Network Structure to Identify Communities in On-Line Social Networks of Artists

paper_content:

Community detection methods from complex network theory are applied to a subset of the Myspace artist network to identify groups of similar artists. Methods based on the greedy optimization of modularity and random walks are used. In a second iteration, inter-artist audio-based similarity scores are used as input to enhance these community detection methods. The resulting community structures are evaluated using a collection of artist-assigned genre tags. Evidence suggesting the Myspace artist network structure is closely related to musical genre is presented and a Semantic Web service for accessing this structure is described.
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paper_title: You Can Judge an Artist by an Album Cover: Using Images for Music Annotation

paper_content:

A computer-vision system predicts music genre tags by making use of content-based image analysis, suggesting that we can learn some notion of artists' similarity on the basis of visual appearance alone.
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paper_title: Web-collaborative filtering: recommending music by crawling the Web

paper_content:

Abstract We show that it is possible to collect data that are useful for collaborative filtering (CF) using an autonomous Web spider. In CF, entities are recommended to a new user based on the stated preferences of other, similar users. We describe a CF spider that collects from the Web lists of semantically related entities. These lists can then be used by existing CF algorithms by encoding them as `pseudo-users'. Importantly, the spider can collect useful data without pre-programmed knowledge about the format of particular pages or particular sites. Instead, the CF spider uses commercial Web-search engines to find pages likely to contain lists in the domain of interest, and then applies previously proposed heuristics to extract lists from these pages. We show that data collected by this spider are nearly as effective for CF as data collected from real users, and more effective than data collected by two plausible hand-programmed spiders. In some cases, autonomously spidered data can also be combined with actual user data to improve performance.
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paper_title: Towards Automatic Retrieval of Album Covers

paper_content:

We present first steps towards intelligent retrieval of music album covers from the web. The continuous growth of electronic music distribution constantly increases the interest in methods to automatically provide added value like lyrics or album covers. While existing approaches rely on large proprietary databases, we focus on methods that make use of the whole web by using Google's or A9.com's image search. We evaluate the current state of the approach and point out directions for further improvements.
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paper_title: Do you sound like your friends? exploring artist similarity via artist social network relationships and audio signal processing

paper_content:

A sample of the Myspace artist network is examined to investigate the relationship between social connectivity and audio-based similarity. Audio data from the Myspace artist pages is analyzed using well-established signal-based music information retrieval techniques. In addition to showing that the Myspace artist network exhibits many of the properties common to social networks, we show there is an ambiguous relationship between audio-based similarity and the social network topology.
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paper_title: Heterogeneous Embedding for Subjective Artist Similarity

paper_content:

We describe an artist recommendation system which integrates several heterogeneous data sources to form a holistic similarity space. Using social, semantic, and acoustic features, we learn a low-dimensional feature transformation which is optimized to reproduce human-derived measurements of subjective similarity between artists. By producing low-dimensional representations of artists, our system is suitable for visualization and recommendation tasks.
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paper_title: Data gathering for a culture specific approach in MIR

paper_content:

In this paper we describe the data gathering work done within a large research project, CompMusic, which emphasizes a culture specific approach in the automatic description of several world music repertoires. Currently we are focusing on the Hindustani (North India), Carnatic (South India) and Turkish-makam (Turkey) music traditions. The selection and organization of the data to be processed for the characterization of each of these traditions is of the utmost importance.
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paper_title: Exploiting Twitter's Collective Knowledge for Music Recommendations

paper_content:

Twitter is the largest source of public opinion and also contains a vast amount of information about its users’ music favors or listening behaviour. However, this source has not been exploited for the recommendation of music yet. In this paper, we present how Twitter can be facilitated for the creation of a data set upon which music recommendations can be computed. The data set is based on microposts which were automatically generated by music player software or posted by users and may also contain further information about audio tracks.
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Title: A Survey of Music Similarity and Recommendation from Music Context Data

Section 1: INTRODUCTION
Description 1: Introduce the importance of music information retrieval (MIR), the challenges faced in content-based approaches, and the emergence of context-based methods.

Section 2: GENERAL CONSIDERATIONS
Description 2: Discuss the general implications and differences between content-based and context-based similarity measures.

Section 3: TEXT-BASED APPROACHES
Description 3: Review work that uses textual representations from web pages, user tags, and song lyrics for deriving music similarity.

Section 4: CO-OCCURRENCE-BASED APPROACHES
Description 4: Examine methods that estimate similarity based on the co-occurrence of music entities in web pages, microblogs, playlists, and peer-to-peer networks.

Section 5: USER RATING-BASED APPROACHES
Description 5: Explore approaches that utilize explicit user feedback and collaborative filtering for music similarity and recommendation.

Section 6: OTHER SOURCES OF CONTEXTUAL MUSIC SIMILARITY
Description 6: Discuss alternative and implicit data sources for deriving music similarity, including social network data, images, and promotional materials.

Section 7: DISCUSSION AND OUTLOOK
Description 7: Summarize the findings, discuss the pros and cons of the reviewed approaches, and provide an outlook on future directions for research in context-based music information extraction and similarity calculation.