A survey of tagging techniques for music, speech and environmental sound
---
paper_title: Music Retrieval: A Tutorial and Review

paper_content:

The increasing availability of music in digital format needs to be matched by the development of tools for music accessing, filtering, classification, and retrieval. The research area of Music Information Retrieval (MIR) covers many of these aspects. The aim of this paper is to present an overview of this vast and new field. A number of issues, which are peculiar to the music language, are described--including forms, formats, and dimensions of music--together with the typologies of users and their information needs. To fulfil these needs a number of approaches are discussed, from direct search to information filtering and clustering of music documents. An overview of the techniques for music processing, which are commonly exploited in many approaches, is also presented. Evaluation and comparisons of the approaches on a common benchmark are other important issues. To this end, a description of the initial efforts and evaluation campaigns for MIR is provided.
---
paper_title: Fifty years of progress in speech and speaker recognition

paper_content:

Speech and speaker recognition technology has made very significant progress in the past 50 years. The progress can be summarized by the following changes: (1) from template matching to corpus‐base statistical modeling, e.g., HMM and n‐grams, (2) from filter bank/spectral resonance to Cepstral features (Cepstrum + DCepstrum + DDCepstrum), (3) from heuristic time‐normalization to DTW/DP matching, (4) from gdistanceh‐based to likelihood‐based methods, (5) from maximum likelihood to discriminative approach, e.g., MCE/GPD and MMI, (6) from isolated word to continuous speech recognition, (7) from small vocabulary to large vocabulary recognition, (8) from context‐independent units to context‐dependent units for recognition, (9) from clean speech to noisy/telephone speech recognition, (10) from single speaker to speaker‐independent/adaptive recognition, (11) from monologue to dialogue/conversation recognition, (12) from read speech to spontaneous speech recognition, (13) from recognition to understanding, (14) f...
---
paper_title: A toolbox for animal call recognition

paper_content:

Monitoring the natural environment is increasingly important as habit degradation and climate change reduce the world's biodiversity. We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales. One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal's proximity to the microphone. Second, initial experimentation suggested that no single recognizer could deal with the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and exper...
---
paper_title: An efficient code for environmental sound classification.

paper_content:

An efficient code for classifying environmental sounds is described that exploits a recent significant advance in signal processing known as compressed sensing (CS) [cf.Donoho, D. (2006). IEEE Trans. Inf. Theory 52, 1289–1306]. CS involves a novel approach to sampling in which the salient information in signals is recovered from the projection onto a small set of random basis functions. The advantage of the random basis over traditional Fourier or wavelet representations is that it allows accurate classification at low target-to-interference ratios based on few samples and little or no prior information about signals.
---
paper_title: Semantic Annotation and Retrieval of Music and Sound Effects

paper_content:

We present a computer audition system that can both annotate novel audio tracks with semantically meaningful words and retrieve relevant tracks from a database of unlabeled audio content given a text-based query. We consider the related tasks of content-based audio annotation and retrieval as one supervised multiclass, multilabel problem in which we model the joint probability of acoustic features and words. We collect a data set of 1700 human-generated annotations that describe 500 Western popular music tracks. For each word in a vocabulary, we use this data to train a Gaussian mixture model (GMM) over an audio feature space. We estimate the parameters of the model using the weighted mixture hierarchies expectation maximization algorithm. This algorithm is more scalable to large data sets and produces better density estimates than standard parameter estimation techniques. The quality of the music annotations produced by our system is comparable with the performance of humans on the same task. Our ldquoquery-by-textrdquo system can retrieve appropriate songs for a large number of musically relevant words. We also show that our audition system is general by learning a model that can annotate and retrieve sound effects.
---
paper_title: Audio tag annotation and retrieval using tag count information

paper_content:

Audio tags correspond to keywords that people use to describe different aspects of a music clip, such as the genre, mood, and instrumentation. With the explosive growth of digital music available on the Web, automatic audio tagging, which can be used to annotate unknown music or retrieve desirable music, is becoming increasingly important. This can be achieved by training a binary classifier for each tag based on the labeled music data. However, since social tags are usually assigned by people with different levels of musical knowledge, they inevitably contain noisy information. To address the noisy label problem, we propose a novel method that exploits the tag count information. By treating the tag counts as costs, we model the audio tagging problem as a cost-sensitive classification problem. The results of audio tag annotation and retrieval experiments show that the proposed approach outperforms our previous method, which won the MIREX 2009 audio tagging competition.
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paper_title: Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions

paper_content:

Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.
---
paper_title: Classification of audio signals using SVM and RBFNN

paper_content:

In the age of digital information, audio data has become an important part in many modern computer applications. Audio classification has been becoming a focus in the research of audio processing and pattern recognition. Automatic audio classification is very useful to audio indexing, content-based audio retrieval and on-line audio distribution, but it is a challenge to extract the most common and salient themes from unstructured raw audio data. In this paper, we propose effective algorithms to automatically classify audio clips into one of six classes: music, news, sports, advertisement, cartoon and movie. For these categories a number of acoustic features that include linear predictive coefficients, linear predictive cepstral coefficients and mel-frequency cepstral coefficients are extracted to characterize the audio content. Support vector machines are applied to classify audio into their respective classes by learning from training data. Then the proposed method extends the application of neural network (RBFNN) for the classification of audio. RBFNN enables nonlinear transformation followed by linear transformation to achieve a higher dimension in the hidden space. The experiments on different genres of the various categories illustrate the results of classification are significant and effective.
---
paper_title: Improving music genre classification using collaborative tagging data

paper_content:

As a fundamental and critical component of music information retrieval (MIR) systems, music genre classification has attracted considerable research attention. Automatically classifying music by genre is, however, a challenging problem due to the fact that music is an evolving art. While most of the existing work categorizes music using features extracted from music audio signals, in this paper, we propose to exploit the semantic information embedded in tags supplied by users of social networking websites. Particularly, we consider the tag information by creating a graph of tracks so that tracks are neighbors if they are similar in terms of their associated tags. Two classification methods based on the track graph are developed. The first one employs a classification scheme which simultaneously considers the audio content and neighborhood of tracks. In contrast, the second one is a two-level classifier which initializes genre label for unknown tracks using their audio content, and then iteratively updates the genres considering the influence from their neighbors. A set of optimizing strategies are designed for the purpose of further enhancing the quality of the two-level classifier. Extensive experiments are conducted on real-world data collected from Last.fm. Promising experimental results demonstrate the benefit of using tags for accurate music genre classification.
---
paper_title: Content-based classification, search, and retrieval of audio

paper_content:

Many audio and multimedia applications would benefit from the ability to classify and search for audio based on its characteristics. The audio analysis, search, and classification engine described here reduces sounds to perceptual and acoustical features. This lets users search or retrieve sounds by any one feature or a combination of them, by specifying previously learned classes based on these features, or by selecting or entering reference sounds and asking the engine to retrieve similar or dissimilar sounds.
---
paper_title: Comparison of techniques for environmental sound recognition

paper_content:

This paper presents a comprehensive comparative study of artificial neural networks, learning vector quantization and dynamic time warping classification techniques combined with stationary/non-stationary feature extraction for environmental sound recognition. Results show 70% recognition using mel frequency cepstral coefficients or continuous wavelet transform with dynamic time warping.
---
paper_title: Using image processing to detect and classify narrow-band cricket and frog calls.

paper_content:

An automatic call recognition (ACR) process is described that uses image processing techniques on spectrogram images to detect and classify constant-frequency cricket and frog calls recorded amidst a background of evening sounds found in a lowland Costa Rican rainforest. This process involves using image blur filters along with thresholding filters to isolate likely calling events. Features of these events, notably the event's central frequency, duration and bandwidth, along with the type of blur filter applied, are used with a Bayesian classifier to make identifications of the different calls. Of the 22 distinct sonotypes (calls presumed to be species-specific) recorded in the study site, 17 of them were recorded in high enough numbers to both train and test the classifier. The classifier approaches 100% true-positive accuracy for these 17 sonotypes, but also has a high false-negative rate (over 50% for 4 sonotypes). The very high true-positive accuracy of this process enables its use for monitoring singing crickets (and some frog species) in tropical forests.
---
paper_title: Searching for music: from melodies in mind to the resources on the web

paper_content:

The paper describes existing solutions that suit various scenarios of music search including audio fingerprint and query-by-humming approaches. Pitfalls of the Earth Mover's Distance algorithm (EMD) are analyzed as well as situations where the quality of comparing music patterns is not good enough. We propose the adaptation of the EMD algorithm that allows to take into account characteristics of note significance in the given music fragment. We examine the ways to formalize human music perception factors with the help of knowledge bases and statistical information gathered in process of music search.
---
paper_title: On the suitability of state-ofthe-art music information retrieval methods for analyzing , categorizing and accessing non-Western and ethnic music collections

paper_content:

With increasing amounts of music being available in digital form, research in music information retrieval has turned into a dominant field to support organization of and easy access to large collections of music. Yet, most research is focussed traditionally on Western music, mostly in the form of mastered studio recordings. This leaves the question whether current music information retrieval approaches can also be applied to collections of non-Western and in particular ethnic music with completely different characteristics and requirements. In this work we analyze the performance of a range of automatic audio description algorithms on three music databases with distinct characteristics, specifically a Western music collection used previously in research benchmarks, a collection of Latin American music with roots in Latin American culture, but following Western tonality principles, as well as a collection of field recordings of ethnic African music. The study quantitatively shows the advantages and shortcomings of different feature representations extracted from music on the basis of classification tasks, and presents an approach to visualize, access and interact with ethnic music collections in a structured way.
---
paper_title: Bird classification algorithms: theory and experimental results

paper_content:

To minimize the number of birdstrikes, a common method is to use microphone arrays to monitor and identify dangerous birds near the airport or some critical locations in the airspace. However, it was recognized that the range of existing ground-based acoustic monitoring devices is only limited to a few hundred meters. Moreover, the bird classification performance in low signal-to-noise environments such as airports is not very satisfactory. This paper summarizes the development of a high performance bird classification system using a hidden Markov model (HMM) and Gaussian mixture model (GMM). Experimental results verified the classification performance.
---
paper_title: Music Information Retrieval Using Social Tags and Audio

paper_content:

In this paper we describe a novel approach to applying text-based information retrieval techniques to music collections. We represent tracks with a joint vocabulary consisting of both conventional words, drawn from social tags, and audio muswords, representing characteristics of automatically-identified regions of interest within the signal. We build vector space and latent aspect models indexing words and muswords for a collection of tracks, and show experimentally that retrieval with these models is extremely well-behaved. We find in particular that retrieval performance remains good for tracks by artists unseen by our models in training, and even if tags for their tracks are extremely sparse.
---
paper_title: Nearest-Neighbor Automatic Sound Annotation with a WordNet Taxonomy

paper_content:

Sound engineers need to access vast collections of sound effects for their film and video productions. Sound effects providers rely on text-retrieval techniques to give access to their collections. Currently, audio content is annotated manually, which is an arduous task. Automatic annotation methods, normally fine-tuned to reduced domains such as musical instruments or limited sound effects taxonomies, are not mature enough for labeling with great detail any possible sound. A general sound recognition tool would require first, a taxonomy that represents the world and, second, thousands of classifiers, each specialized in distinguishing little details. We report experimental results on a general sound annotator. To tackle the taxonomy definition problem we use WordNet, a semantic network that organizes real world knowledge. In order to overcome the need of a huge number of classifiers to distinguish many different sound classes, we use a nearest-neighbor classifier with a database of isolated sounds unambiguously linked to WordNet concepts. A 30% concept prediction is achieved on a database of over 50,000 sounds and over 1600 concepts.
---
paper_title: Automatic mood detection from acoustic music data

paper_content:

Music mood describes the inherent emotional meaning of a music clip. It is helpful in music understanding, music search and some music-related applications. In this paper, a hierarchical framework is presented to automate the task of mood detection from acoustic music data, by following some music psychological theories in western cultures. Three feature sets , intensity, timbre and rhythm, are extracted to represent the characteristics of a music clip. Moreover, a mood tracking approach is also presented for a whole piece of music. Experimental evaluations indicate that the proposed algorithms produce satisfactory results.
---
paper_title: Features for Audio and Music Classification

paper_content:

Four audio feature sets are evaluated in their ability to classify five general audio classes and seven popular music genres. The feature sets include low-level signal properties, mel-frequency spectral coefficients, and two new sets based on perceptual models of hearing. The temporal behavior of the features is analyzed and parameterized and these parameters are included as additional features. Using a standard Gaussian framework for classification, results show that the temporal behavior of features is important for both music and audio classification. In addition, classification is better, on average, if based on features from models of auditory perception rather than on standard features.
---
paper_title: Wavelets in Recognition of Bird Sounds

paper_content:

This paper presents a novel method to recognize inharmonic and transient bird sounds efficiently. The recognition algorithm consists of feature extraction using wavelet decomposition and recognition using either supervised or unsupervised classifier. The proposed method was tested on sounds of eight bird species of which five species have inharmonic sounds and three reference species have harmonic sounds. Inharmonic sounds are not well matched to the conventional spectral analysis methods, because the spectral domain does not include any visible trajectories that computer can track and identify. Thus, the wavelet analysis was selected due to its ability to preserve both frequency and temporal information, and its ability to analyze signals which contain discontinuities and sharp spikes. The shift invariant feature vectors calculated from the wavelet coefficients were used as inputs of two neural networks: the unsupervised self-organizing map (SOM) and the supervised multilayer perceptron (MLP). The results were encouraging: the SOM network recognized 78% and the MLP network 96% of the test sounds correctly.
---
paper_title: Musical genre classification of audio signals

paper_content:

Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals. In this paper, the automatic classification of audio signals into an hierarchy of musical genres is explored. More specifically, three feature sets for representing timbral texture, rhythmic content and pitch content are proposed. The performance and relative importance of the proposed features is investigated by training statistical pattern recognition classifiers using real-world audio collections. Both whole file and real-time frame-based classification schemes are described. Using the proposed feature sets, classification of 61% for ten musical genres is achieved. This result is comparable to results reported for human musical genre classification.
---
paper_title: Listening to nature : acoustic monitoring of the environment

paper_content:

The requirement to monitor the rapid pace of environmental change due to global warming and to human development is producing large volumes of data but placing much stress on the capacity of ecologists to store, analyse and visualise that data. To date, much of the data has been provided by low level sensors monitoring soil moisture, dissolved nutrients, light intensity, gas composition and the like. However, a significant part of an ecologist’s work is to obtain information about species diversity, distributions and relationships. This task typically requires the physical presence of an ecologist in the field, listening and watching for species of interest. It is an extremely difficult task to automate because of the higher order difficulties in bandwidth, data management and intelligent analysis if one wishes to emulate the highly trained eyes and ears of an ecologist. This paper is concerned with just one part of the bigger challenge of environmental monitoring – the acquisition and analysis of acoustic recordings of the environment. Our intention is to provide helpful tools to ecologists – tools that apply information technologies and computational technologies to all aspects of the acoustic environment. ::: ::: The on-line system which we are building in conjunction with ecologists offers an integrated approach to recording, data management and analysis. The ecologists we work with have different requirements and therefore we have adopted the toolbox approach, that is, we offer a number of different web services that can be concatenated according to need. In particular, one group of ecologists is concerned with identifying the presence or absence of species and their distributions in time and space. Another group, motivated by legislative requirements for measuring habitat condition, are interested in summary indices of environmental health. In both case, the key issues are scalability and automation.
---
paper_title: Comparison of techniques for environmental sound recognition

paper_content:

This paper presents a comprehensive comparative study of artificial neural networks, learning vector quantization and dynamic time warping classification techniques combined with stationary/non-stationary feature extraction for environmental sound recognition. Results show 70% recognition using mel frequency cepstral coefficients or continuous wavelet transform with dynamic time warping.
---
paper_title: A toolbox for animal call recognition

paper_content:

Monitoring the natural environment is increasingly important as habit degradation and climate change reduce the world's biodiversity. We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales. One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal's proximity to the microphone. Second, initial experimentation suggested that no single recognizer could deal with the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and exper...
---
paper_title: Classification of acoustic events using SVM-based clustering schemes

paper_content:

Acoustic events produced in controlled environments may carry information useful for perceptually aware interfaces. In this paper we focus on the problem of classifying 16 types of meeting-room acoustic events. First of all, we have defined the events and gathered a sound database. Then, several classifiers based on support vector machines (SVM) are developed using confusion matrix based clustering schemes to deal with the multi-class problem. Also, several sets of acoustic features are defined and used in the classification tests. In the experiments, the developed SVM-based classifiers are compared with an already reported binary tree scheme and with their correlative Gaussian mixture model (GMM) classifiers. The best results are obtained with a tree SVM-based classifier that may use a different feature set at each node. With it, a 31.5% relative average error reduction is obtained with respect to the best result from a conventional binary tree scheme.
---
paper_title: An efficient code for environmental sound classification.

paper_content:

An efficient code for classifying environmental sounds is described that exploits a recent significant advance in signal processing known as compressed sensing (CS) [cf.Donoho, D. (2006). IEEE Trans. Inf. Theory 52, 1289–1306]. CS involves a novel approach to sampling in which the salient information in signals is recovered from the projection onto a small set of random basis functions. The advantage of the random basis over traditional Fourier or wavelet representations is that it allows accurate classification at low target-to-interference ratios based on few samples and little or no prior information about signals.
---
paper_title: Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring

paper_content:

Trends in bird population sizes are an important indicator in nature conservation but measuring such sizes is a very difficult, labour intensive process. Enormous progress in audio signal processing and pattern recognition in recent years makes it possible to incorporate automated methods into the detection of bird vocalisations. These methods can be employed to support the census of population sizes. We report about a study testing the feasibility of bird monitoring supported by automatic bird song detection. In particular, we describe novel algorithms for the detection of the vocalisations of two endangered bird species and show how these can be used in automatic habitat mapping. These methods are based on detecting temporal patterns in a given frequency band typical for the species. Special effort is put into the suppression of the noise present in real-world audio scenes. Our results show that even in real-world recording conditions high recognition rates with a tolerable rate of false positive detections are possible.
---
paper_title: Using image processing to detect and classify narrow-band cricket and frog calls.

paper_content:

An automatic call recognition (ACR) process is described that uses image processing techniques on spectrogram images to detect and classify constant-frequency cricket and frog calls recorded amidst a background of evening sounds found in a lowland Costa Rican rainforest. This process involves using image blur filters along with thresholding filters to isolate likely calling events. Features of these events, notably the event's central frequency, duration and bandwidth, along with the type of blur filter applied, are used with a Bayesian classifier to make identifications of the different calls. Of the 22 distinct sonotypes (calls presumed to be species-specific) recorded in the study site, 17 of them were recorded in high enough numbers to both train and test the classifier. The classifier approaches 100% true-positive accuracy for these 17 sonotypes, but also has a high false-negative rate (over 50% for 4 sonotypes). The very high true-positive accuracy of this process enables its use for monitoring singing crickets (and some frog species) in tropical forests.
---
paper_title: A toolbox for animal call recognition

paper_content:

Monitoring the natural environment is increasingly important as habit degradation and climate change reduce the world's biodiversity. We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales. One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal's proximity to the microphone. Second, initial experimentation suggested that no single recognizer could deal with the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and exper...
---
paper_title: A call-independent and automatic acoustic system for the individual recognition of animals: A novel model using four passerines

paper_content:

Research into acoustic recognition systems for animals has focused on call-dependent and species identification rather than call-independent and individual identification. Here we present a system for automatic call-independent individual recognition using mel-frequency cepstral coefficients and Gaussian mixture models across four passerine species. To our knowledge this is the first application of these techniques to the individual recognition of birds, and the results are promising. Accuracies of 89.1-92.5% were achieved and the acoustic feature and classifier method developed here have excellent potential for individual animal recognition and can be easily applied to other species.
---
paper_title: Semi-automatic classification of bird vocalizations using spectral peak tracks

paper_content:

Automatic off-line classification and recognition of bird vocalizations has been a subject of interest to ornithologists and pattern detection researchers for many years. Several new applications, including bird vocalization classification for aircraft bird strike avoidance, will require real time classification in the presence of noise and other disturbances. The vocalizations of many common bird species can be represented using a sum-of-sinusoids model. An experiment using computer software to perform peak tracking of spectral analysis data demonstrates the usefulness of the sum-of-sinusoids model for rapid automatic recognition of isolated bird syllables. The technique derives a set of spectral features by time-variant analysis of the recorded bird vocalizations, then performs a calculation of the degree to which the derived parameters match a set of stored templates that were determined from a set of reference bird vocalizations. The results of this relatively simple technique are favorable for both c...
---
paper_title: Comparison of pattern recognition techniques for the classification of impact acoustic emissions.

paper_content:

Current day condition monitoring applications involving wood are mostly carried out through visual inspection and if necessary some impact acoustic examination is carried out. These inspections are mainly done intuitively by skilled personnel. In this paper, a pattern recognition approach has been considered to automate such intuitive human skills for the development of robust and reliable methods within the area. The study presents a comparison of several pattern recognition techniques combined with various stationary feature extraction techniques for classification of impact acoustic emissions. Further issues concerning feature fusion are discussed as well. It is hoped that this kind of broad analysis could be used to handle a wide spectrum of tasks within the area, and would provide a perfect ground for future research directions. A brief introduction to the techniques is provided for the benefit of the readers unfamiliar with the techniques. Pattern classifiers such as support vector machines, etc. are combined with stationary feature extraction techniques such as linear predictive cepstral coefficients, etc. Results from support vector machines in combination with linear predictive cepstral coefficients delivered good classification rates. However, Gaussian mixture models delivered higher classification rates when feature fusion is proposed.
---
paper_title: Wavelets in Recognition of Bird Sounds

paper_content:

This paper presents a novel method to recognize inharmonic and transient bird sounds efficiently. The recognition algorithm consists of feature extraction using wavelet decomposition and recognition using either supervised or unsupervised classifier. The proposed method was tested on sounds of eight bird species of which five species have inharmonic sounds and three reference species have harmonic sounds. Inharmonic sounds are not well matched to the conventional spectral analysis methods, because the spectral domain does not include any visible trajectories that computer can track and identify. Thus, the wavelet analysis was selected due to its ability to preserve both frequency and temporal information, and its ability to analyze signals which contain discontinuities and sharp spikes. The shift invariant feature vectors calculated from the wavelet coefficients were used as inputs of two neural networks: the unsupervised self-organizing map (SOM) and the supervised multilayer perceptron (MLP). The results were encouraging: the SOM network recognized 78% and the MLP network 96% of the test sounds correctly.
---
paper_title: Automated sound recording and analysis techniques for bird surveys and conservation

paper_content:

There is a great need for increased use and further development of automated sound recording and analysis of avian sounds. Birds are critical to ecosystem functioning so techniques to make avian monitoring more efficient and accurate will greatly benefit science and conservation efforts. We provide an overview of the hardware approaches to automated sound recording as well as an overview of the prominent techniques used in software to automatically detect and classify avian sound. We provide a comparative summary of examples of three general categories of hardware solutions for automating sound recording which include a hardware interface for a scheduling timer to control a standalone commercial recorder, a programmable recording device, and a single board computer. We also describe examples of the two main approaches to improving microphone performance for automated recorders through small arrays of microphone elements and using waveguides. For the purposes of thinking about automated sound analysis, we suggest five basic sound fragment types of avian sound and discuss a variety of techniques to automatically detect and classify avian sounds to species level, as well as their limitations. A variety of the features to measure for the various call types are provided, along with a variety of classification methods for those features. They are discussed in context of general performance as well as the monitoring and conservation efforts they are used in.
---
paper_title: Sequence alignment kernel for recognition of promoter regions

paper_content:

In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 σ 70 -promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data. Availability: The demo version of our method is accessible from our website http://mendel.cs.rhul.ac.uk/
---

Title: A Survey of Tagging Techniques for Music, Speech, and Environmental Sound

Section 1: Study Objects
Description 1: Discusses the objects of this study, covering human speech, music, and environmental sounds.

Section 2: What is Tagging?
Description 2: Defines the act of tagging in the context of music, speech, and environmental sounds.

Section 3: Application Areas
Description 3: Explores various applications and systems involved in sound tagging and the advantages of tagging in those areas.

Section 4: Automatic Tagging
Description 4: Describes techniques for extracting music information automatically, discussing methods based on audio features and the combination of social tags and audio features.

Section 5: Progress to Date and Research Gaps
Description 5: Summarizes the current achievements and identifies research gaps in social tagging and automatic tagging.

Section 6: Speech Tagging
Description 6: Presents the challenges and main directions in speech tagging and categorizes types of speech recognition systems and techniques.

Section 7: Environmental Sound Tagging
Description 7: Examines the challenges and methods in tagging environmental sounds, both inner room and outside, and includes manual and semi-automatic tagging methods.

Section 8: Manuals tagging
Description 8: Details the manual tagging process, its accuracy, and the challenges presented by large volumes of data.

Section 9: Semi-Automatic Tagging
Description 9: Introduces semi-automatic tagging as a hybrid approach combining manual and automatic techniques to improve tagging accuracy.

Section 10: Commons
Description 10: Discusses the commonalities in tagging techniques across music, speech, and environmental sounds.

Section 11: Differences
Description 11: Highlights the differences between music, speech, and environmental sound tagging, focusing on challenges like noise reduction and specific features/classifiers.

Section 12: Research Gaps
Description 12: Identifies the general gaps existing in sound tagging, including dataset scarcity and handling large-scale data.

Section 13: Datasets
Description 13: Lists published datasets for research in sound tagging and discusses their importance in facilitating study across different regions.

Section 14: Research Tools for Sound Tagging
Description 14: Reviews common tools used in sound tagging, highlighting those specific to music and environmental sounds.

Section 15: Evaluation Criteria
Description 15: Describes common evaluation methods such as precision, recall, F-score, and ROC curves used to measure accuracy in sound tagging.

Section 16: Worldwide Research
Description 16: Summarizes the global contributions to sound tagging, showing the distribution of research efforts and publications over the years.

Section 17: Conclusion
Description 17: Provides a comprehensive conclusion of the survey, summarizing the key findings and future directions in sound tagging research.