An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing
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paper_title: The low power energy aware processing (LEAP)embedded networked sensor system

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A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, low power energy aware processing (LEAP), has been developed. The LEAP architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems. This paper also describes a new distributed node testbed demonstrating that by exploiting high high energy efficiency components and enabling proper on-demand scheduling, the LEAP architecture may meet both sensing performance and energy dissipation objectives for a broad class of applications.
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paper_title: FIDO: a Field Integrated Design & Operations Rover for Mars Surface Exploration

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We overview or recent development and testing of the FIDO rover, an advanced technology prototype for long range mobile planetary science.
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paper_title: Experiences with the Extensible Sensing System ESS

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Author(s): Richard Guy; Ben Greenstein; John Hicks; Rahul Kapur; Nithya Ramanathan; Tom Schoellhammer; Thanos Stathopoulos; Karen Weeks; Kevin Chang; Lew Girod; Deborah Estrin | Abstract: The Extensible Sensing System (ESS) has been in use for several years in a variety of sensor network deployments. It is a key component of a collection of tools that together are a nearly complete, end-to-end, sensor-to-user facility for deploying and managing a sensor network. This paper provides the context and architectural overview of ESS, along with selected deployment details and a series of lessons learned. Lesson areas include connectivity, interactivity, energy vs. robustness, vertical integration, and real-time visibility. The current version of ESS reflects changes from these lessons; further, new tools are in development that complement ESS.
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paper_title: A macroscope in the redwoods

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The wireless sensor network "macroscope" offers the potential to advance science by enabling dense temporal and spatial monitoring of large physical volumes. This paper presents a case study of a wireless sensor network that recorded 44 days in the life of a 70-meter tall redwood tree, at a density of every 5 minutes in time and every 2 meters in space. Each node measured air temperature, relative humidity, and photosynthetically active solar radiation. The network captured a detailed picture of the complex spatial variation and temporal dynamics of the microclimate surrounding a coastal redwood tree. This paper describes the deployed network and then employs a multi-dimensional analysis methodology to reveal trends and gradients in this large and previously-unobtainable dataset. An analysis of system performance data is then performed, suggesting lessons for future deployments.
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paper_title: Future of analog in the VLSI environment

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It is argued analog circuits will remain irreplaceable for the implementation of the interface circuitry between digital processing and the external world. Most of their characteristics can be improved with scaled-down processes. Analog circuits will also compete with digital and will remain advantageous for low precision signal processing. Since little precision is required to carry out many cognitive tasks, analog VLSI (very-large-scale integration) will dominate in the implementation of many types of neural networks. >
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paper_title: The low power energy aware processing (LEAP)embedded networked sensor system

paper_content:

A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, low power energy aware processing (LEAP), has been developed. The LEAP architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems. This paper also describes a new distributed node testbed demonstrating that by exploiting high high energy efficiency components and enabling proper on-demand scheduling, the LEAP architecture may meet both sensing performance and energy dissipation objectives for a broad class of applications.
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paper_title: Instrumenting the world with wireless sensor networks

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Pervasive micro-sensing and actuation may revolutionize the way in which we understand and manage complex physical systems: from airplane wings to complex ecosystems. The capabilities for detailed physical monitoring and manipulation offer enormous opportunities for almost every scientific discipline, and it will alter the feasible granularity of engineering. We identify opportunities and challenges for distributed signal processing in networks of these sensing elements and investigate some of the architectural challenges posed by systems that are massively distributed, physically-coupled, wirelessly networked, and energy limited.
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paper_title: Maximum-likelihood source localization and unknown sensor location estimation for wideband signals in the near-field

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In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator.
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paper_title: Fine-grained network time synchronization using reference broadcasts

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Recent advances in miniaturization and low-cost, low-power design have led to active research in large-scale networks of small, wireless, low-power sensors and actuators. Time synchronization is critical in sensor networks for diverse purposes including sensor data fusion, coordinated actuation, and power-efficient duty cycling. Though the clock accuracy and precision requirements are often stricter than in traditional distributed systems, strict energy constraints limit the resources available to meet these goals.We present Reference-Broadcast Synchronization, a scheme in which nodes send reference beacons to their neighbors using physical-layer broadcasts. A reference broadcast does not contain an explicit timestamp; instead, receivers use its arrival time as a point of reference for comparing their clocks. In this paper, we use measurements from two wireless implementations to show that removing the sender's nondeterminism from the critical path in this way produces high-precision clock agreement (1.85 ± 1.28μsec, using off-the-shelf 802.11 wireless Ethernet), while using minimal energy. We also describe a novel algorithm that uses this same broadcast property to federate clocks across broadcast domains with a slow decay in precision (3.68 ± 2.57μsec after 4 hops). RBS can be used without external references, forming a precise relative timescale, or can maintain microsecond-level synchronization to an external timescale such as UTC. We show a significant improvement over the Network Time Protocol (NTP) under similar conditions.
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paper_title: Architecture and evaluation of an unplanned 802.11b mesh network

paper_content:

This paper evaluates the ability of a wireless mesh architecture to provide high performance Internet access while demanding little deployment planning or operational management. The architecture considered in this paper has unplanned node placement (rather than planned topology), omni-directional antennas (rather than directional links), and multi-hop routing (rather than single-hop base stations). These design decisions contribute to ease of deployment, an important requirement for community wireless networks. However, this architecture carries the risk that lack of planning might render the network's performance unusably low. For example, it might be necessary to place nodes carefully to ensure connectivity; the omni-directional antennas might provide uselessly short radio ranges; or the inefficiency of multi-hop forwarding might leave some users effectively disconnected.The paper evaluates this unplanned mesh architecture with a case study of the Roofnet 802.11b mesh network. Roofnet consists of 37 nodes spread over four square kilometers of an urban area. The network provides users with usable performance despite lack of planning: the average inter-node throughput is 627 kbits/second, even though the average route has three hops.The paper evaluates multiple aspects of the architecture: the effect of node density on connectivity and throughput; the characteristics of the links that the routing protocol elects to use; the usefulness of the highly connected mesh afforded by omni-directional antennas for robustness and throughput; and the potential performance of a single-hop network using the same nodes as Roofnet.
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paper_title: Generalized perceptual linear prediction features for animal vocalization analysis.

paper_content:

A new feature extraction model, generalized perceptual linear prediction (gPLP), is developed to calculate a set of perceptually relevant features for digital signal analysis of animal vocalizations. The gPLP model is a generalized adaptation of the perceptual linear prediction model, popular in human speech processing, which incorporates perceptual information such as frequency warping and equal loudness normalization into the feature extraction process. Since such perceptual information is available for a number of animal species, this new approach integrates that information into a generalized model to extract perceptually relevant features for a particular species. To illustrate, qualitative and quantitative comparisons are made between the species-specific model, generalized perceptual linear prediction (gPLP), and the original PLP model using a set of vocalizations collected from captive African elephants (Loxodonta africana) and wild beluga whales (Delphinapterus leucas). The models that incorporate perceptional information outperform the original human-based models in both visualization and classification tasks.
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paper_title: The Art of Electronics

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At long last, here is the thoroughly revised and updated third edition of the hugely successful The Art of Electronics. It is widely accepted as the best single authoritative book on electronic circuit design. In addition to new or enhanced coverage of many topics, the third edition includes 90 oscilloscope screenshots illustrating the behavior of working circuits, dozens of graphs giving highly useful measured data of the sort that is often buried or omitted in datasheets but which you need when designing circuits, and 80 tables (listing some 1650 active components), enabling intelligent choice of circuit components by listing essential characteristics (both specified and measured) of available parts. The new Art of Electronics retains the feeling of informality and easy access that helped make the earlier editions so successful and popular. It is an indispensable reference and the gold standard for anyone, student or researcher, professional or amateur, who works with electronic circuits.
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paper_title: Circuits, Signals, and Systems

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These twenty lectures have been developed and refined by Professor Siebert during the more than two decades he has been teaching introductory Signals and Systems courses at MIT. The lectures are designed to pursue a variety of goals in parallel: to familiarize students with the properties of a fundamental set of analytical tools; to show how these tools can be applied to help understand many important concepts and devices in modern communication and control engineering practice; to explore some of the mathematical issues behind the powers and limitations of these tools; and to begin the development of the vocabulary and grammar, common images and metaphors, of a general language of signal and system theory.Although broadly organized as a series of lectures, many more topics and examples (as well as a large set of unusual problems and laboratory exercises) are included in the book than would be presented orally. Extensive use is made throughout of knowledge acquired in early courses in elementary electrical and electronic circuits and differential equations.Contents: Review of the "classical" formulation and solution of dynamic equations for simple electrical circuits; The unilateral Laplace transform and its applications; System functions; Poles and zeros; Interconnected systems and feedback; The dynamics of feedback systems; Discrete-time signals and linear difference equations; The unilateral Z-transform and its applications; The unit-sample response and discrete-time convolution; Convolutional representations of continuous-time systems; Impulses and the superposition integral; Frequency-domain methods for general LTI systems; Fourier series; Fourier transforms and Fourier's theorem; Sampling in time and frequency; Filters, real and ideal; Duration, rise-time and bandwidth relationships: The uncertainty principle; Bandpass operations and analog communication systems; Fourier transforms in discrete-time systems; Random Signals; Modern communication systems."Circuits, Signals, and Systems" is included in The MIT Press Series in Electrical Engineering and Computer Science, copublished with McGraw-Hill.
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paper_title: Blind Beamforming on a Randomly Distributed Sensor Array System

paper_content:

We consider a digital signal processing sensor array system, based on randomly distributed sensor nodes, for surveillance and source localization applications. In most array processing the sensor array geometry is fixed and known and the steering array vector/manifold information is used in beamformation. In this system, array calibration may be impractical due to unknown placement and orientation of the sensors with unknown frequency/spatial responses. This paper proposes a blind beamforming technique, using only the measured sensor data, to form either a sample data or a sample correlation matrix. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector associated with the largest eigenvalue of a matrix eigenvalue problem. Theoretical justification of this approach uses a generalization of Szego's (1958) theory of the asymptotic distribution of eigenvalues of the Toeplitz form. An efficient blind beamforming time delay estimate of the dominant source is proposed. Source localization based on a least squares (LS) method for time delay estimation is also given. Results based on analysis, simulation, and measured acoustical sensor data show the effectiveness of this beamforming technique for signal enhancement and space-time filtering.
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paper_title: Maximum-likelihood source localization and unknown sensor location estimation for wideband signals in the near-field

paper_content:

In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator.
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paper_title: Robust endpoint detection and energy normalization for real-time speech and speaker recognition

paper_content:

When automatic speech recognition (ASR) and speaker verification (SV) are applied in adverse acoustic environments, endpoint detection and energy normalization can be crucial to the functioning of both systems. In low signal-to-noise ratio (SNR) and nonstationary environments, conventional approaches to endpoint detection and energy normalization often fail and ASR performances usually degrade dramatically. The purpose of this paper is to address the endpoint problem. For ASR, we propose a real-time approach. It uses an optimal filter plus a three-state transition diagram for endpoint detection. The filter is designed utilizing several criteria to ensure accuracy and robustness. It has almost invariant response at various background noise levels. The detected endpoints are then applied to energy normalization sequentially. Evaluation results show that the proposed algorithm significantly reduces the string error rates in low SNR situations. The reduction rates even exceed 50% in several evaluated databases. For SV, we propose a batch-mode approach. It uses the optimal filter plus a two-mixture energy model for endpoint detection. The experiments show that the batch-mode algorithm can detect endpoints as accurately as using HMM forced alignment while the proposed one has much less computational complexity.
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paper_title: Robust text-independent speaker identification using Gaussian mixture speaker models

paper_content:

This paper introduces and motivates the use of Gaussian mixture models (GMM) for robust text-independent speaker identification. The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modeling speaker identity. The focus of this work is on applications which require high identification rates using short utterance from unconstrained conversational speech and robustness to degradations produced by transmission over a telephone channel. A complete experimental evaluation of the Gaussian mixture speaker model is conducted on a 49 speaker, conversational telephone speech database. The experiments examine algorithmic issues (initialization, variance limiting, model order selection), spectral variability robustness techniques, large population performance, and comparisons to other speaker modeling techniques (uni-modal Gaussian, VQ codebook, tied Gaussian mixture, and radial basis functions). The Gaussian mixture speaker model attains 96.8% identification accuracy using 5 second clean speech utterances and 80.8% accuracy using 15 second telephone speech utterances with a 49 speaker population and is shown to outperform the other speaker modeling techniques on an identical 16 speaker telephone speech task. >
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paper_title: Classifier-based non-linear projection for adaptive endpointing of continuous speech

paper_content:

In this paper we present an algorithm for segmenting or locating the endpoints of speech in a continuous signal stream. The proposed algorithm is based on non-linear likelihood-based projections derived from a Bayesian classifier. It utilizes class distributions in a speech/non-speech classifier to project the signal into a 2-dimensional space where, in the ideal case, optimal classification can be performed with a simple linear discriminant. The projection results in the transformation of diffuse, nebulous classes in high-dimensional space into compact clusters in the low-dimensional space that can be easily separated by simple clustering mechanisms. In this space, decision boundaries for optimal classification can be more easily identified using simple clustering criteria. The segmentation algorithm proposed utilizes this property to determine and update optimal classification thresholds continuously for the signal being segmented. The performance of the proposed algorithm has been evaluated on data recorded under extremely diverse environmental noise conditions. The experiments show that the algorithm performs comparably to manual segmentations even under these diverse conditions.
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paper_title: Automatic classification and speaker identification of African elephant (Loxodonta africana) vocalizations

paper_content:

A hidden Markov model (HMM) system is presented for automatically classifying African elephant vocalizations. The development of the system is motivated by successful models from human speech analysis and recognition. Classification features include frequency-shifted Mel-frequency cepstral coefficients (MFCCs) and log energy, spectrally motivated features which are commonly used in human speech processing. Experiments, including vocalization type classification and speaker identification, are performed on vocalizations collected from captive elephants in a naturalistic environment. The system classified vocalizations with accuracies of 94.3% and 82.5% for type classification and speaker identification classification experiments, respectively. Classification accuracy, statistical significance tests on the model parameters, and qualitative analysis support the effectiveness and robustness of this approach for vocalization analysis in nonhuman species.
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paper_title: Support vector machines for speaker and language recognition

paper_content:

Abstract Support vector machines (SVMs) have proven to be a powerful technique for pattern classification. SVMs map inputs into a high-dimensional space and then separate classes with a hyperplane. A critical aspect of using SVMs successfully is the design of the inner product, the kernel, induced by the high dimensional mapping. We consider the application of SVMs to speaker and language recognition. A key part of our approach is the use of a kernel that compares sequences of feature vectors and produces a measure of similarity. Our sequence kernel is based upon generalized linear discriminants. We show that this strategy has several important properties. First, the kernel uses an explicit expansion into SVM feature space—this property makes it possible to collapse all support vectors into a single model vector and have low computational complexity. Second, the SVM builds upon a simpler mean-squared error classifier to produce a more accurate system. Finally, the system is competitive and complimentary to other approaches, such as Gaussian mixture models (GMMs). We give results for the 2003 NIST speaker and language evaluations of the system and also show fusion with the traditional GMM approach.
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paper_title: Speaker verification using adapted Gaussian mixture models

paper_content:

Reynolds, Douglas A., Quatieri, Thomas F., and Dunn, Robert B., Speaker Verification Using Adapted Gaussian Mixture Models, Digital Signal Processing10(2000), 19?41.In this paper we describe the major elements of MIT Lincoln Laboratory's Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance is also described and discussed. Finally, representative performance benchmarks and system behavior experiments on NIST SRE corpora are presented.
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paper_title: Signal modeling techniques in speech recognition

paper_content:

A tutorial on signal processing in state-of-the-art speech recognition systems is presented, reviewing those techniques most commonly used. The four basic operations of signal modeling, i.e. spectral shaping, spectral analysis, parametric transformation, and statistical modeling, are discussed. Three important trends that have developed in the last five years in speech recognition are examined. First, heterogeneous parameter sets that mix absolute spectral information with dynamic, or time-derivative, spectral information, have become common. Second, similarity transform techniques, often used to normalize and decorrelate parameters in some computationally inexpensive way, have become popular. Third, the signal parameter estimation problem has merged with the speech recognition process so that more sophisticated statistical models of the signal's spectrum can be estimated in a closed-loop manner. The signal processing components of these algorithms are reviewed. >
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paper_title: Recognizing transient low-frequency whale sounds by spectrogram correlation

paper_content:

A method is described for the automatic recognition of transient animal sounds. Automatic recognition can be used in wild animal research, including studies of behavior, population, and impact of anthropogenic noise. The method described here, spectrogram correlation, is well-suited to recognition of animal sounds consisting of tones and frequency sweeps. For a sound type of interest, a two-dimensional synthetic kernel is constructed and cross-correlated with a spectrogram of a recording, producing a recognition function—the likelihood at each point in time that the sound type was present. A threshold is applied to this function to obtain discrete detection events, instants at which the sound type of interest was likely to be present. An extension of this method handles the temporal variation commonly present in animal sounds. Spectrogram correlation was compared to three other methods that have been used for automatic call recognition: matched filters, neural networks, and hidden Markov models. The test ...
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paper_title: Speaker recognition using neural networks and conventional classifiers

paper_content:

An evaluation of various classifiers for text-independent speaker recognition is presented. In addition, a new classifier is examined for this application. The new classifier is called the modified neural tree network (MNTN). The MNTN is a hierarchical classifier that combines the properties of decision trees and feedforward neural networks. The MNTN differs from the standard NTN in both the new learning rule used and the pruning criteria. The MNTN is evaluated for several speaker recognition experiments. These include closed- and open-set speaker identification and speaker verification. The database used is a subset of the TIMIT database consisting of 38 speakers from the same dialect region. The MNTN is compared with nearest neighbor classifiers, full-search, and tree-structured vector quantization (VQ) classifiers, multilayer perceptrons (MLPs), and decision trees. For closed-set speaker identification experiments, the full-search VQ classifier and MNTN demonstrate comparable performance. Both methods perform significantly better than the other classifiers for this task. The MNTN and full-search VQ classifiers are also compared for several speaker verification and open-set speaker-identification experiments. The MNTN is found to perform better than full-search VQ classifiers for both of these applications. In addition to matching or exceeding the performance of the VQ classifier for these applications, the MNTN also provides a logarithmic saving for retrieval. >
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paper_title: Seasonality of blue and fin whale calls and the influence of sea ice in the Western Antarctic Peninsula

paper_content:

The calling seasonality of blue (Balaenoptera musculus) and fin (B. physalus) whales was assessed using acoustic data recorded on seven autonomous acoustic recording packages (ARPs) deployed from March 2001 to February 2003 in the Western Antarctic Peninsula. Automatic detection and acoustic power analysis methods were used for determining presence and absence of whale calls. Blue whale calls were detected year round, on average 177 days per year, with peak calling in March and April, and a secondary peak in October and November. Lowest calling rates occurred between June and September, and in December. Fin whale calling rates were seasonal with calls detected between February and June (on average 51 days/year), and peak calling in May. Sea ice formed a month later and retreated a month earlier in 2001 than in 2002 over all recording sites. During the entire deployment period, detected calls of both species of whales showed negative correlation with sea ice concentrations at all sites, suggesting an absence of blue and fin whales in areas covered with sea ice. A conservative density estimate of calling whales from the acoustic data yields 0.43 calling blue whales per 1000 n mi2 and 1.30 calling fin whales per 1000 n mi2, which is about one-third higher than the density of blue whales and approximately equal to the density of fin whales estimated from the visual surveys.
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paper_title: Perceptual linear predictive (PLP) analysis of speech

paper_content:

A new technique for the analysis of speech, the perceptual linear predictive (PLP) technique, is presented and examined. This technique uses three concepts from the psychophysics of hearing to derive an estimate of the auditory spectrum: (1) the critical-band spectral resolution, (2) the equal-loudness curve, and (3) the intensity-loudness power law. The auditory spectrum is then approximated by an autoregressive all-pole model. A 5th-order all-pole model is effective in suppressing speaker-dependent details of the auditory spectrum. In comparison with conventional linear predictive (LP) analysis, PLP analysis is more consistent with human hearing. The effective second formant F2' and the 3.5-Bark spectral-peak integration theories of vowel perception are well accounted for. PLP analysis is computationally efficient and yields a low-dimensional representation of speech. These properties are found to be useful in speaker-independent automatic-speech recognition.
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paper_title: Partitioning the feature space of a classifier with linear hyperplanes

paper_content:

We describe the design and use of linear hyperplanes to partition the feature space of a classifier. The objective of the partitioning is to minimize the average entropy of the class distribution in the final partitions. The hyperplanes are characterized by a vector /spl nu//sub n/ and scalar h/sub n/, which are computed with the objective of maximizing the mutual information associated with the partitioning. We show that the problem of designing the /spl nu//sub n/ can be simplified to an approximately equivalent problem, the solution of which, interestingly enough, turns out to be the linear discriminant of the data. We also describe a decision-tree based technique that partitions the feature space in a hierarchical fashion where each node in the decision tree represents a linear hyperplane that further partitions the feature space into two regions. The end result of the tree-growing process is that the entire feature space is partitioned into nonoverlapping regions where each region is bounded by a number of hyperplanes. As the criterion for the design of the hyperplanes is the minimization of the average class entropy of the regions, each region is characterized by the occurrence of only a small number of classes. The partitioning information provided by the decision-tree can then be used to eliminate a number of classes from being considered, and hence simplifies the job of the classifier. We show the application of the decision-tree as a preprocessor to a classifier in the speech recognition problem. Here the classes are modeled with mixtures of tens to hundreds of thousands of Gaussians, and the use of the partitioning information reduces the computation associated with the classification process by factors larger than 20 with negligible degradation in the word error rate (note, however, that the overall decoding process comprises other steps in addition to the classification-the computational speedup mentioned above does not affect the computation of these other steps). We also compare the performance of this quantization scheme with standard Gaussian clustering schemes and show that the decision-tree based quantization provides better performance in most cases.
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paper_title: Generalized perceptual linear prediction features for animal vocalization analysis.

paper_content:

A new feature extraction model, generalized perceptual linear prediction (gPLP), is developed to calculate a set of perceptually relevant features for digital signal analysis of animal vocalizations. The gPLP model is a generalized adaptation of the perceptual linear prediction model, popular in human speech processing, which incorporates perceptual information such as frequency warping and equal loudness normalization into the feature extraction process. Since such perceptual information is available for a number of animal species, this new approach integrates that information into a generalized model to extract perceptually relevant features for a particular species. To illustrate, qualitative and quantitative comparisons are made between the species-specific model, generalized perceptual linear prediction (gPLP), and the original PLP model using a set of vocalizations collected from captive African elephants (Loxodonta africana) and wild beluga whales (Delphinapterus leucas). The models that incorporate perceptional information outperform the original human-based models in both visualization and classification tasks.
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paper_title: Fundamentals Of Speech Recognition

paper_content:

1. Fundamentals of Speech Recognition. 2. The Speech Signal: Production, Perception, and Acoustic-Phonetic Characterization. 3. Signal Processing and Analysis Methods for Speech Recognition. 4. Pattern Comparison Techniques. 5. Speech Recognition System Design and Implementation Issues. 6. Theory and Implementation of Hidden Markov Models. 7. Speech Recognition Based on Connected Word Models. 8. Large Vocabulary Continuous Speech Recognition. 9. Task-Oriented Applications of Automatic Speech Recognition.
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paper_title: Boosted Learning in Dynamic Bayesian Networks for Multimodal Speaker Detection

paper_content:

Bayesian network models provide an attractive framework for multimodal sensor fusion. They combine an intuitive graphical representation with efficient algorithms for inference and learning. However, the unsupervised nature of standard parameter learning algorithms for Bayesian networks can lead to poor performance in classification tasks. We have developed a supervised learning framework for Bayesian networks, which is based on the Adaboost algorithm of Schapire and Freund. Our framework covers static and dynamic Bayesian networks with both discrete and continuous states. We have tested our framework in the context of a novel multimodal HCI application: a speech-based command and control interface for a Smart Kiosk. We provide experimental evidence for the utility of our boosted learning approach.
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paper_title: Missing-feature approaches in speech recognition

paper_content:

In this article we have reviewed a wide variety of techniques based on the identification of missing spectral features that have proved effective in reducing the error rates of automatic speech recognition systems. These approaches have been conspicuously effective in ameliorating the effects of transient maskers such as impulsive noise or background music. We described two broad classes of missing feature algorithms: feature-vector imputation algorithms (which restore unreliable components of incoming feature vectors) and classifier-modification algorithms (which dynamically reconfigure the classifier itself to cope with the effects of unreliable feature components). We reviewed the mathematics of four major missing feature techniques: the feature-imputation techniques of cluster-based reconstruction and covariance-based reconstruction, and the classifier-modification methods of class-conditional imputation and marginalization. We also discussed the ways in which the common feature extraction procedures of cepstral analysis, temporal-difference features, and mean subtraction can be handled by speech recognition systems that make use of missing feature techniques. We concluded with a discussion of a small number of selected experimental results. These results confirm the effectiveness of all types of missing feature approaches discussed in ameliorating the effects of both stationary and transient noise, as well as the particular effectiveness of both soft masks and fragment decoding.
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Title: An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing

Section 1: Introduction
Description 1: Introduce the broad area including design, implementation, and the deployment of remote acoustic sensors, and outline the different sections of the paper.

Section 2: Packaging and System Integration
Description 2: Discuss the mechanical aspects and decisions related to selecting and integrating components into a working embedded sensing system.

Section 3: Energy Management
Description 3: Describe the challenges and strategies associated with managing energy consumption in remote embedded acoustic systems.

Section 4: Factors Contributing to Energy Consumption in Acoustic Sensing
Description 4: Detail the primary factors that contribute to energy requirements, including analog front end and sampling, digital processing, storage, and network transmission.

Section 5: Energy Technologies
Description 5: Explore various energy storage and recharging technologies available for remote embedded sensing applications.

Section 6: Communication Environment and Data Reduction
Description 6: Evaluate different networking technologies and local data reduction policies that can be applied based on the connectivity and storage capacity.

Section 7: Signal Acquisition
Description 7: Expand on the aspects of signal acquisition, including minimizing electronics and environmental noise to secure good results from acoustic sensing systems.

Section 8: Signal Enhancement
Description 8: Discuss methods of enhancing the sampled and recorded signal digitally, such as filtering and beamforming techniques.

Section 9: Signal Detection and Classification
Description 9: Examine the strategies for detecting and classifying signals to provide useful information from large acoustic datasets.

Section 10: Directions for Research
Description 10: Identify significant areas for future research that can advance the capabilities and deployment of embedded sensors, focusing on power management, classification in noisy environments, and user interfaces.