An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. <s> BIB001 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. > <s> BIB002 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. <s> BIB003 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. > <s> BIB004 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. > <s> BIB005 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. <s> BIB006 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. <s> BIB007 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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 ... <s> BIB008 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. <s> BIB009 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. <s> BIB010 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. <s> BIB011 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. <s> BIB012 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. <s> BIB013 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. <s> BIB014 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Detection and Classification <s> 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. <s> BIB015
Signal detection and classification are necessary to provide useful information about large acoustic datasets which cannot be effectively summarized by human staff due to cost and time constraints. System designers must make choices about where the processing will occur, the feature set to use, and the types of classification schemes that will be implemented. Location of Processing. In most instances, the ideal situation is to perform all processing on the embedded sensor and to report classification results as part of the metadata for the acoustic data. For some applications, only the metadata itself need be transmitted, greatly reducing bandwidth requirements. An example of this type of system is one where tactical observations are reported to a command and control center  . In other cases, such as for bioacoustic studies, the acoustic data must be reported in addition to the metadata as it may be archived for further study or verification. For bioacousticians, it is frequently desirable to archive the entire acoustic signal as the data may be used for new studies at later dates BIB004 . For many embedded sensors, the power, computational, and storage requirements necessary to implement classifiers are prohibitive and the acoustic sensor either does limited or no preprocessing. In these cases, sampled or parameterized acoustic data is typically sent to a general purpose computer or cluster where detailed analysis is performed. Signal Detection. In most applications, it is desirable to have a low cost algorithm which can identify potential events of interest. This is typically accomplished using either a rule-or classifier-based signal detector. Rule-based classifiers are typically finite state machines with transition rules between signal and noise based upon a measure of the energy in the signal. Li et. al. BIB009 provide a good example of this type of system. In contrast, classifier-based systems such as BIB010 use machine learning techniques to discriminate between the signal and the noise. The advantage of the classifier-based systems is that they are usually more easily adapted to new environments and do not require as much tweaking of the parameters to obtain good results. Unfortunately, many classifier-based algorithms have higher computational cost and may not be as well suited for sensors with limited computational capacity. When multiple networked sensors detect the same event, it is possible to have the detectors collaborate to determine bearing and report the acoustic data with the highest SNR. Feature Extraction. For complex signals such as human speech, a representation of the short-time spectrum is typically desirable. Cepstral coefficients (CC) are derived either from the output of perceptual filterbanks such as the so-called Mel filters or from the signal's linear prediction coefficients (LPC) BIB002 (perceptual weighting is possible for LPC cepstra BIB001 ). The homomorphic transform used to create the cepstrum has the advantage of transforming signals that have been convolved into ones that are added, making it possible to approximate the vocal tract filter or BIB004 compensate for convolutional noise. It is common to provide information about the rate of spectral change by appending the first and second differences of the CCs to the feature vector. While perceptually weighted CC are well accepted for speech technologies, their use is not appropriate in many non-speech applications. For bioacousticians working with species whose hearing is radically different from humans, the use of human centric filters is inappropriate although it is still possible to obtain good results in certain cases BIB011  . Recently, Clemins et al. BIB014 have proposed an extension to the work of BIB001 to permit perceptual filters for species with known hearing characteristics. For non-biological noises, perceptual filters are not necessarily appropriate. Classification. A wide variety of classifiers have been applied to acoustic data. An issue that is likely to have an influence on classifier choice is whether or not the data has an expected structure over time such as speech or stereotyped animal calls. When the audio has unknown temporal characteristics (e.g. recognizing a speaker without requiring a predetermined phrase), classifiers that do not typically capture time domain structure have been shown to be successful. Examples of such classifiers include Gaussian mixture models (GMMs) BIB005 BIB007 , classification and regression trees (CARTs) BIB006 , support vector machines (SVMs) BIB015 , and neural networks BIB004 . Data with structure over time is typically recognized by other methods. When the acoustic pattern is relatively consistent from one production to the next, matched filters BIB012 , correlated spectrograms BIB008 , or dynamic time warping BIB003 can be used. For more variable acoustic data, hidden Markov models (HMMs) BIB003  are frequently used classifiers. HMMs provide a set of state dependent distributions and distributions which govern the transitions between states. As each observation is processed, the HMM has the possibility of transitioning to another state. The state sequence is considered to be hidden, and dynamic programming techniques are used to determine the likelihood of either all possible state transition sequences or the best one. Most current systems use GMMs to model the state dependent distributions, but many of the aforementioned time-independent classifiers  have been used in place of GMMs with excellent results. The implementations of classifiers cited throughout this discussion all assume that data has been sent to a centralized processing location and do not typically address the problem of distributed learning where decisions are based upon input from multiple sensors. While less studied, there are examples in the literature of classification systems which fuse multiple sensors. Garg et al. BIB013 demonstrate a system which uses an ensemble method to improve a Bayesian network which fuses the outputs of visual detectors and a simple audio signal detector. The interested reader is referred to  for a general introduction to distributed learning on a sensor network.