Pub Date : 2013-09-01DOI: 10.1109/TASL.2013.2248720
D. Giannoulis, Anssi Klapuri
A method is described for musical instrument recognition in polyphonic audio signals where several sound sources are active at the same time. The proposed method is based on local spectral features and missing-feature techniques. A novel mask estimation algorithm is described that identifies spectral regions that contain reliable information for each sound source, and bounded marginalization is then used to treat the feature vector elements that are determined to be unreliable. The mask estimation technique is based on the assumption that the spectral envelopes of musical sounds tend to be slowly-varying as a function of log-frequency and unreliable spectral components can therefore be detected as positive deviations from an estimated smooth spectral envelope. A computationally efficient algorithm is proposed for marginalizing the mask in the classification process. In simulations, the proposed method clearly outperforms reference methods for mixture signals. The proposed mask estimation technique leads to a recognition accuracy that is approximately half-way between a trivial all-one mask (all features are assumed reliable) and an ideal “oracle” mask.
{"title":"Musical Instrument Recognition in Polyphonic Audio Using Missing Feature Approach","authors":"D. Giannoulis, Anssi Klapuri","doi":"10.1109/TASL.2013.2248720","DOIUrl":"https://doi.org/10.1109/TASL.2013.2248720","url":null,"abstract":"A method is described for musical instrument recognition in polyphonic audio signals where several sound sources are active at the same time. The proposed method is based on local spectral features and missing-feature techniques. A novel mask estimation algorithm is described that identifies spectral regions that contain reliable information for each sound source, and bounded marginalization is then used to treat the feature vector elements that are determined to be unreliable. The mask estimation technique is based on the assumption that the spectral envelopes of musical sounds tend to be slowly-varying as a function of log-frequency and unreliable spectral components can therefore be detected as positive deviations from an estimated smooth spectral envelope. A computationally efficient algorithm is proposed for marginalizing the mask in the classification process. In simulations, the proposed method clearly outperforms reference methods for mixture signals. The proposed mask estimation technique leads to a recognition accuracy that is approximately half-way between a trivial all-one mask (all features are assumed reliable) and an ideal “oracle” mask.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2248720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62888540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1109/TASL.2013.2263141
N. Wachowski, M. Azimi-Sadjadi
This paper introduces a new framework for detecting, classifying, and estimating the signatures of multiple transient acoustical sources from a time-transform representation (TTR) of an audio waveform. A TTR is a vector observation sequence containing the coefficients of consecutive windows of data with respect to known sampled basis waveforms. A set of likelihood ratio tests is hierarchically applied to each time slice of a TTR to detect and classify signals in the presence of interference. Since the signatures of each acoustical event typically span several adjacent dependent observations, a Kalman filter is used to generate the parameters necessary for computing the likelihood values. The experimental results of applying the proposed method to a problem of detecting and classifying man-made and natural transient acoustical events in national park soundscape recordings attest to its effectiveness at performing the aforementioned tasks.
{"title":"Characterization of Multiple Transient Acoustical Sources From Time-Transform Representations","authors":"N. Wachowski, M. Azimi-Sadjadi","doi":"10.1109/TASL.2013.2263141","DOIUrl":"https://doi.org/10.1109/TASL.2013.2263141","url":null,"abstract":"This paper introduces a new framework for detecting, classifying, and estimating the signatures of multiple transient acoustical sources from a time-transform representation (TTR) of an audio waveform. A TTR is a vector observation sequence containing the coefficients of consecutive windows of data with respect to known sampled basis waveforms. A set of likelihood ratio tests is hierarchically applied to each time slice of a TTR to detect and classify signals in the presence of interference. Since the signatures of each acoustical event typically span several adjacent dependent observations, a Kalman filter is used to generate the parameters necessary for computing the likelihood values. The experimental results of applying the proposed method to a problem of detecting and classifying man-made and natural transient acoustical events in national park soundscape recordings attest to its effectiveness at performing the aforementioned tasks.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2263141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62890070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1109/TASL.2013.2263137
M. Souden, S. Araki, K. Kinoshita, T. Nakatani, H. Sawada
We propose a new framework for joint multichannel speech source separation and acoustic noise reduction. In this framework, we start by formulating the minimum-mean-square error (MMSE)-based solution in the context of multiple simultaneous speakers and background noise, and outline the importance of the estimation of the activities of the speakers. The latter is accurately achieved by introducing a latent variable that takes N+1 possible discrete states for a mixture of N speech signals plus additive noise. Each state characterizes the dominance of one of the N+1 signals. We determine the posterior probability of this latent variable, and show how it plays a twofold role in the MMSE-based speech enhancement. First, it allows the extraction of the second order statistics of the noise and each of the speech signals from the noisy data. These statistics are needed to formulate the multichannel Wiener-based filters (including the minimum variance distortionless response). Second, it weighs the outputs of these linear filters to shape the spectral contents of the signals' estimates following the associated target speakers' activities. We use the spatial and spectral cues contained in the multichannel recordings of the sound mixtures to compute the posterior probability of this latent variable. The spatial cue is acquired by using the normalized observation vector whose distribution is well approximated by a Gaussian-mixture-like model, while the spectral cue can be captured by using a pre-trained Gaussian mixture model for the log-spectra of speech. The parameters of the investigated models and the speakers' activities (posterior probabilities of the different states of the latent variable) are estimated via expectation maximization. Experimental results including comparisons with the well-known independent component analysis and masking are provided to demonstrate the efficiency of the proposed framework.
{"title":"A Multichannel MMSE-Based Framework for Speech Source Separation and Noise Reduction","authors":"M. Souden, S. Araki, K. Kinoshita, T. Nakatani, H. Sawada","doi":"10.1109/TASL.2013.2263137","DOIUrl":"https://doi.org/10.1109/TASL.2013.2263137","url":null,"abstract":"We propose a new framework for joint multichannel speech source separation and acoustic noise reduction. In this framework, we start by formulating the minimum-mean-square error (MMSE)-based solution in the context of multiple simultaneous speakers and background noise, and outline the importance of the estimation of the activities of the speakers. The latter is accurately achieved by introducing a latent variable that takes N+1 possible discrete states for a mixture of N speech signals plus additive noise. Each state characterizes the dominance of one of the N+1 signals. We determine the posterior probability of this latent variable, and show how it plays a twofold role in the MMSE-based speech enhancement. First, it allows the extraction of the second order statistics of the noise and each of the speech signals from the noisy data. These statistics are needed to formulate the multichannel Wiener-based filters (including the minimum variance distortionless response). Second, it weighs the outputs of these linear filters to shape the spectral contents of the signals' estimates following the associated target speakers' activities. We use the spatial and spectral cues contained in the multichannel recordings of the sound mixtures to compute the posterior probability of this latent variable. The spatial cue is acquired by using the normalized observation vector whose distribution is well approximated by a Gaussian-mixture-like model, while the spectral cue can be captured by using a pre-trained Gaussian mixture model for the log-spectra of speech. The parameters of the investigated models and the speakers' activities (posterior probabilities of the different states of the latent variable) are estimated via expectation maximization. Experimental results including comparisons with the well-known independent component analysis and masking are provided to demonstrate the efficiency of the proposed framework.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2263137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1109/TASL.2013.2263139
A. Southern, S. Siltanen, D. Murphy, L. Savioja
Synthesizing the room impulse response (RIR) of an arbitrary enclosure may be performed using a number of alternative acoustic modeling methods, each with their own particular advantages and limitations. This article is concerned with obtaining a hybrid RIR derived from both wave and geometric-acoustics based methods, optimized for use across different regions of time or frequency. Consideration is given to how such RIRs can be matched across modeling domains in terms of both amplitude and boundary behavior and the approach is verified using a number of standardised case studies.
{"title":"Room Impulse Response Synthesis and Validation Using a Hybrid Acoustic Model","authors":"A. Southern, S. Siltanen, D. Murphy, L. Savioja","doi":"10.1109/TASL.2013.2263139","DOIUrl":"https://doi.org/10.1109/TASL.2013.2263139","url":null,"abstract":"Synthesizing the room impulse response (RIR) of an arbitrary enclosure may be performed using a number of alternative acoustic modeling methods, each with their own particular advantages and limitations. This article is concerned with obtaining a hybrid RIR derived from both wave and geometric-acoustics based methods, optimized for use across different regions of time or frequency. Consideration is given to how such RIRs can be matched across modeling domains in terms of both amplitude and boundary behavior and the approach is verified using a number of standardised case studies.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2263139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1109/TASL.2013.2238732
Zhanyu Ma, A. Leijon, W. Kleijn
Quantization of the linear predictive coding parameters is an important part in speech coding. Probability density function (PDF)-optimized vector quantization (VQ) has been previously shown to be more efficient than VQ based only on training data. For data with bounded support, some well-defined bounded-support distributions (e.g., the Dirichlet distribution) have been proven to outperform the conventional Gaussian mixture model (GMM), with the same number of free parameters required to describe the model. When exploiting both the boundary and the order properties of the line spectral frequency (LSF) parameters, the distribution of LSF differences LSF can be modelled with a Dirichlet mixture model (DMM). We propose a corresponding DMM based VQ. The elements in a Dirichlet vector variable are highly mutually correlated. Motivated by the Dirichlet vector variable's neutrality property, a practical non-linear transformation scheme for the Dirichlet vector variable can be obtained. Similar to the Karhunen-Loève transform for Gaussian variables, this non-linear transformation decomposes the Dirichlet vector variable into a set of independent beta-distributed variables. Using high rate quantization theory and by the entropy constraint, the optimal inter- and intra-component bit allocation strategies are proposed. In the implementation of scalar quantizers, we use the constrained-resolution coding to approximate the derived constrained-entropy coding. A practical coding scheme for DVQ is designed for the purpose of reducing the quantization error accumulation. The theoretical and practical quantization performance of DVQ is evaluated. Compared to the state-of-the-art GMM-based VQ and recently proposed beta mixture model (BMM) based VQ, DVQ performs better, with even fewer free parameters and lower computational cost
{"title":"Vector quantization of LSF parameters with a mixture of dirichlet distributions","authors":"Zhanyu Ma, A. Leijon, W. Kleijn","doi":"10.1109/TASL.2013.2238732","DOIUrl":"https://doi.org/10.1109/TASL.2013.2238732","url":null,"abstract":"Quantization of the linear predictive coding parameters is an important part in speech coding. Probability density function (PDF)-optimized vector quantization (VQ) has been previously shown to be more efficient than VQ based only on training data. For data with bounded support, some well-defined bounded-support distributions (e.g., the Dirichlet distribution) have been proven to outperform the conventional Gaussian mixture model (GMM), with the same number of free parameters required to describe the model. When exploiting both the boundary and the order properties of the line spectral frequency (LSF) parameters, the distribution of LSF differences LSF can be modelled with a Dirichlet mixture model (DMM). We propose a corresponding DMM based VQ. The elements in a Dirichlet vector variable are highly mutually correlated. Motivated by the Dirichlet vector variable's neutrality property, a practical non-linear transformation scheme for the Dirichlet vector variable can be obtained. Similar to the Karhunen-Loève transform for Gaussian variables, this non-linear transformation decomposes the Dirichlet vector variable into a set of independent beta-distributed variables. Using high rate quantization theory and by the entropy constraint, the optimal inter- and intra-component bit allocation strategies are proposed. In the implementation of scalar quantizers, we use the constrained-resolution coding to approximate the derived constrained-entropy coding. A practical coding scheme for DVQ is designed for the purpose of reducing the quantization error accumulation. The theoretical and practical quantization performance of DVQ is evaluated. Compared to the state-of-the-art GMM-based VQ and recently proposed beta mixture model (BMM) based VQ, DVQ performs better, with even fewer free parameters and lower computational cost","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2238732","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62885836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1109/TASL.2013.2260741
Benoit Fuentes, R. Badeau, G. Richard
Recently, new methods for smart decomposition of time-frequency representations of audio have been proposed in order to address the problem of automatic music transcription. However those techniques are not necessarily suitable for notes having variations of both pitch and spectral envelope over time. The HALCA (Harmonic Adaptive Latent Component Analysis) model presented in this article allows considering those two kinds of variations simultaneously. Each note in a constant-Q transform is locally modeled as a weighted sum of fixed narrowband harmonic spectra, spectrally convolved with some impulse that defines the pitch. All parameters are estimated by means of the expectation-maximization (EM) algorithm, in the framework of Probabilistic Latent Component Analysis. Interesting priors over the parameters are also introduced in order to help the EM algorithm converging towards a meaningful solution. We applied this model for automatic music transcription: the onset time, duration and pitch of each note in an audio file are inferred from the estimated parameters. The system has been evaluated on two different databases and obtains very promising results.
{"title":"Harmonic Adaptive Latent Component Analysis of Audio and Application to Music Transcription","authors":"Benoit Fuentes, R. Badeau, G. Richard","doi":"10.1109/TASL.2013.2260741","DOIUrl":"https://doi.org/10.1109/TASL.2013.2260741","url":null,"abstract":"Recently, new methods for smart decomposition of time-frequency representations of audio have been proposed in order to address the problem of automatic music transcription. However those techniques are not necessarily suitable for notes having variations of both pitch and spectral envelope over time. The HALCA (Harmonic Adaptive Latent Component Analysis) model presented in this article allows considering those two kinds of variations simultaneously. Each note in a constant-Q transform is locally modeled as a weighted sum of fixed narrowband harmonic spectra, spectrally convolved with some impulse that defines the pitch. All parameters are estimated by means of the expectation-maximization (EM) algorithm, in the framework of Probabilistic Latent Component Analysis. Interesting priors over the parameters are also introduced in order to help the EM algorithm converging towards a meaningful solution. We applied this model for automatic music transcription: the onset time, duration and pitch of each note in an audio file are inferred from the estimated parameters. The system has been evaluated on two different databases and obtains very promising results.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2260741","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1109/TASL.2013.2260743
I. Kodrasi, Stefan Goetze, S. Doclo
Acoustic multichannel equalization techniques such as the multiple-input/output inverse theorem (MINT), which aim to equalize the room impulse responses (RIRs) between the source and the microphone array, are known to be highly sensitive to RIR estimation errors. To increase robustness, it has been proposed to incorporate regularization in order to decrease the energy of the equalization filters. In addition, more robust partial multichannel equalization techniques such as relaxed multichannel least-squares (RMCLS) and channel shortening (CS) have recently been proposed. In this paper, we propose a partial multichannel equalization technique based on MINT (P-MINT) which aims to shorten the RIR. Furthermore, we investigate the effectiveness of incorporating regularization to further increase the robustness of P-MINT and the aforementioned partial multichannel equalization techniques, i.e., RMCLS and CS. In addition, we introduce an automatic non-intrusive procedure for determining the regularization parameter based on the L-curve. Simulation results using measured RIRs show that incorporating regularization in P-MINT yields a significant performance improvement in the presence of RIR estimation errors, whereas a smaller performance improvement is observed when incorporating regularization in RMCLS and CS. Furthermore, it is shown that the intrusively regularized P-MINT technique outperforms all other investigated intrusively regularized multichannel equalization techniques in terms of perceptual speech quality (PESQ). Finally, it is shown that the automatic non-intrusive regularization parameter in regularized P-MINT leads to a very similar performance as the intrusively determined optimal regularization parameter, making regularized P-MINT a robust, perceptually advantageous, and practically applicable multichannel equalization technique for speech dereverberation.
{"title":"Regularization for Partial Multichannel Equalization for Speech Dereverberation","authors":"I. Kodrasi, Stefan Goetze, S. Doclo","doi":"10.1109/TASL.2013.2260743","DOIUrl":"https://doi.org/10.1109/TASL.2013.2260743","url":null,"abstract":"Acoustic multichannel equalization techniques such as the multiple-input/output inverse theorem (MINT), which aim to equalize the room impulse responses (RIRs) between the source and the microphone array, are known to be highly sensitive to RIR estimation errors. To increase robustness, it has been proposed to incorporate regularization in order to decrease the energy of the equalization filters. In addition, more robust partial multichannel equalization techniques such as relaxed multichannel least-squares (RMCLS) and channel shortening (CS) have recently been proposed. In this paper, we propose a partial multichannel equalization technique based on MINT (P-MINT) which aims to shorten the RIR. Furthermore, we investigate the effectiveness of incorporating regularization to further increase the robustness of P-MINT and the aforementioned partial multichannel equalization techniques, i.e., RMCLS and CS. In addition, we introduce an automatic non-intrusive procedure for determining the regularization parameter based on the L-curve. Simulation results using measured RIRs show that incorporating regularization in P-MINT yields a significant performance improvement in the presence of RIR estimation errors, whereas a smaller performance improvement is observed when incorporating regularization in RMCLS and CS. Furthermore, it is shown that the intrusively regularized P-MINT technique outperforms all other investigated intrusively regularized multichannel equalization techniques in terms of perceptual speech quality (PESQ). Finally, it is shown that the automatic non-intrusive regularization parameter in regularized P-MINT leads to a very similar performance as the intrusively determined optimal regularization parameter, making regularized P-MINT a robust, perceptually advantageous, and practically applicable multichannel equalization technique for speech dereverberation.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2260743","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1109/TASL.2013.2258011
F. William, A. Sangwan, J. Hansen
In this study, a new algorithm for automatic accent evaluation of native and non-native speakers is presented. The proposed system consists of two main steps: alignment and scoring. In the alignment step, the speech utterance is processed using a Weighted Finite State Transducer (WFST) based technique to automatically estimate the pronunciation mismatches (substitutions, deletions, and insertions). Subsequently, in the scoring step, two scoring systems which utilize the pronunciation mismatches from the alignment phase are proposed: (i) a WFST-scoring system to measure the degree of accentedness on a scale from -1 (non-native like) to +1 (native like), and a (ii) Maximum Entropy (ME) based technique to assign perceptually motivated scores to pronunciation mismatches. The accent scores provided from the WFST-scoring system as well as the ME scoring system are termed as the WFST and P-WFST (perceptual WFST) accent scores, respectively. The proposed systems are evaluated on American English (AE) spoken by native and non-native (native speakers of Mandarin-Chinese) speakers from the CU-Accent corpus. A listener evaluation of 50 Native American English (N-AE) was employed to assist in validating the performance of the proposed accent assessment systems. The proposed P-WFST algorithm shows higher and more consistent correlation with human evaluated accent scores, when compared to the Goodness Of Pronunciation (GOP) measure. The proposed solution for accent classification and assessment based on WFST and P-WFST scores show that an effective advancement is possible which correlates well with human perception.
{"title":"Automatic Accent Assessment Using Phonetic Mismatch and Human Perception","authors":"F. William, A. Sangwan, J. Hansen","doi":"10.1109/TASL.2013.2258011","DOIUrl":"https://doi.org/10.1109/TASL.2013.2258011","url":null,"abstract":"In this study, a new algorithm for automatic accent evaluation of native and non-native speakers is presented. The proposed system consists of two main steps: alignment and scoring. In the alignment step, the speech utterance is processed using a Weighted Finite State Transducer (WFST) based technique to automatically estimate the pronunciation mismatches (substitutions, deletions, and insertions). Subsequently, in the scoring step, two scoring systems which utilize the pronunciation mismatches from the alignment phase are proposed: (i) a WFST-scoring system to measure the degree of accentedness on a scale from -1 (non-native like) to +1 (native like), and a (ii) Maximum Entropy (ME) based technique to assign perceptually motivated scores to pronunciation mismatches. The accent scores provided from the WFST-scoring system as well as the ME scoring system are termed as the WFST and P-WFST (perceptual WFST) accent scores, respectively. The proposed systems are evaluated on American English (AE) spoken by native and non-native (native speakers of Mandarin-Chinese) speakers from the CU-Accent corpus. A listener evaluation of 50 Native American English (N-AE) was employed to assist in validating the performance of the proposed accent assessment systems. The proposed P-WFST algorithm shows higher and more consistent correlation with human evaluated accent scores, when compared to the Goodness Of Pronunciation (GOP) measure. The proposed solution for accent classification and assessment based on WFST and P-WFST scores show that an effective advancement is possible which correlates well with human perception.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2258011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1109/TASL.2013.2260742
Jose Manuel Gil-Cacho, M. Signoretto, T. Waterschoot, M. Moonen, S. H. Jensen
Acoustic echo cancellation (AEC) is used in speech communication systems where the existence of echoes degrades the speech intelligibility. Standard approaches to AEC rely on the assumption that the echo path to be identified can be modeled by a linear filter. However, some elements introduce nonlinear distortion and must be modeled as nonlinear systems. Several nonlinear models have been used with more or less success. The kernel affine projection algorithm (KAPA) has been successfully applied to many areas in signal processing but not yet to nonlinear AEC (NLAEC). The contribution of this paper is three-fold: (1) to apply KAPA to the NLAEC problem, (2) to develop a sliding-window leaky KAPA (SWL-KAPA) that is well suited for NLAEC applications, and (3) to propose a kernel function, consisting of a weighted sum of a linear and a Gaussian kernel. In our experiment set-up, the proposed SWL-KAPA for NLAEC consistently outperforms the linear APA, resulting in up to 12 dB of improvement in ERLE at a computational cost that is only 4.6 times higher. Moreover, it is shown that the SWL-KAPA outperforms, by 4-6 dB, a Volterra-based NLAEC, which itself has a much higher 413 times computational cost than the linear APA.
{"title":"Nonlinear Acoustic Echo Cancellation Based on a Sliding-Window Leaky Kernel Affine Projection Algorithm","authors":"Jose Manuel Gil-Cacho, M. Signoretto, T. Waterschoot, M. Moonen, S. H. Jensen","doi":"10.1109/TASL.2013.2260742","DOIUrl":"https://doi.org/10.1109/TASL.2013.2260742","url":null,"abstract":"Acoustic echo cancellation (AEC) is used in speech communication systems where the existence of echoes degrades the speech intelligibility. Standard approaches to AEC rely on the assumption that the echo path to be identified can be modeled by a linear filter. However, some elements introduce nonlinear distortion and must be modeled as nonlinear systems. Several nonlinear models have been used with more or less success. The kernel affine projection algorithm (KAPA) has been successfully applied to many areas in signal processing but not yet to nonlinear AEC (NLAEC). The contribution of this paper is three-fold: (1) to apply KAPA to the NLAEC problem, (2) to develop a sliding-window leaky KAPA (SWL-KAPA) that is well suited for NLAEC applications, and (3) to propose a kernel function, consisting of a weighted sum of a linear and a Gaussian kernel. In our experiment set-up, the proposed SWL-KAPA for NLAEC consistently outperforms the linear APA, resulting in up to 12 dB of improvement in ERLE at a computational cost that is only 4.6 times higher. Moreover, it is shown that the SWL-KAPA outperforms, by 4-6 dB, a Volterra-based NLAEC, which itself has a much higher 413 times computational cost than the linear APA.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2260742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/TASL.2013.2260155
Eleftheria Georganti, T. May, S. Par, J. Mourjopoulos
A novel method for the estimation of the distance of a sound source from binaural speech signals is proposed. The method relies on several statistical features extracted from such signals and their binaural cues. Firstly, the standard deviation of the difference of the magnitude spectra of the left and right binaural signals is used as a feature for this method. In addition, an extended set of additional statistical features that can improve distance detection is extracted from an auditory front-end which models the peripheral processing of the human auditory system. The method incorporates the above features into two classification frameworks based on Gaussian mixture models and Support Vector Machines and the relative merits of those frameworks are evaluated. The proposed method achieves distance detection when tested in various acoustical environments and performs well in unknown environments. Its performance is also compared to an existing binaural distance detection method.
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