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Nonstationary Noise psd matrix estimation for Multichannel Blind Speech Extraction

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2017年第11期25卷 2223-2236页

作者： Taseska, Maja Habets, Emanuel A. P. Univ Erlangen Nurnberg Int Audio Labs Erlangen D-91058 Erlangen Germany

Noise power spectral density (psd) matrix estimation is one of the most important components of a multichannel blind speech extraction framework, as it largely determines the amount of residual noise at the output of a spatial filter. Optimality of well-known spatial filters, such as the multichannel Wiener filter, is only ensured if the psd matrices of the noise and the desired speech are accurately estimated. In practical situations, where the noise is non-stationary, temporal averaging over time frames where the desired signal is inactive does not provide sufficiently fast tracking of the noise psd matrix, resulting in high residual noise at the spatial filter output. Therefore, approaches that estimate the psd matrices using narrowband signal detection have been proposed. Following the well-known single-and multichannel minima-controlled recursive averaging (MCRA) approaches, in this paper, we focus on narrowband speech presence probability-based noise psd matrix estimators, which are suitable for blind scenarios where the location and the propagation vector of the desired speech source are unknown. The main contributions of the paper are a maximum likelihood interpretation of the multichannel MCRA, and a coherent-to-diffuse ratio-based a priori speech absence probability (SAP) estimator. The latter is a key parameter that determines the accuracy of the noise psd matrix estimates in nonstationary scenarios. In this paper, we confirm the importance of the a priori SAP and show that its control is crucial for source extraction in nonstationary environments.

关键词： Blind source extraction psd matrix estimation spatial filtering

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Blind Source Separation of Moving Sources Using Sparsity-Based Source Detection and Tracking

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2018年第3期26卷 657-670页

作者： Taseska, Maja Habets, Emanuel A. P. Int Audio Labs Erlangen D-91058 Erlangen Germany

Sparsity-based blind source separation (BSS) algorithms in the short time-frequency (TF) domain have received a lot of attention due to their versatility and noise reduction capabilities. In most of these algorithms, the estimation of the BSS filters relies on the accurate association of each time-frequency bin to the dominant source at that bin. The TF bin associations are then used to estimate the statistics of the source signals, and BSS is achieved by optimal spatial filters computed using the estimated statistics. The main objective of this paper is to apply such a framework to scenarios with an unknown number of moving sources. While state-of-the-art approaches employ online clustering algorithms to solve the problem for moving sources, we propose an approximate Bayesian tracker and perform the association of each TF bin to the dominant source using the tracker's measurement-to-source association probabilities. Therefore, the choice of the underlying narrowband models and measurements for the tracker as well as the resulting tracking algorithm constitute the main contributions of this paper. The TF bin associations obtained from the tracker are then used to estimate the statistics of the source signals. The performance of the resulting BSS filters is compared to the performance of state-of-the-art sparsity-based and independent vector analysis-based BSS algorithms. Our proposed approach targets scenarios with at least two spatially separated microphone arrays, with known microphone positions and relative orientations. The framework also allows for efficient management of a time-varying number of sources.

关键词： Spatial filtering source separation acoustic source tracking psd matrix estimation

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Informed Spatial Filtering for Sound Extraction Using Distributed Microphone Arrays

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2014年第7期22卷 1195-1207页

作者： Taseska, Maja Habets, Emanuel A. P. Univ Erlangen Nurnberg Int Audio Labs Erlangen D-91058 Erlangen Germany Int Audio Labs Erlangen D-91058 Erlangen Germany

Hands-free acquisition of speech is required in many human-machine interfaces and communication systems. The signals received by integrated microphones contain a desired speech signal, spatially coherent interfering signals, and background noise. In order to enhance the desired speech signal, state-of-the-art techniques apply data-dependent spatial filters which require the second order statistics (SOS) of the desired signal, the interfering signals and the background noise. As the number of sources and the reverberation time increase, the estimation accuracy of the SOS deteriorates, often resulting in insufficient noise and interference reduction. In this paper, a signal extraction framework with distributed microphone arrays is developed. An expectation maximization (EM)-based algorithm detects the number of coherent speech sources and estimates source clusters using time-frequency (TF) bin-wise position estimates. Subsequently, the second order statistics (SOS) are estimated using bin-wise speech presence probability (SPP) and a source probability for each source. Finally, a desired source is extracted using a minimum variance distortionless response (MVDR) filter, a multichannel Wiener filter (MWF) and a parametric multichannel Wiener filter (PMWF). The same framework can be employed for source separation, where a spatial filter is computed for each source considering the remaining sources as interferers. Evaluation using simulated and measured data demonstrates the effectiveness of the framework in estimating the number of sources, clustering, signal enhancement, and source separation.

关键词： Distributed arrays EM algorithm psd matrix estimation source extraction spatial filtering

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Spotforming: Spatial Filtering With Distributed Arrays for Position-Selective Sound Acquisition

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2016年第7期24卷 1291-1304页

作者： Taseska, Maja Habets, Emanuel A. P. Univ Erlangen Nurnberg Int Audio Labs Erlangen D-91058 Erlangen Germany Fraunhofer IIS D-91058 Erlangen Germany

Hands-free capture of speech often requires extraction of sources from a certain spot of interest (SOI), while reducing interferers and background noise. Although state-of-theart spatial filters are fully data-dependent and computed using the power spectral density (psd) matrices of the desired and the undesired signals, the existing solutions to extract sources from a SOI are only partially data-dependent. Estimating the timevarying psd matrices from the data is a challenging problem, especially in dynamic and quickly time-varying acoustic scenes. Hence, the spot signal statistics are often pre-computed based on a near-field propagation model, resulting in suboptimal filters. In this work, we propose a fully data-dependent spatial filtering framework for extraction of speech signals that originate from a SOI. To achieve position-based spatial selectivity, distributed arrays are used, which offer larger spatial diversity compared to arrays of closely spaced microphones. The psd matrices of the desired and the undesired signals are updated at each time-frequency bin by using a minimum Bayes risk detector that is based on a probabilistic model of narrowband position estimates. The proposed framework is applicable in challenging multitalk situations, without requiring any prior information, except the geometry, location, and orientation of the arrays.

关键词： Source extraction distributed arrays spatial filtering psd matrix estimation signal detection

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DOA-informed source extraction in the presence of competing talkers and background noise

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2017年第1期2017卷 1页

作者： Taseska, Maja Habets, Emanuel A. P. Int Audio Labs Erlangen Erlangen Germany

A desired speech signal in hands-free communication systems is often degraded by noise and interfering speech. Even though the number and locations of the interferers are often unknown in practice, it is justified to assume in certain applications that the direction-of-arrival (DOA) of the desired source is approximately known. Using the known DOA, fixed spatial filters such as the delay-and-sum beamformer can be steered to extract the desired source. However, it is well-known that fixed data-independent spatial filters do not provide sufficient reduction of directional interferers. Instead, the DOA information can be used to estimate the statistics of the desired and the undesired signals and to compute optimal data-dependent spatial filters. One way the DOA is exploited for optimal spatial filtering in the literature, is by designing DOA-based narrowband detectors to determine whether a desired or an undesired signal is dominant at each time-frequency (TF) bin. Subsequently, the statistics of the desired and the undesired signals can be estimated during the TF bins where the respective signal is dominant. In a similar manner, a Gaussian signal model-based detector which does not incorporate DOA information has been used in scenarios where the undesired signal consists of stationary background noise. However, when the undesired signal is non-stationary, resulting for example from interfering speakers, such a Gaussian signal model-based detector is unable to robustly distinguish desired from undesired speech. To this end, we propose a DOA model-based detector to determine the dominant source at each TF bin and estimate the desired and undesired signal statistics. We demonstrate that data-dependent spatial filters that use the statistics estimated by the proposed framework achieve very good undesired signal reduction, even when using only three microphones.

关键词： Spatial filtering Speech enhancement psd matrix estimation RTF estimation Signal detection

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MMSE-BASED SOURCE EXTRACTION USING POSITION-BASED POSTERIOR PROBABILITIES

MMSE-BASED SOURCE EXTRACTION USING POSITION-BASED POSTERIOR ...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Taseska, Maja Habets, Emanuel A. P. Int Audio Labs Erlangen D-91058 Erlangen Germany

ISBN: (纸本)9781479903566

A scenario with multiple talkers and additive background noise is considered, where some talkers are active simultaneously and the activity of the talkers changes with time. We propose an MMSE-based method to blindly extract any talker using bin-wise position estimates obtained from distributed microphone arrays. In order to distinguish between different talkers, the position estimates are clustered using the expectation maximization algorithm. The resulting posterior probabilities allow to estimate the psd matrices of the talkers and compute an MMSE-optimal linear filter for extracting each talker. We evaluate the performance of the proposed method in terms of noise and interference reduction and distortion of the desired speech signal at the output of a multichannel Wiener filter.

关键词： speech separation psd matrix estimation distributed arrays expectation maximization

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AN ONLINE EM ALGORITHM FOR SOURCE EXTRACTION USING DISTRIBUTED MICROPHONE ARRAYS

AN ONLINE EM ALGORITHM FOR SOURCE EXTRACTION USING DISTRIBUT...

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21st European Signal Processing Conference (EUSIPCO)

作者： Taseska, Maja Habets, Emanuel A. P. Int Audio Labs Erlangen D-91058 Erlangen Germany

ISBN: (纸本)9781479936878

Expectation maximization (EM)-based clustering is applied in many recent multichannel source extraction techniques. The estimated model parameters are used to compute time-frequency masks, or estimate second order statistics (SOS) of the source signals. However, in applications with moving sources where the model parameters are time-varying, the batch EM algorithm is inapplicable. We propose an online EM-based clustering of position estimates, where the model parameters are estimated adaptively. A direct-to-diffuse ratio-based speech presence probability is used to detect noisy observations and reduce diffuse and spatially incoherent noise. The desired source signal is extracted by a multichannel Wiener filter computed using SOS estimated from the time-varying model parameters. We show that the signal of a moving source can be extracted, while reducing moving interferers and background noise.

关键词： expectation maximization online learning psd matrix estimation distributed arrays

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SPOTFORMING USING DISTRIBUTED MICROPHONE ARRAYS

SPOTFORMING USING DISTRIBUTED MICROPHONE ARRAYS

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14th IEEE Workshop on Applications of Signal Processing to AudNew Paltzio and Acoustics (WASPAA)

作者： Taseska, Maja Habets, Emanuel A. P. Int Audio Labs Erlangen D-91058 Erlangen Germany

ISBN: (纸本)9781479909728

Extracting sounds that originate from a specific location, while reducing noise and interferers is required in many hands-free communications systems. We propose a spotforming approach that uses distributed microphone arrays and aims at extracting sounds that originate from a pre-defined spot of interest (SOI), while reducing background noise and sounds that originate from outside the SOI. The spotformer is realized as a linear spatial filter, which is based on the signal statistics of sounds from the SOI, the signal statistics of sounds outside the SOI and the background noise signal statistics. The required signal statistics are estimated from the microphone signals, while taking into account the uncertainty in the location estimates of the desired and the interfering sound sources. The applicability of the method is demonstrated by simulations and the quality of the extracted signal is evaluated in different scenarios.

关键词： distributed arrays psd matrix estimation source extraction spatial filtering

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SPEECH ENHANCEMENT WITH A LOW-COMPLEXITY ONLINE SOURCE NUMBER ESTIMATOR USING DISTRIBUTED ARRAYS 22

SPEECH ENHANCEMENT WITH A LOW-COMPLEXITY ONLINE SOURCE NUMBE...

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22nd European Signal Processing Conference (EUSIPCO)

作者： Taseska, Maja Khan, Affan Hasan Habets, Emanuel A. P. Int Audio Labs Erlangen Wolfsmantel 33 D-91058 Erlangen Germany

ISBN: (纸本)9780992862619

Enhancement of a desired speech signal in the presence of background noise and interferers is required in various modern communication systems. Existing multichannel techniques often require that the number of sources and their locations are known in advance, which makes them inapplicable in many practical situations. We propose a framework which uses the microphones of distributed arrays to enhance a desired speech signal by reducing background noise and an initially unknown number of interferers. The desired signal is extracted by a minimum variance distortionless response filter in dynamic scenarios where the number of active interferers is time-varying. An efficient, geometry-based approach that estimates the number of active interferers and their locations online is proposed. The overall performance is compared to the one of a geometry-based probabilistic framework for source extraction, recently proposed by the authors.

关键词： Source extraction psd matrix estimation distributed arrays number of sources

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Minimum Bayes risk signal detection for speech enhancement based on a narrowband DOA model 40

Minimum Bayes risk signal detection for speech enhancement b...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Taseska, Maja Habets, Emanuël A.P. International Audio Laboratories Erlangen Am Wolfsmantel 33 Erlangen Germany

ISBN: (纸本)9781467369978

A desired speech signal in hands-free communication systems is often degraded by background noise and interferers. Data-dependent spatial filters for desired speech extraction depend on the power spectral density (psd) matrices of the desired and the undesired signals, which are commonly estimated recursively using a signal model-based speech presence probability (SPP). The SPP and the psd matrix estimates are only accurate, if the statistics of the undesired signals vary more slowly compared to the desired signal. In practical situations with competing talkers, this assumption is violated. To estimate the psd matrices of highly non-stationary signals, we propose a minimum Bayes risk detector based on a model for the narrowband direction-of-arrival estimates. The performance of the proposed detector and the objective quality of the extracted desired speech are evaluated using simulated and measured data. © 2015 IEEE.

关键词： psd matrix estimation signal detection Speech enhancement

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