Dual microphone noise PSD estimation for mobile phones in hands-free position exploiting the coherence and speech presence probability

Nelke, Christoph Matthias; Beaugeant, Christophe; Vary, Peter

doi:10.1109/icassp.2013.6639076

Cited by 20 publications

(19 citation statements)

References 11 publications

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“…Since the speech leakage power becomes a relevant factor w.r.t noise power when the noise level is decreased, the LogErr of noise power estimation in BCIBd is increased with SNR. The other point which should be mentioned is the relatively high estimation error by BCIBo owing to the filtering error of BCI in (9). This filtering effect is corrected by the correction gain afterwards.…”

Section: Noise Psd Estimationmentioning

confidence: 96%

“…In [7] the previously published dual-channel noise PSD estimator based on (cross-) PSD [8] has been modified employing the coherence properties of the noise signal. The authors of [9] combined the single and dual microphone noise estimators. The speech and noise coherence was estimated frame-by-frame in order to estimate the noise power spectrum.…”

Section: Introductionmentioning

confidence: 99%

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Fast noise PSD estimation based on blind channel identification

Azarpour

Enzner

2014

2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC)

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This paper introduces a new binaural noise estimator based on the target cancellation technique. The left and right source-tomicrophone transfer functions (channels) are blindly estimated by means of the constrained least-mean-square algorithm, minimizing the cross-relation error between left and right microphone signals. This blind channel identification (BCI) thus implies a blocking of the target signal and a biased noise estimation in the error signal. The related noise power is then corrected using the estimated channels. The performance of the proposed algorithm is investigated in comparison to different single and dual channel noise power estimators. The investigations show that the proposed algorithm is capable of estimating and tracking the noise power fast and accurately. The suitability of the noise power spectral density (PSD) estimator is finally confirmed within a speech enhancement framework.

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Section: Noise Psd Estimationmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Fast noise PSD estimation based on blind channel identification

Azarpour

Enzner

2014

2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC)

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“…2 was evaluated in comparison to the previous methods including the single-channel SPP-based noise PSD estimator (SC-SPP) [5], the improved dual-channel noise PSD estimator (ImNPSD) [17], the dual-channel noise PSD estimator (DC-NPSD) [6], and the bias-corrected blocking method of the interaural transfer function (BB-ITF) [15]. The proposed noise PSD estimator in (23) is referred to as "Prop" in all plots.…”

Section: Noise Estimation Evaluationmentioning

confidence: 99%

“…Since the de-noising gains of the speech enhancement algorithm are fundamentally determined by the noise power spectral density (PSD), it is important to obtain an accurate noise PSD estimate. Therefore, extensive research has been conducted on noise PSD estimations using a singlemicrophone system [2][3][4][5]; however, they often exhibit limited performances in situations with non-stationary noise or a low signal-to-noise (SNR) ratio [6].…”

Section: Introductionmentioning

confidence: 99%

Robust noise power spectral density estimation for binaural speech enhancement in time-varying diffuse noise field

Baek

Park

2017

J AUDIO SPEECH MUSIC PROC.

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In speech enhancement, noise power spectral density (PSD) estimation plays a key role in determining appropriate de-nosing gains. In this paper, we propose a robust noise PSD estimator for binaural speech enhancement in timevarying noise environments. First, it is shown that the noise PSD can be numerically obtained using an eigenvalue of the input covariance matrix. A simplified estimator is then derived through an approximation process, so that the noise PSD is expressed as a combination of the second eigenvalue of the input covariance matrix, the noise coherence, and the interaural phase difference (IPD) of the input signal. Later, to enhance the accuracy of the noise PSD estimate in time-varying noise environments, an eigenvalue compensation scheme is presented, in which two eigenvalues obtained in noise-dominant regions are combined using a weighting parameter based on the speech presence probability (SPP). Compared with the previous prediction filter-based approach, the proposed method requires neither causality delays nor explicit estimation of the prediction errors. Finally, the proposed noise PSD estimator is applied to a binaural speech enhancement system, and its performance is evaluated through computer simulations. The simulation results show that the proposed noise PSD estimator yields accurate noise PSD regardless of the direction of the target speech signal. Therefore, slightly better performance in quality and intelligibility can be obtained than that with conventional algorithms.

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“…For measurement setups with two microphones, many algorithms for speech enhancement exploit the correlation between the two microphone signals ( [17], [18], [19]). As frequency dependent correlation measure the magnitude squared coherence (MSC) can be used…”

Section: Coherence Analysismentioning

confidence: 99%

Measurement, analysis and simulation of wind noise signals for mobile communication devices

Nelke

Vary

2014

2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC)

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In this contribution, we study the characteristics of sound generated by wind and a signal model for the synthesis of wind noise signals is derived. An analysis of the statistics of wind noise recorded in a laboratory setup is carried out with respect to the spectral and temporal properties of the signals. In particular, an autoregresive model is developed for the spectral shape description and the temporal statistics are modeled by a Markov chain. These two components are combined in a model which synthesizes reproducible artificial wind noise signals. Furthermore, a database of measured wind noise signals is provided. The aim of this model and the measured audio data is to provide wind signals for the evaluation of speech enhancement and noise reduction systems.Index Terms-Wind noise analysis, wind noise simulation, communication quality assessment, wind noise database

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Dual microphone noise PSD estimation for mobile phones in hands-free position exploiting the coherence and speech presence probability

Cited by 20 publications

References 11 publications

Fast noise PSD estimation based on blind channel identification

Fast noise PSD estimation based on blind channel identification

Robust noise power spectral density estimation for binaural speech enhancement in time-varying diffuse noise field

Measurement, analysis and simulation of wind noise signals for mobile communication devices

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