651 research outputs found

    Multi-channel dereverberation for speech intelligibility improvement in hearing aid applications

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    RTF-Based Binaural MVDR Beamformer Exploiting an External Microphone in a Diffuse Noise Field

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    Besides suppressing all undesired sound sources, an important objective of a binaural noise reduction algorithm for hearing devices is the preservation of the binaural cues, aiming at preserving the spatial perception of the acoustic scene. A well-known binaural noise reduction algorithm is the binaural minimum variance distortionless response beamformer, which can be steered using the relative transfer function (RTF) vector of the desired source, relating the acoustic transfer functions between the desired source and all microphones to a reference microphone. In this paper, we propose a computationally efficient method to estimate the RTF vector in a diffuse noise field, requiring an additional microphone that is spatially separated from the head-mounted microphones. Assuming that the spatial coherence between the noise components in the head-mounted microphone signals and the additional microphone signal is zero, we show that an unbiased estimate of the RTF vector can be obtained. Based on real-world recordings, experimental results for several reverberation times show that the proposed RTF estimator outperforms the widely used RTF estimator based on covariance whitening and a simple biased RTF estimator in terms of noise reduction and binaural cue preservation performance.Comment: Accepted at ITG Conference on Speech Communication 201

    OBJECTIVE AND SUBJECTIVE EVALUATION OF DEREVERBERATION ALGORITHMS

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    Reverberation significantly impacts the quality and intelligibility of speech. Several dereverberation algorithms have been proposed in the literature to combat this problem. A majority of these algorithms utilize a single channel and are developed for monaural applications, and as such do not preserve the cues necessary for sound localization. This thesis describes a blind two-channel dereverberation technique that improves the quality of speech corrupted by reverberation while preserving cues that affect localization. The method is based by combining a short term (2ms) and long term (20ms) weighting function of the linear prediction (LP) residual of the input signal. The developed and other dereverberation algorithms are evaluated objectively and subjectively in terms of sound quality and localization accuracy. The binaural adaptation provides a significant increase in sound quality while removing the loss in localization ability found in the bilateral implementation

    Learning-Based Reference-Free Speech Quality Assessment for Normal Hearing and Hearing Impaired Applications

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    Accurate speech quality measures are highly attractive and beneficial in the design, fine-tuning, and benchmarking of speech processing algorithms, devices, and communication systems. Switching from narrowband telecommunication to wideband telephony is a change within the telecommunication industry which provides users with better speech quality experience but introduces a number of challenges in speech processing. Noise is the most common distortion on audio signals and as a result there have been a lot of studies on developing high performance noise reduction algorithms. Assistive hearing devices are designed to decrease communication difficulties for people with loss of hearing. As the algorithms within these devices become more advanced, it becomes increasingly crucial to develop accurate and robust quality metrics to assess their performance. Objective speech quality measurements are more attractive compared to subjective assessments as they are cost-effective and subjective variability is eliminated. Although there has been extensive research on objective speech quality evaluation for narrowband speech, those methods are unsuitable for wideband telephony. In the case of hearing-impaired applications, objective quality assessment is challenging as it has to be capable of distinguishing between desired modifications which make signals audible and undesired artifacts. In this thesis a model is proposed that allows extracting two sets of features from the distorted signal only. This approach which is called reference-free (nonintrusive) assessment is attractive as it does not need access to the reference signal. Although this benefit makes nonintrusive assessments suitable for real-time applications, more features need to be extracted and smartly combined to provide comparable accuracy as intrusive metrics. Two feature vectors are proposed to extract information from distorted signals and their performance is examined in three studies. In the first study, both feature vectors are trained on various portions of a noise reduction database for normal hearing applications. In the second study, the same investigation is performed on two sets of databases acquired through several hearing aids. Third study examined the generalizability of the proposed metrics on benchmarking four wireless remote microphones in a variety of environmental conditions. Machine learning techniques are deployed for training the models in the three studies. The studies show that one of the feature sets is robust when trained on different portions of the data from different databases and it also provides good quality prediction accuracy for both normal hearing and hearing-impaired applications

    Sound Source Separation

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    This is the author's accepted pre-print of the article, first published as G. Evangelista, S. Marchand, M. D. Plumbley and E. Vincent. Sound source separation. In U. Zölzer (ed.), DAFX: Digital Audio Effects, 2nd edition, Chapter 14, pp. 551-588. John Wiley & Sons, March 2011. ISBN 9781119991298. DOI: 10.1002/9781119991298.ch14file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.26file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.2

    Informed Sound Source Localization for Hearing Aid Applications

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    Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling

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    We consider the problem of separating speech sources captured by multiple spatially separated devices, each of which has multiple microphones and samples its signals at a slightly different rate. Most asynchronous array processing methods rely on sample rate offset estimation and resampling, but these offsets can be difficult to estimate if the sources or microphones are moving. We propose a source separation method that does not require offset estimation or signal resampling. Instead, we divide the distributed array into several synchronous subarrays. All arrays are used jointly to estimate the time-varying signal statistics, and those statistics are used to design separate time-varying spatial filters in each array. We demonstrate the method for speech mixtures recorded on both stationary and moving microphone arrays.Comment: To appear at the International Workshop on Acoustic Signal Enhancement (IWAENC 2018
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