In speech communication systems, such as voice-controlled systems, hands-free mobile telephones, and hearing aids, the received microphone signals are degraded by room reverberation, background noise, and other interferences This signal degradation may lead to total unintelligibility of the speech and decreases the performance of automatic speech recognition systems In the context of this work reverberation is the process of multi-path propagation of an acoustic sound from its source to one or more microphones The received microphone signal generally consists of a direct sound, reflections that arrive shortly after the direct sound (commonly called early reverberation), and reflections that arrive after the early reverberation (commonly called late reverberation) Reverberant speech can be described as sounding distant with noticeable echo and colouration These detrimental perceptual effects are primarily caused by late reverberation, and generally increase with increasing distance between the source and microphone Conversely, early reverberations tend to improve the intelligibility of speech In combination with the direct sound it is sometimes referred to as the early speech component Reduction of the detrimental effects of reflections is evidently of considerable practical importance, and is the focus of this dissertation More specifically the dissertation deals with dereverberation techniques, ie, signal processing techniques to reduce the detrimental effects of reflections In the dissertation, novel single- and multimicrophone speech dereverberation algorithms are developed that aim at the suppression of late reverberation, ie, at estimation of the early speech component This is done via so-called spectral enhancement techniques that require a specific measure of the late reverberant signal This measure, called spectral variance, can be estimated directly from the received (possibly noisy) reverberant signal(s) using a statistical reverberation model and a limited amount of a priori knowledge about the acoustic channel(s) between the source and the microphone(s) In our work an existing single-channel statistical reverberation model serves as a starting point The model is characterized by one parameter that depends on the acoustic characteristics of the environment We show that the spectral variance estimator that is based on this model, can only be used when the source-microphone distance is larger than the so-called critical distance This is, crudely speaking, the distance where the direct sound power is equal to the total reflective power A generalization of the statistical reverberation model in which the direct sound is incorporated is developed This model requires one additional parameter that is related to the ratio between the direct sound energy and the sound energy of all reflections The generalized model is used to derive a novel spectral variance estimator When the novel estimator is used for dereverberation rather than the existing estimator, and the source-microphone distance is smaller than the critical distance, the dereverberation performance is significantly increased Single-microphone systems only exploit the temporal and spectral diversity of the received signal Reverberation, of course, also induces spatial diversity To additionally exploit this diversity, multiple microphones must be used, and their outputs must be combined by a suitable spatial processor such as the so-called delay and sum beamformer It is not a priori evident whether spectral enhancement is best done before or after the spatial processor For this reason we investigate both possibilities, as well as a merge of the spatial processor and the spectral enhancement technique An advantage of the latter option is that the spectral variance estimator can be further improved Our experiments show that the use of multiple microphones affords a significant improvement of the perceptual speech quality The applicability of the theory developed in this dissertation is demonstrated using a hands-free communication system Since hands-free systems are often used in a noisy and reverberant environment, the received microphone signal does not only contain the desired signal but also interferences such as room reverberation that is caused by the desired source, background noise, and a far-end echo signal that results from a sound that is produced by the loudspeaker Usually an acoustic echo canceller is used to cancel the far-end echo Additionally a post-processor is used to suppress background noise and residual echo, ie, echo which could not be cancelled by the echo canceller In this work a novel structure and post-processor for an acoustic echo canceller are developed The post-processor suppresses late reverberation caused by the desired source, residual echo, and background noise The late reverberation and late residual echo are estimated using the generalized statistical reverberation model Experimental results convincingly demonstrate the benefits of the proposed system for suppressing late reverberation, residual echo and background noise The proposed structure and post-processor have a low computational complexity, a highly modular structure, can be seamlessly integrated into existing hands-free communication systems, and affords a significant increase of the listening comfort and speech intelligibility

/pdf/single-and-multi-microphone-speech-dereverberation-using-81pl8h93qv.pdf

Single- and multi-microphone speech dereverberation using spectral enhancement

In the context of microphone arrays, the term post-filtering denotes the post-processing of the array output by a single-channel noise suppression filter. A theoretical analysis shows that Wiener post-filtering of the output of an optimum distortionless beamformer provides a minimum mean squared error solution. We examine published methods for post-filter estimation and develop a new algorithm. A simulation system is presented to compare the performance of the discussed algorithms.

Post-Filtering Techniques

Communication systems are described, including both portable handset and headset devices, which use a number of microphone configurations to receive acoustic signals of an environment. The microphone configurations include, for example, a two-microphone array including two unidirectional microphones, and a two-microphone array including one unidirectional microphone and one omnidirectional microphone. The communication systems also include Voice Activity Detection (VAD) devices to provide information of human voicing activity. Components of the communications systems receive the acoustic signals and voice activity signals and, in response, automatically generate control signals from data of the voice activity signals. Components of the communication systems use the control signals to automatically select a denoising method appropriate to data of frequency subbands of the acoustic signals. The selected denoising method is applied to the acoustic signals to generate denoised acoustic signals when the acoustic signal includes speech (101) and noise (102).

Microphone and voice activity detection (vad) configurations for use with communication systems

In this contribution, a dual-channel input-output speech enhancement system is introduced. The proposed algorithm is an adaptation of the well-known superdirective beamformer including postfiltering to the binaural application. In contrast to conventional beamformer processing, the proposed system outputs enhanced stereo signals while preserving the important interaural amplitude and phase differences of the original signal. Instrumental performance evaluations in a real environment with multiple speech sources indicate that the proposed computational efficient spectral weighting system can achieve significant attenuation of speech interferers while maintaining a high speech quality of the target signal.

/pdf/dual-channel-speech-enhancement-by-superdirective-2gwd3jtgjh.pdf

Dual-channel speech enhancement by superdirective beamforming

A system and method for processing an acoustic input signal and providing at least one output acoustic signal to a user of a hearing-aid system. The hearing-aid system includes first and second channels with one of the channels having an adaptive delay. The first channel includes a directional unit for receiving the acoustic input signal and providing a directional signal; a correlative unit for receiving the directional signal and providing a noise reduced signal by utilizing correlative measures for identifying a speech signal of interest in the directional signal; and, a compensator for receiving the noise reduced signal and providing a compensated signal for compensating for a hearing loss of the user.

Binaural adaptive hearing aid

Speech enhancement using sub-band adaptive Griffiths--Jim signal processing

Speech enhancement using sub-band intermittent adaption

A new self-tuning deconvolution filter/smoother is presented. The algorithm runs in real time on a TMS32010 microprocessor. The filter/smoother employs a moving average of the innovations sequence to estimate the required signal. An application to speech processing and to the channel equalization problem is described.

Douglas R. Campbell

Papers

Speech enhancement using sub-band adaptive Griffiths--Jim signal processing

Speech enhancement using sub-band intermittent adaption

Real-time self-tuning deconvolution filter and smoother†

Intelligibility improvements obtained by an enhancement method applied to speech corrupted by noise and reverberation

Binaural sub-band adaptive speech enhancement using artifical neural networks