{"title":"Linearly-Constrained Minimum-Variance Method for Spherical Microphone Arrays Based on Plane-Wave Decomposition of the Sound Field","authors":"Yotam Peled, B. Rafaely","doi":"10.1109/TASL.2013.2277939","DOIUrl":null,"url":null,"abstract":"Speech signals recorded in real environments may be corrupted by ambient noise and reverberation. Therefore, noise reduction and dereverberation algorithms for speech enhancement are typically employed in speech communication systems. Although microphone arrays are useful in reducing the effect of noise and reverberation, existing methods have limited success in significantly removing both reverberation and noise in real environments. This paper presents a method for noise reduction and dereverberation that overcomes some of the limitations of previous methods. The method uses a spherical microphone array to achieve plane-wave decomposition (PWD) of the sound field, based on direction-of-arrival (DOA) estimation of the desired signal and its reflections. A multi-channel linearly-constrained minimum-variance (LCMV) filter is introduced to achieve further noise reduction. The PWD beamformer achieves dereverberation while the LCMV filter reduces the uncorrelated noise with a controllable dereverberation constraint. In contrast to other methods, the proposed method employs DOA estimation, rather than room impulse response identification, to achieve dereverberation, and relative transfer function (RTF) estimation between the source reflections to achieve noise reduction while avoiding signal cancellation. The paper includes a simulation investigation and an experimental study, comparing the proposed method to currently available methods.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2277939","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Audio Speech and Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TASL.2013.2277939","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 28
Abstract
Speech signals recorded in real environments may be corrupted by ambient noise and reverberation. Therefore, noise reduction and dereverberation algorithms for speech enhancement are typically employed in speech communication systems. Although microphone arrays are useful in reducing the effect of noise and reverberation, existing methods have limited success in significantly removing both reverberation and noise in real environments. This paper presents a method for noise reduction and dereverberation that overcomes some of the limitations of previous methods. The method uses a spherical microphone array to achieve plane-wave decomposition (PWD) of the sound field, based on direction-of-arrival (DOA) estimation of the desired signal and its reflections. A multi-channel linearly-constrained minimum-variance (LCMV) filter is introduced to achieve further noise reduction. The PWD beamformer achieves dereverberation while the LCMV filter reduces the uncorrelated noise with a controllable dereverberation constraint. In contrast to other methods, the proposed method employs DOA estimation, rather than room impulse response identification, to achieve dereverberation, and relative transfer function (RTF) estimation between the source reflections to achieve noise reduction while avoiding signal cancellation. The paper includes a simulation investigation and an experimental study, comparing the proposed method to currently available methods.
期刊介绍:
The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.