NDLI: A framework for speech enhancement with ad hoc microphone arrays

Content Provider	ACM Digital Library
Author	Jensen, Jesper Rindom Benesty, Jacob Christensen, Mads Græsbøll Tavakoli, Vincent Mohammad
Copyright Year	2016
Abstract	Speech enhancement is vital for improved listening practices. Ad hoc microphone arrays are promising assets for this purpose. Most well-established enhancement techniques with conventional arrays can be adapted into ad hoc scenarios. Despite recent efforts to introduce various ad hoc speech enhancement apparatus, a common framework for integration of conventional methods into this new scheme is still missing. This paper establishes such an abstraction based on inter and intra subarray speech coherencies. Along with measures for signal quality at the input of subarrays, a measure of coherency is proposed both for subarray selection in local enhancement approaches, and also for selecting a proper global reference when more than one subarray are used. Proposed methods within this framework are evaluated with regard to quantitative and qualitative measures, including array gains, the speech distortion ratio, the PESQ measure, and the STOI intelligibility measure. Major findings in this work are the observed changes in the superiority of different methods for certain conditions. When perceptual quality or intelligibility of the speech are the ultimate goals, there are turning points where the MVDR and the LCMV are superior to Wiener-based methods. Also, for certain scenarios, local approaches may be preferred to global ones.
Starting Page	1038
Ending Page	1051
Page Count	14
File Format	PDF
ISSN	23299290
e-ISSN	23299304
Volume Number	24
Issue Number	6
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-06-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ad hoc array Microphone array Multichannel Noise reduction Pseudo-coherence vector Speech enhancement
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

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