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Microphone Arrays Signal Processing Techniques and Applications
M.S. Brandstein,Darren Ward +1 more
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TLDR
This paper presents a meta-modelling architecture for microphone Array Processing that automates the very labor-intensive and therefore time-heavy and expensive process of manually shaping Microphone Arrays for Speech Input in Automobiles.Abstract:
I. Speech Enhancement.- 1 Constant Directivity Beamforming.- 2 Superdirective Microphone Arrays.- 3 Post-Filtering Techniques.- 4 Spatial Coherence Functions for Differential Microphones in Isotropic Noise Fields.- 5 Robust Adaptive Beamforming.- 6 GSVD-Based Optimal Filtering for Multi-Microphone Speech Enhancement.- 7 Explicit Speech Modeling for Microphone Array Speech Acquisition.- II. Source Localization.- 8 Robust Localization in Reverberant Rooms.- 9 Multi-Source Localization Strategies.- 10 Joint Audio-Video Signal Processing for Object Localization and Tracking.- III. Applications.- 11 Microphone-Array Hearing Aids.- 12 Small Microphone Arrays with Postfilters for Noise and Acoustic Echo Reduction.- 13 Acoustic Echo Cancellation for Beamforming Microphone Arrays.- 14 Optimal and Adaptive Microphone Arrays for Speech Input in Automobiles.- 15 Speech Recognition with Microphone Arrays.- 16 Blind Separation of Acoustic Signals.- IV. Open Problems and Future Directions.- 17 Future Directions for Microphone Arrays.- 18 Future Directions in Microphone Array Processing.read more
Citations
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Supervised Speech Separation Based on Deep Learning: An Overview
DeLiang Wang,Jitong Chen +1 more
TL;DR: A comprehensive overview of deep learning-based supervised speech separation can be found in this paper, where three main components of supervised separation are discussed: learning machines, training targets, and acoustic features.
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New insights into the noise reduction Wiener filter
TL;DR: This paper studies the quantitative performance behavior of the Wiener filter in the context of noise reduction and shows that in the single-channel case the a posteriori signal-to-noise ratio (SNR) is greater than or equal to the a priori SNR (defined before theWiener filter), indicating that the Wieners filter is always able to achieve noise reduction.
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A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation
TL;DR: This paper proposes to analyze a large number of established and recent techniques according to four transverse axes: 1) the acoustic impulse response model, 2) the spatial filter design criterion, 3) the parameter estimation algorithm, and 4) optional postfiltering.
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The Diverse Environments Multi-channel Acoustic Noise Database (DEMAND): A database of multichannel environmental noise recordings
TL;DR: DEMAND (Diverse Environments Multi-channel Acoustic Noise Database) is provided, providing a set of 16-channel noise files recorded in a variety of indoor and outdoor settings to encourage research into algorithms beyond the stereo setup.
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Particle filtering algorithms for tracking an acoustic source in a reverberant environment
TL;DR: A general framework for tracking an acoustic source using particle filters is formulated and four specific algorithms that fit within this framework are discussed, and results indicate that the proposed family of algorithms are able to accurately track a moving source in a moderately reverberant room.