Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms Article Swipe

MAKE IMPACT

Tomohiro Nakatani , Rintaro Ikeshita , Keisuke Kinoshita , Hiroshi Sawada , Naoyuki Kamo , Shoko Araki · YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2111.10574

This paper develops a framework that can perform denoising, dereverberation, and source separation accurately by using a relatively small number of microphones. It has been empirically confirmed that Independent Vector Analysis (IVA) can blindly separate N sources from their sound mixture even with diffuse noise when a sufficiently large number (=M) of microphones are available (i.e., M>>N). However, the estimation accuracy seriously degrades as the number of microphones, or more specifically M-N (>=0), decreases. To overcome this limitation of IVA, we propose switching IVA (swIVA) in this paper. With swIVA, time frames of an observed signal with time-varying characteristics are clustered into several groups, each of which can be well handled by IVA using a small number of microphones, and thus accurate estimation can be achieved by applying IVA individually to each of the groups. Conventionally, a switching mechanism was introduced into a beamformer; however, no blind source separation algorithms with a switching mechanism have been successfully developed until this paper. In order to incorporate dereverberation capability, this paper further extends swIVA to blind Convolutional beamforming algorithm (swCIVA). It integrates swIVA and switching Weighted Prediction Error-based dereverberation (swWPE) in a jointly optimal way. We show that both swIVA and swCIVA can be optimized effectively based on blind signal processing, and that their performance can be further improved using a spatial guide for the initialization. Experiments show that both proposed methods largely outperform conventional IVA and its Convolutional beamforming extension (CIVA) in terms of objective signal quality and automatic speech recognition scores when using a relatively small number of microphones.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2111.10574

PDF

https://arxiv.org/pdf/2111.10574

OA Status

green

Related Works

10

OpenAlex ID

https://openalex.org/W4299704422

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W4299704422

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.48550/arxiv.2111.10574

Digital Object Identifier

Title

Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms

Work title

Type

preprint

OpenAlex work type

Language

en

Primary language

Publication year

2021

Year of publication

Publication date

2021-11-20

Full publication date if available

Authors

Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita, Hiroshi Sawada, Naoyuki Kamo, Shoko Araki

List of authors in order

Landing page

https://arxiv.org/abs/2111.10574

Publisher landing page

PDF URL

https://arxiv.org/pdf/2111.10574

Direct link to full text PDF

Open access

Yes

Whether a free full text is available

OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/2111.10574

Direct OA link when available

Concepts

Computer science, Beamforming, Algorithm, Initialization, Blind signal separation, Speech recognition, Extension (predicate logic), SIGNAL (programming language), Reverberation, Noise (video), Pattern recognition (psychology), Artificial intelligence, Telecommunications, Engineering, Image (mathematics), Programming language, Electrical engineering, Channel (broadcasting)

Top concepts (fields/topics) attached by OpenAlex

Cited by

0

Total citation count in OpenAlex

Related works (count)

10

Other works algorithmically related by OpenAlex

Full payload