Dear user, the application need JavaScript support. Please enable JavaScript in your browser.

You are browsing as a GUEST
Title of the item:

Robot Audition and Computational Auditory Scene Analysis

Title :
Robot Audition and Computational Auditory Scene Analysis
Authors :
Kazuhiro Nakadai
Hiroshi G. Okuno
Show more
Subject Terms :
automatic speech recognition
multimodal integration
open-source softwares
robot audition
sound-source localization
sound source separation
Computer engineering. Computer hardware
Control engineering systems. Automatic machinery (General)
Source :
Advanced Intelligent Systems, Vol 2, Iss 9, Pp n/a-n/a (2020)
Publisher :
Wiley, 2020.
Publication Year :
Collection :
LCC:Computer engineering. Computer hardware
Document Type :
File Description :
electronic resource
Language :
Relation :
Access URL :
Accession Number :
Academic Journal
Robot audition aims at developing robot's ears that work in the real world, that is, machine listening of multiple sound sources. Its critical problem is noise. Speech interfaces have become more familiar and more indispensable as smartphones and artificial intelligence (AI) speakers spread. Their critical problems are noise and multiple simultaneous speakers. Recently two technological advances have contributed to significantly improve the performance of speech interfaces and robot audition. Emerging deep learning technology has improved noise robustness of automatic speech recognition, whereas microphone array processing has improved the performance of preprocessing such as noise reduction. Herein, an overview and history of robot audition are provided together with introduction of an open‐source software for robot audition and its wide applications in the real world. Also, it is discussed how robot audition contributes to the development of computational auditory scene analysis, that is, understanding of real‐world auditory environments.

We use cookies to help identify your computer so we can tailor your user experience, track shopping basket contents and remember where you are in the order process.