Application of blind source separation in industrial noise prediction and control

Wei Yang, Tiao Joo Kwee, Cheng Siong Chin, Wai Lok Woo, Sajin Saju

Research output: Contribution to conferencePaperpeer-review

Abstract

Blind Source Separation (BSS) has been widely used for speech separation, imaging processing, and other applications, but not in the field of industrial noise separation. In this study, the algorithms of BSS are applied to the separation of industrial noises. Two BSS algorithms, namely: Independent Component Analysis (ICA) and Degenerate Un-Mixing Estimation Technique (DUET) are used. Both modeling and experimental sound signals are tested, and the accuracy of the prediction is evaluated. When the modeled mixtures are separated, results show that both the ICA and DUET algorithm can separate the sound sources successfully. However, as compared to ICA, the DUET method can detect the number of sound sources and predict the relative delay between the sound sources. When the measured sound mixtures are used for the separation, the accuracy of prediction is reduced as the sound contains both the amplitude attenuation and phase delay but also the reverberation.

Original languageEnglish
Pages116-127
Number of pages12
Publication statusPublished - 18 Dec 2018
Event47th International Congress and Exposition on Noise Control Engineering: Impact of Noise Control Engineering, INTER-NOISE 2018 - Chicago, United States
Duration: 26 Aug 201829 Aug 2018

Conference

Conference47th International Congress and Exposition on Noise Control Engineering: Impact of Noise Control Engineering, INTER-NOISE 2018
CountryUnited States
CityChicago
Period26/08/1829/08/18

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