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Fully-Online Always-Adaptation of Transfer Functions and Its Application to Sound Source Localization and Separation | IEEE Conference Publication | IEEE Xplore

Fully-Online Always-Adaptation of Transfer Functions and Its Application to Sound Source Localization and Separation


Abstract:

This paper addresses fully-online always-adaptation of a transfer function for robot audition systems based on microphone array processing. The transfer function represen...Show More

Abstract:

This paper addresses fully-online always-adaptation of a transfer function for robot audition systems based on microphone array processing. The transfer function represents signal propagation characteristics between a microphone and a sound source, which provides essential information for real-world scene analysis, such as sound source localization and separation for robots. Although it is commonly defined as a stationary function, it should be considered together with room acoustics and their environmental changes for practical use, that is, it should be defined as a dynamically-changing function. To fulfill this requirement, we propose a fully-online always-adaptation method for a transfer function, by continuously estimating the transfer function from the observed signals in a passive manner, while performing sound source localization and separation. The proposed method was implemented on open source robot audition software HARK as modules which works online. These modules are applied to sound source localization and separation which are primary functions in robot audition. Experimental results showed that the proposed method successfully adapted to an office environment and improved the performance of sound source localization and separation at a close level to the transfer function recorded in the room.
Date of Conference: 27 September 2021 - 01 October 2021
Date Added to IEEE Xplore: 16 December 2021
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Conference Location: Prague, Czech Republic

I. INTRODUCTION

Robot audition [1] has been studied for more than twenty years. Sound source localization and separation are essential functions in robot audition to communicate with people and understand the surrounding auditory scene in the real world, since robots have to listen to several sound sources at the same time even in a noisy environment. Many methods for sound source localization and separation have been reported. They are mainly classified into four groups such as fixed beamforming, adaptive beamforming, blind separation, and deep learning-based methods.

References

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