Online Adaptation of Fourier Series Based Acoustic Transfer Function Model to Improve Sound Source Localization and Separation

Abstract

This paper proposes an online adaptation method for Fourier series based acoustic transfer function (TF) models for robot audition systems based on microphone array signal processing. The TF represents the signal propagation characteristics from a sound source to a microphone, which is an essential component for real-world auditory scene analysis, including sound source localization and separation. The real-world applications of TF-based array signal processing requires two characteristics: 1) adaptability to changes in the acoustic environment (changes in the signal propagation characteristics between the sound source and the microphone), and 2) a lightweight TF set for use in embedded systems such as robots with limited memory and computational resources. This paper proposes an online adaptation method for lightweight TF models using the Fourier series expansion. This method has both above two characteristics. Experimental results showed that the use of TF set adapted online using the proposed method performs better sound source localization and separation performance than existing online TF adaptation methods.