Joint Audio Source Localization and Separation with Distributed Microphone Arrays Based on Spatially-Regularized Multichannel NMF
Yoshiaki Sumura, Diego Di Carlo, Aditya Arie Nugraha, Yoshiaki Bando, Kazuyoshi Yoshii · 2024
This paper describes a statistically principled method that simultaneously localizes and separates multiple sound sources using multiple calibrated microphone arrays distributed in a room. Given the extensive research on direction of arrival (DOA) estimation with a single microphone array, for 3D source localization, one may attempt triangulation based on DOAs separately and egocentrically estimated by multiple arrays. However, in multiple sources scenarios, this cascading approach faces both the inter-array DOA association problem and the error accumulation problem. To solve these problems, we propose a spatially regularized extension of a versatile blind source separation method called multichannel nonnegative matrix factorization (MNMF). Our method treats multiple microphone arrays as a single big array and puts priors on the frequency-wise spatial covariance matrices (SCMs) of each source. These priors are defined using the source DOA computed from the 3D positions of the source and arrays. The power spectral densities (PSDs), SCMs, and positions of multiple sources are jointly estimated under the unified maximum-a-posteriori (MAP) principle. We show the effectiveness of the joint statistical estimation for real data recorded by four five-channel microphone arrays of Microsoft Azure Kinect.