Joint model-based recognition and localization of overlapped acoustic events using a set of distributed small microphone arrays

TL;DR

This paper introduces a joint model-based system that simultaneously recognizes, localizes, and detects overlapping acoustic events using multiple small microphone arrays, improving performance over traditional separate methods.

Contribution

It presents a novel integrated approach for acoustic event recognition and localization in overlapping scenarios using distributed microphone arrays, with demonstrated experimental advantages.

Findings

Improved accuracy in recognizing overlapping acoustic events.

Effective localization of multiple simultaneous sources.

Enhanced performance with estimated priors.

Abstract

In the analysis of acoustic scenes, often the occurring sounds have to be detected in time, recognized, and localized in space. Usually, each of these tasks is done separately. In this paper, a model-based approach to jointly carry them out for the case of multiple simultaneous sources is presented and tested. The recognized event classes and their respective room positions are obtained with a single system that maximizes the combination of a large set of scores, each one resulting from a different acoustic event model and a different beamformer output signal, which comes from one of several arbitrarily-located small microphone arrays. By using a two-step method, the experimental work for a specific scenario consisting of meeting-room acoustic events, either isolated or overlapped with speech, is reported. Tests carried out with two datasets show the advantage of the proposed approach with respect to some usual techniques, and that the inclusion of estimated priors brings a further performance improvement.