Joint model-based recognition and localization of overlapped acoustic events using a set of distributed small microphone arrays
This work addresses the challenge of analyzing overlapped acoustic events for applications like smart environments, but it is incremental as it builds on existing methods with a joint framework.
The paper tackles the problem of detecting, recognizing, and localizing multiple simultaneous acoustic events in a meeting-room scenario, presenting a model-based approach that jointly performs these tasks using distributed small microphone arrays. Experimental results on two datasets show the approach outperforms usual techniques, with further improvement from estimated priors.
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.