A Music Stream Segregation System Based on Adaptive Multi-Agents

Abstract

A principal problem of auditory scene analysis is stream segregation: decomposing an input acoustic signal into signals of individual sound sources included in the input. While existing signal processing algorithms cannot properly solve this inverse problem, a multiagent-based architecture has been considered to be a promising methodology in its modularity and scalability. However, most attempts made so far depend on subjectively defined rules to deal with variability of sounds. Here we propose a quantitatively principled architecture in agent interaction by formulating the problem as least-squares optimization. In this architecture, adaptation of the agents is the essential idea. We have developed two kinds of processing to realize adaptivity: template filtering and phase tracking. These mechanisms enable each agent to optimally, in the least-squares sense, track the individual sound. As an example application of the proposed architecture, we have built a music recognition system that recognizes instrument names and pitches of the notes included in ensemble music performances. Experimental results show that these adaptive mechanisms significantly improve the recognition accuracy.