A waveform generation model based approach for segregation of monaural mixture sound

This paper describes a novel method for segregating concur-rent monaural sounds. In a real environment, there are many types of sounds, such as periodic sound, aperiodic sound, impulsive sound and so on, and several sounds usually oc-cur simultaneously. In order to recognize the sounds, it is necessary to be able to model such various type of sounds and segregate the concurrent sounds. The proposed method adopts a waveform generation model consisting of an Auto-Regressive process and a Hidden Markov Model as a tem-plate model and achieves segregation of monaural concurrent sounds based on the mixed AR-HMMs. Experiments were conducted to confirm the feasibility of the method using ten types of non-speech sounds. The experimental results indi-cate that the proposed method is effective for various types of sounds.