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

Sasou Akira, AIST
Tanaka Kazuyo, AIST

Page numbers in the proceedings:
Volume I pp 409-412

Sound Synthesis and Propagation

Paper abstract
This paper describes a novel method for segregating concurrent 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 occur 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 template 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 indicate that the proposed method is effective for various types of sounds.

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