A sound source identification system for ensemble music based on template adaptation and music stream extraction

Sound source identification is an important problem in auditory scene analysis when multiple sound objects are simultaneously present in the scene. This paper proposes an adaptive method for sound source identification that is applicable to real performances of ensemble music. For musical sound source identification, the feature-based methods and template-matching-based methods were already proposed. However, it is difficult to extract features of a single note from a sound mixture. In addition, sound variability has been a problem when dealing with real music performances. Thus this paper proposes an adaptive method for template matching that can cope with variability in musical sounds. The method is based on the matched filtering and does not require a feature extraction process. Moreover, this paper discusses musical context integration based on the Bayesian probabilistic networks. Evaluations using recordings of real ensemble performances have revealed that the proposed method improve the source identification accuracy from 60.8% to 88.5% on average.

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