Realtime Multiple-pitch and Multiple-instrument Recognition For Music Signals using Sparse Non-negative Constraints

International audience; In this paper we introduce a simple and fast method for realtime recognition of multiple-pitches produced by multiple musical instruments. Our proposed method is based on two important facts: one that timbral information of any instrument is pitch-dependant and two, that the modulation spectrum of the same pitch seems to result into a persistent representation of the characteristics of the instrumental family, as discussed in the paper. Using these basic facts, we construct a learning algorithm to obtain pitch templates of all possible notes on various instruments and then devise an online algorithm to decompose a realtime audio buffer using the learned templates. The learning and decomposition proposed here are inspired by non-negative matrix factorization methods but differ by introduction of an explicit sparsity control. Our test results show significant recognition rate for a realtime system and on real music recordings. We discuss further improvements that can be made over the proposed system.