상세정보
학술대회
Nonnegative Matrix Partial Co-Factorization for Drum Source Separation
- 발행일
- 201003
- 출처
- International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2010, pp.1942-1945
- 초록
- We address a problem of separating drums from polyphonic music containing various pitched instruments as well as drums. Nonnegative matrix factorization (NMF) was successfully applied to spectrograms of music to learn basis vectors, followed by support vector machine (SVM) to classify basis vectors into ones associated with drums (rhythmic source) only and pitched instruments (harmonic sources). Basis vectors associated with pitched instruments are used to reconstruct drum-eliminated music. However, it is cumbersome to construct a training set for pitched instruments since various instruments are involved. In this paper, we propose a method which only incorporates prior knowledge on drums, not requiring such training sets of pitched instruments. To this end, we present nonnegative matrix partial co-factorization (NMPCF) where the target matrix (spectrograms of music) and drum-only-matrix (collected from various drums a priori) are simultaneously decomposed, sharing some factor matrix partially, to force some portion of basis vectors to be associated with drums only. We develop a simple multiplicative algorithm for NMPCF and show its usefulness empirically, with numerical experiments on real-world music signals. ©2010 IEEE.
- KSP 제안 키워드
- Harmonic sources, Multiplicative algorithm, Nonnegative Matrix Factorization(NMF), Nonnegative Matrix Partial Co-Factorization, Numerical experiments, Real-world, Support VectorMachine(SVM), Target matrix, basis vectors, polyphonic music, prior knowledge