[4O2-OS-3b] [Organized Session] OS-3

[4O2-OS-3b-04] Polyphonic music factorization into sound basis and activation using RBM

〇Kenya Arakawa¹, Toru Nakashika¹ (1. The University of Electro-Communications)

Keywords:Music, RBM, machine learning

Recently, music studies based on deep learning that require a large amount of input have been garnering attention increasing. Along with that, the task of generating accurate scores from audio data is also important. Although NMF is often used for music factorization into sound basis and activation, there is room for improvement and many methods have currently being proposed. In this paper, we propose method of polyphonic music factorization using RBM. RBM is stochastic model and outputs binary-valued latent features, which is suitable for music score notation. Furthermore, we also propose sparse-RBM in order to settle cross cancel problem. In conclusion, our proposed method showed better accuracy than NMF.