Monophonic sound source separation by non-negative sparse autoencoders - Zen, Keiki; Suzuki, Masahiro; Sato, Haruhiko; Oyama, Satoshi; Kurihara, Masahito (Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics, 2014)
Monophonic sound source separation is an essential subject on the fields where sound, such as voice, music and noise, is dealt with. In particular, unsupervised approaches to this problem have high versatility in comparison with supervised approaches. Non-negative matrix factorization is the most frequently used algorithm for the monophonic sound source separation without prior knowledge. This is also applied to various applications, including data clustering, face recognition, gene expression classification. However, non-negative matrix factorization cannot be efficiently used in online learning. In order to solve this difficulty, the non-negative sparse autoencoder was proposed in the literature. Although several successful applications have been reported, this is not yet applied to the monophonic sound source separation. This paper shows that the non-negative sparse autoencoder can perform the monophonic sound source separation without prior knowledge in online learning.