Abstract:
In this paper, we address a monaural source separation problem and propose a new penalized supervised nonnegative matrix factorization (SNMF). Conventional SNMF often deg...Show MoreMetadata
Abstract:
In this paper, we address a monaural source separation problem and propose a new penalized supervised nonnegative matrix factorization (SNMF). Conventional SNMF often degrades the separation performance owing to the basis-sharing problem between supervised bases and nontarget bases. To solve this problem, we employ two types of penalty term based on orthogonality and divergence maximization in the cost function to force the nontarget bases to become as different as possible from the supervised bases. From the experimental results, it can be confirmed that the proposed method prevents the simultaneous generation of similar spectral patterns in the supervised bases and other bases, and increases the separation performance compared with the conventional method.
Date of Conference: 12-15 December 2013
Date Added to IEEE Xplore: 03 April 2014
Electronic ISBN:978-1-4799-4796-6
Print ISSN: 2162-7843