In this letter, we address monaural source separation based on supervised nonnegative matrix factorization (SNMF) and propose a new penalized SNMF. Conventional SNMF often degrades the separation performance owing to the basis-sharing problem. Our penalized SNMF forces nontarget bases to become different from the target bases, which increases the separated sound quality.

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 basissharing 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.

When playing back videos with trick mode such as fast-forward and fast-rewind via home network, viewer may feel uncomfortable because of its unstable frame rate. This is a problem about user experience of home network video systems. This paper proposes the smooth playback method which has achieved a stable frame rate with statistical picture selection algorithms.

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