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We address a novel nonnegative matrix factorization (NMF) with a new basis deformation method to handle various music sounds. Conventional supervised NMF has a critical problem that a mismatch between bases trained in advance and an actual target sound reduces the accuracy of separation. To solve this problem, we proposed an advanced supervised NMF that applies a single time-invariant filter to the...
This paper addresses an audio source separation problem and proposes a new basis training method for semi-supervised nonnegative matrix factorization (NMF). In a conventional semi-supervised NMF, pretrained spectral bases for a target source can represent other undesired interfering sources, which degrade the separation performance. To solve this problem, we propose the training of two types of supervised...
In this paper, we address the music signal separation problem and propose a new supervised nonnegative matrix factorization (SNMF) algorithm employing the deformation of a spectral supervision basis trained in advance. Conventional SNMF has a problem that the separation accuracy is degraded by a mismatch between the trained basis and the spectrogram of the actual target sound in open data. To reduce...
In this paper, we propose a new hybrid method that concatenates directional clustering and advanced nonnegative matrix factorization (NMF) for the purpose of the specific sound extraction from the multichannel music signal. Multichannel music signal separation technology is aimed to extract a specific target signal from observed multichannel signals that contain multiple instrumental sounds. In the...
In this paper, we address an optimization issue for the divergence in supervised nonnegative matrix factorization with spectrogram restoration, which has been proposed for addressing multichannel signal separation. This method separates non-target components and reconstructs some missing data caused by preceding spatial clustering via supervised basis extrapolation. In our previous study, we only...
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...
In this paper, we address a music signal separation problem, and propose a new supervised algorithm for real instrumental signal separation employing a deformable capability for a spectral supervision trained in advance. Nonnegative matrix factorization (NMF) is one of the techniques used for the separation of an audio mixture that consists of multiple instrumental sources. Conventional supervised...
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