Norihiro Takamune

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This paper deals with the problem of the underdetermined blind separation and tracking of moving sources. In practical situations, sound sources such as human speakers can move freely and so blind separation algorithms must be designed to track the temporal changes of the impulse responses. We propose solving this problem through the posterior inference of(More)
For monaural source separation two main approaches have thus far been adopted. One approach involves applying non-negative matrix factorization (NMF) to an observed magnitude spectrogram, interpreted as a non-negative matrix. The other approach is based on the concept of computational auditory scene analysis (CASA). A CASA-based approach called the "(More)
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(More)
Restricted Boltzmann Machines (RBMs) are neural network models for unsupervised learning, but have recently found a wide range of applications as feature extractors for supervised learning algorithms. They have also received a lot of attention recently after being proposed as building blocks for deep belief networks. The success of these models raises the(More)
Restricted Boltzmann machines (RBMs) are stochastic neural networks that can be used to learn features from raw data. They have attracted particular attention recently after being proposed as building blocks for deep belief network (DBN) and have been applied with notable success in a range of problems including speech recognition and object recognition.(More)
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(More)
Layerwise pre-training is one of important elements for deep learning, and Restricted Boltzmann Machines (RBMs) is popular layerwise pre-training method. At present, the most popular training algorithm for RBMs is the Contrastive Divergence (CD) learning algorithm. We propose deriving a new training algorithm based on an auxiliary function approach for RBMs(More)
This paper deals with the problem of the underdetermined blind separation and tracking of moving sources. In practical situations, sound sources such as human speakers can move and so blind separation algorithms must be designed to track the temporal change of the impulse responses. We propose solving this problem through the posterior inference of the(More)
A method for sparse sound field decomposition with parametric dictionary learning is proposed. Sound field decomposition forms the foundation of various acoustic signal processing applications. Our main focus is sound field recording and reproduction for high-fidelity audio systems. To improve the reproduction accuracy above the spatial Nyquist frequency,(More)
A sparse sound field decomposition method using prior information on source signals in the time-frequency domain is proposed. Sparse sound field decomposition has been proved to be effective for various acoustic signal processing applications. Current methods for sparse decomposition are based only on the spatial sparsity of the source distribution.(More)
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