Norihiro Takamune

Learn 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 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)
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 neu-ral 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)
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)
  • 1