Hiroyuki Okumura

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This study proposes a method to decompose a signal into a set of periodic signals with time-varying amplitude. The proposed method imposes a penalty on the resultant periodic subsignals in order to improve the sparsity of the decomposition. This penalty is defined as the sum of the l 2 norms of the resultant periodic subsignals to find the shortest path to(More)
In this paper, we propose a single-channel speech separation method by using a sparse decomposition with a periodic signal model. In our separation method, a mixture of speeches is approximated with periodic signals with time-varying amplitude. The decomposition with the periodic signal model is performed under a sparsity penalty. Due to the sparsity(More)
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