Hiroshi Saruwatari

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Despite several recent proposals to achieve blind source separation (BSS) for realistic acoustic signals, the separation performance is still not good enough. In particular, when the impulse responses are long, performance is highly limited. In this paper, we consider a two-input, two-output convolutive BSS problem. First, we show that it is not good to be(More)
We propose a new Single-Input Multiple-Output (SIMO)-modelbased ICA with information-geometric learning algorithm for highfidelity blind source separation. The SIMO-ICA consists of multiple ICAs and a fidelity controller, and each ICA runs in parallel under the fidelity control of the entire separation system. The SIMOICA can separate the mixed signals, not(More)
We describe a new method of blind source separation (BSS) on a microphone array combining subband independent component analysis (ICA) and beamforming. The proposed array system consists of the following three sections: (1) subband ICA-based BSS section with estimation of the direction of arrival (DOA) of the sound source, (2) null beamforming section based(More)
In the voice conversion algorithm based on the Gaussian Mixture Model (GMM) applied to STRAIGHT, quality of converted speech is degraded because the converted spectrum is exceedingly smoothed. In this paper, we propose the GMM-based algorithm with dynamic frequency warping to avoid the over-smoothing. We also propose an addition of the weighted residual(More)
We propose a new blind spatial subtraction array (BSSA) consisting of a noise estimator based on independent component analysis (ICA) for efficient speech enhancement. In this paper, first, we theoretically and experimentally point out that ICA is proficient in noise estimation under a non-point-source noise condition rather than in speech estimation.(More)
This paper addresses the determined blind source separation problem and proposes a new effective method unifying independent vector analysis (IVA) and nonnegative matrix factorization (NMF). IVA is a state-of-the-art technique that utilizes the statistical independence between sources in a mixture signal, and an efficient optimization scheme has been(More)
We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the slow-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (a) frequency-domain ICA with direction-of-arrival (DOA) estimation, (b) null beamforming(More)
Despite several recent proposals to achieve Blind Source Separation (BSS) for realistic acoustic signal, separation performance is still not enough. In particular, when the length of impulse response is long, performance is highly limited. In this paper, we show it is useless to be constrained by the condition, P T , where T is the frame size of FFT and P(More)
The voice conversion algorithm based on the Gaussian mixture model (GMM) has also been proposed by Stylianou et al. In this algorithm, the acoustic space of a speaker is represented continuously. In this paper, we apply this GMMbased voice conversion algorithm to STRAIGHT proposed by Kawahara et al., which is recognized as a high quality vocoder. In order(More)