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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)-model-based ICA with information-geometric learning algorithm for high-fidelity 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 SIMO-ICA can separate the mixed signals,(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 GMM-based voice conversion algorithm to STRAIGHT proposed by Kawahara et al., which is recognized as a high quality vocoder. In order(More)
We propose a new algorithm for blind source separation (BSS), in which frequency-domain independent component analysis (FDICA) and time-domain ICA (TDICA) are combined to achieve a superior source-separation performance under reverberant conditions. Generally speaking, conventional TDICA fails to separate source signals under heavily reverberant conditions(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)
Frequency-domain blind source separation (BSS) is shown to be equivalent to two sets of frequency-domain adaptive beamformers (ABFs) under certain conditions. The zero search of the off-diagonal components in the BSS update equation can be viewed as the minimization of the mean square error in the ABFs. The unmixing matrix of the BSS and the filter(More)
百lis paper describes an automatic building of N-gram language models from Web texts for large vocabulary continuous speech recognition. Although a huge amount of well-formed texts are needed to train a model, collecting and organizing such text cor­ pus for every task by hand needs a great labor. We need the lan­ guage model to update frequently to cover(More)