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To solve the problems of slow convergence and low computational precision of blind source separation(BSS) based on traditional particle swarm optimization(PSO), a novel approach-based adaptive particle swarm optimization for real-time blind source separation is proposed, in which the observations are linear convolutive mixtures of statistically independent speech sources. It combines the independent...
A novel method is proposed for speech model, which is used for speech transformation and speech recognition. This method divides segmentations of a speech into an adaptive segment, in which its speech wave is integrated and has approximate periods. A speech is divided into voiced speech and unvoiced speech by voice activity detection, which is widely used in speech dividing. This speech model mainly...
This research presents an innovative system for adaptive speech denoising using Independent Component Analysis (ICA) and Voice Activity Detection (VAD). Designed for instantaneous mixtures (two sources and two microphones), the proposed system identifies the noise contained in each noisy mixture. For that type of noise applies the most suitable ICA method among three methods (FastICA, Kernel ICA and...
In this paper, a sparse component analysis algorithm is presented for the case in which the number of sources is less than or equal to the number of sensors, but the channel (mixing matrix) is time-varying. The method is based on a smoothed ??0 norm for the sparsity criteria, and takes advantage of the idea that sparsity of the sources is decreased when they are mixed. The method is able to separate...
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