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Techniques of using a microphone array to determine a sound source location, the localization problem, has been studied for many years. A popular method is the so-called MUSIC (Multiple Signal Classification). There is a second type of method that tries to solve both sound separation and localization problems in one setting. The second method used for localization purpose is less known. In this study,...
Musical noise is a typical problem with blind source separation using a time-frequency mask. Recently, the cepstral smoothing of spectral masks (CSM) was proposed. Based on the idea of smoothing in the cepstral domain, this paper proposes the cepstral smoothing of separated signals (CSS) on the assumption that a cepstral representation better reflects the characteristics of speech signals than those...
Independent component analysis (ICA) is a popular approach for blind source separation when the source signals are stationary with fixed distribution functions. However, the source signals are nonstationary in real-world applications, e.g. the source signals may abruptly appear or disappear or even the number of sources may be changed by time. This study presents a nonstationary ICA for dynamic source...
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 new technique based on the Fourier-Bessel (FB) expansion is presented for separating multiple formants of a speech signal. The discrete energy separation algorithm (DESA) is applied to an isolated speech formant to extract the instantaneous frequency (IF) and the time-varying amplitude envelope (AE) of the formant. It is demonstrated that the proposed technique which is called the...
A sequential approach to sparse component analysis (SeqTIF) is proposed in this paper. Although SeqTIF employs the estimation process of the simultaneous TIFROM algorithm, a source cancellation and deflation technique are also incorporated to sequentially estimate speech signals in the mixture. Results indicate that SeqTIF's separation performance shows a clear improvement upon the simultaneous TIFROM...
This paper addresses the problem of representing the image and speech signal using a set of features that are approximately statistically independent. This statistical independence simplifies building probabilistic models based on these features that can be used in applications like speaker recognition. Since basic independent component analysis (ICA) isn't suitable to many applications because of...
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