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Reducing the interference noise in a monaural noisy speech signal has been a challenging task for many years. Compared to traditional unsupervised speech enhancement methods, e.g., Wiener filtering, supervised approaches, such as algorithms based on hidden Markov models (HMM), lead to higher-quality enhanced speech signals. However, the main practical(More)
This paper proposes a single-channel speech dereverberation method enhancing the spectrum of the reverberant speech signal. The proposed method uses a non-negative approximation of the convolutive transfer function (N-CTF) to simultaneously estimate the magnitude spectrograms of the speech signal and the room impulse response (RIR). To utilize the speech(More)
This paper presents two single-channel speech dereverberation methods to enhance the quality of speech signals that have been recorded in an enclosed space. For both methods, the room acoustics are modeled using a non-negative approximation of the convolutive transfer function (N-CTF), and to additionally exploit the spectral properties of the speech(More)
Recently projected gradient (PG) approaches have found many applications in solving the minimization problems underlying nonnegative matrix factorization (NMF). NMF is a linear representation of data that could lead to sparse result of natural images. To improve the parts-based representation of data some sparseness constraints have been proposed. In this(More)
Source separation models that make use of nonnegativity in their parameters have been gaining increasing popularity in the last few years, spawning a significant number of publications on the topic. Although these techniques are conceptually similar to other matrix decompositions, they are surprisingly more effective in extracting perceptually meaningful(More)
Deriving a good model for multitalker babble noise can facilitate different speech processing algorithms, e.g., noise reduction, to reduce the so-called cocktail party difficulty. In the available systems, the fact that the babble waveform is generated as a sum of N different speech waveforms is not exploited explicitly. In this paper, first we develop a(More)
In this paper, a linear MMSE filter is derived for single-channel speech enhancement which is based on Nonnegative Matrix Factorization (NMF). Assuming an additive model for the noisy observation, an estimator is obtained by minimizing the mean square error between the clean speech and the estimated speech components in the frequency domain. In addition,(More)
We present a speech enhancement algorithm which is based on a Bayesian Nonnegative Matrix Factorization (NMF). Both Minimum Mean Square Error (MMSE) and Maximum a-Posteriori (MAP) estimates of the magnitude of the clean speech DFT coefficients are derived. To exploit the temporal continuity of the speech and noise signals, a proper prior distribution is(More)
Nonnegative matrix factorization is an appealing technique for many audio applications. However, in it's basic form it does not use temporal structure, which is an important source of information in speech processing. In this paper, we propose NMF-based filtering and smoothing algorithms that are related to Kalman filtering and smoothing. While our(More)