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Non-negative matrix factorization (NMF) is one of the most well-known techniques that are applied to separate a desired source from mixture data. In the NMF framework, a collection of data is factorized into a basis matrix and an encoding matrix. The basis matrix for mixture data is usually constructed by augmenting the basis matrices for independent(More)
This letter presents a speech enhancement technique combining statistical models and non-negative matrix factorization (NMF) with on-line update of speech and noise bases. The statistical model-based enhancement methods have been known to be less effective to non-stationary noises while the template-based enhancement techniques can deal with them quite(More)
Recently, lots of algorithms using machine learning approaches have been proposed in the speech enhancement area. One of the most well-known approaches is the non-negative matrix factorization (NMF) -based one which analyzes noisy speech with speech and noise bases. However, NMF-based algorithms have difficulties in estimating speech and noise encoding(More)
Speech enhancement based on statistical models has shown good performance, but the performance degrades when environment noise is highly non-stationary due to the stationary assumption. On the contrary, the template-based enhancement methods are more robust to non-stationary noise, but these are heavily dependent on a priori information present in training(More)
Nonnegative matrix factorization (NMF) is an unsupervised technique to represent nonnegative data as linear combinations of nonnegative bases, which has shown impressive performance for source separation. However, its source separation performance degrades when one signal can also be described well with the bases for the interfering source signals. In this(More)
Nonnegative matrix factorization (NMF) is a matrix factorization technique that might find meaningful latent nonnegative components. Since, however, the objective function is non-convex, the source separation performance can degrade when the iterative update of the basis matrix is stuck to a poor local minimum. Most of the research updates basis iteratively(More)
This paper presents a novel data-driven approach to single channel speech enhancement employing Gaussian process (GP). Our approach is based on applying GP regression to estimate the residual gain with the input features being the a priori and a posteriori signal-to-noise ratios (SNRs). The residual gain is defined as the difference between the optimal gain(More)
Non-negative matrix factorization (NMF) is a dimensionality reduction method that usually leads to a part-based representation, and it is shown to be effective for source separation. However, the performance of the source separation degrades when one signal can be described with the bases for the other source signals. In this paper, we propose a(More)
Non-negative matrix factorization (NMF) is an unsupervised technique to represents a nonnegative data matrix with a product of nonnegative basis and encoding matrices. The encoding matrix for the training phase contains information on the pattern of how each basis vector is utilized. The histogram for each row of this matrix corresponding to a specific(More)
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