[PDF] Top 20 Blind source separation with optimal transport non negative matrix factorization
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Blind source separation with optimal transport non negative matrix factorization
... Using optimal transport as a loss between spectrograms was also proposed by [10] under the name “optimal spec- tral ...cost matrix designed specifically for musical instruments, allowing them ... See full document
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On the use of a spatial cue as prior information for stereo sound source separation based on spatially weighted non negative tensor factorization
... channel matrix, Q, was further developed to improve the separation ...of separation quality, which was carried out as in previous studies, demonstrated the effec- tiveness of the weighting tensor, G, ... See full document
9
Multimodal voice conversion based on non-negative matrix factorization
... the source speaker’s audio- visual feature with the target speaker’s audio feature, the voice individuality of the source speaker is converted to the target ...an optimal weight of the image feature, ... See full document
9
Feature enhancement of reverberant speech by distribution matching and non negative matrix factorization
... years—non-negative matrix factorization (NMF)—which models the speech spectrogram as a sparse non-negative linear combination of dictionary elements (“speech ...sound ... See full document
14
Percussive/harmonic sound separation by non-negative matrix factorization with smoothness/sparseness constraints
... Filtering-based Separation (MFS) under two assumptions: the harmonics are considered to be outliers in a tempo- ral slice that contains a mixture of percussive and pitched instruments, and the percussive onsets ... See full document
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Posteriori Regularization based Non Negative Matrix Factorization approach for Speech Enhancement
... An NMF applications includes vast areas like source separation, pattern recognition, classification [4], spectrogram analysis. In this paper, regularized NMF based speech enhancement is proposed, which uses ... See full document
6
Bayesian group sparse learning for music source separation
... Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative signals with the sparseness ...for blind source separation and many other signal ... See full document
15
Development Of Source Separation Algorithm In Audio Application
... sound source such as instruments or ...to blind source separation and familiar techniques that used to extract the single sources from mixture signals is known as non-negative ... See full document
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Feature enhancement of reverberant speech by distribution matching and non-negative matrix factorization
... years—non-negative matrix factorization (NMF)—which models the speech spectrogram as a sparse non-negative linear combination of dictionary elements (“speech ...sound ... See full document
14
A Novel Singing Voice Separation Method Based on Sparse Non-Negative Matrix Factorization and Low-Rank Modeling
... the source domain dictionary is transferred to a target domain for image denoising with a dictionary-regularization term designed based on the energy ...is non- stationary. It is clear that encountering ... See full document
11
Blind Source Separation for NMR Spectra with Negative Intensity
... of blind source separation techniques to reproduce the spectra of the underlying pure ...(Non-Negative Matrix Factorization) are top-performing ...to blind ... See full document
27
Unsupervised machine learning applied to scanning precession electron diffraction data
... determine source sig- nals a priori is known as blind source separation (BSS) ...a non-negative double singular value decomposition (NNDSVD), which is based on two SVD processes, ... See full document
14
Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
... We introduced various forms of the NMF problem in the previous section. Next, we concentrate on practical algorithms to find locally optimal solutions. Unlike previous work, where variations of the gradient ... See full document
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A study of blind source separation using nonnegative matrix factorization
... channel blind source separation (SCBSS) which is based on NMF with spectral masks has been ...the separation process even when calculating NMF with fewer iteration, which yields a faster ... See full document
7
Non-negative matrix factorization for blind image separation
... perform blind source separation rather very poorly due to non-uniqueness of solution and/or lack of additional constraints which should be ...the matrix A), the dual viewpoint by ... See full document
20
An Experimental Survey on Non Negative Matrix Factorization for Single Channel Blind Source Separation
... by non-gaussianities ...with non negativity constraints. In NMF, the non negativity constraint leads to the parts based representation of the input mixture which helps to develop structural ... See full document
6
Blind Source Separation Survey
... blended source. On account of non-covering occasion grouping, numerous methodologies have made an extraordinary progress utilizing different component extraction strategies and profound learning ...with ... See full document
5
Exploiting Nonnegative Matrix Factorization with Mixed Group Sparsity Constraint to Separate Speech Signal from Single-channel Mixture with Unknown Ambient Noise
... the separation mask or the separation model by end-to-end training and gain a significant ...from source examples obtained by a search engine to guide the separation ... See full document
8
A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization
... Bayesian Non-negative Matrix Factorization (BNMF) is a promising approach for under- standing uncertainty and structure in matrix ...traditional non-Bayesian NMF objectives that ... See full document
56
Learning Microbial Community Structures with Supervised and Unsupervised Non-negative Matrix Factorization
... the ith OTU or gene is observed in the jth sample. Thus, each feature (column) in T describes a subcommunity and each column in W contains the linear coefficients for the corresponding sample (column) in X. The whole ... See full document
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