Non-Negative Matrix Factorization for Blind Source Separation in Wavelet Transform Domain
نویسندگان
چکیده
This paper describes a new multilevel decomposition method for the separation of convolutive image mixtures. The proposed method uses an Adaptive Quincunx Lifting Scheme (AQLS) based on wavelet decomposition to preprocess the input data, followed by a Non-Negative Matrix Factorization whose role is to unmix the decomposed images. The unmixed images are, thereafter, reconstructed using the inverse of AQLS transform. Experiments carried out on images from various origins showed that the proposed method yields better results than many widely used blind source separation algorithms. keywords: Convolutive blind source separation, nonnegative matrix factorization, wavelet transform, multiscale analysis, adaptive lifting scheme, quincunx sampling.
منابع مشابه
Blind Source Separation with Optimal Transport Non-negative Matrix Factorization
Optimal transport as a loss for machine learning optimization problems has recently gained a lot of attention. Building upon recent advances in computational optimal transport, we develop an optimal transport non-negative matrix factorization (NMF) algorithm for supervised speech blind source separation (BSS). Optimal transport allows us to design and leverage a cost between short-time Fourier ...
متن کاملGeneralized Cepstral Features for Clustering in Blind Audio Source Separation
To generalize cepstral domain audio features, the usage of the so-called generalized logarithm function has been proposed. In this paper, the A-law companding function is suggested as another suitable generalization regarding amplitude scaling and mel-scale warping. The application of these generalized cepstral features in a state-ofthe-art blind source separation (BSS) algorithm is evaluated: ...
متن کاملSéparation de sources audio en milieu réverbérant : Factorisation en matrices non-négatives et représentation temporelle du mélange convolutif
This paper addresses the problem of multichannel audio source separation in under-determined reverberant mixtures. We target a semi-blind scenario assuming that the mixing lters are known. The proposed method consists in working directly with the time-domain mixture signals. This approach makes it possible to accurately represent the convolutive mixing process, it is therefore suitable for the ...
متن کاملA Novel Algorithm for Multichannel Deconvolutive based on αβ-Divergence
We introduce a novel Algorithm for underdetermined convolutive mixture of source signals. Where the convolution is routinely approximated in the short-time Fourier transform (STFT) domain as linear instantaneous mixing in each frequency band. Each source STFT is given a model inspired from nonnegative matrix factorization (NMF) with the -divergence, this divergence is a family of cost functions...
متن کاملNMF with Sparse Regularizations in Transformed Domains
Non-negative blind source separation (non-negative BSS), which is also referred to as non-negative matrix factorization (NMF), is a very active field in domains as different as astrophysics, audio processing or biomedical signal processing. In this context, the efficient retrieval of the sources requires the use of signal priors such as sparsity. If NMF has now been well studied with sparse con...
متن کامل