Blind source separation with optimal transport non-negative matrix factorization
نویسندگان
چکیده
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 کاملSupervised non-negative matrix factorization for audio source separation
Source separation is a widely studied problems in signal processing. Despite the permanent progress reported in the literature it is still considered a significant challenge. This chapter first reviews the use of non-negative matrix factorization (NMF) algorithms for solving source separation problems, and proposes a new way for the supervised training in NMF. Matrix factorization methods have ...
متن کاملAn Experimental Survey on Non-Negative Matrix Factorization for Single Channel Blind Source Separation
In applications such as speech and audio denoising, music transcription, music and audio based forensics, it is desirable to decompose a single-channel recording into its respective sources, commonly referred to as blind source separation (BSS). One of the techniques used in BSS is non-negative matrix factorization (NMF). In NMF both supervised and unsupervised mode of operations is used. Among...
متن کاملNon-Negative Matrix Factorization for Blind Source Separation in Wavelet Transform Domain
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 inver...
متن کاملNon-Negative Matrix Factorization and Its Application in Blind Sparse Source Separation with Less Sensors Than Sources
Non-Negative Matrix Factorization (NMF) implies that a given nonnegative matrix is represented by a product of two non-negative matrices. In this paper, a factorization condition (consistent condition) on basis matrix is proposed firstly. For a given consistent basis matrix, although there exist infinite solutions (factorizations) generally, the sparse solution is unique with probability one, w...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: EURASIP Journal on Advances in Signal Processing
سال: 2018
ISSN: 1687-6180
DOI: 10.1186/s13634-018-0576-2