A Skip Attention Mechanism for Monaural Singing Voice Separation
نویسندگان
چکیده
منابع مشابه
Singing Voice Separation from Monaural Recordings
Separating singing voice from music accompaniment has wide applications in areas such as automatic lyrics recognition and alignment, singer identification, and music information retrieval. Compared to the extensive studies of speech separation, singing voice separation has been little explored. We propose a system to separate singing voice from music accompaniment from monaural recordings. The ...
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Singing voice separation based on deep learning relies on the usage of time-frequency masking. In many cases the masking process is not a learnable function or is not encapsulated into the deep learning optimization. Consequently, most of the existing methods rely on a post processing step using the generalized Wiener filtering. This work proposes a method that learns and optimizes (during trai...
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This paper presents a Bayesian nonnegative matrix factorization (NMF) approach to extract singing voice from background music accompaniment. Using this approach, the likelihood function based on NMF is represented by a Poisson distribution and the NMF parameters, consisting of basis and weight matrices, are characterized by the exponential priors. A variational Bayesian expectationmaximization ...
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This extended abstract describes the system we submitted for the singing voice separation task of MIREX 2016. Our submission here is an extension of the deep clustering network from [1].
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Separating the leading vocals from the musical accompaniment is a challenging task that appears naturally in several music processing applications. Robust principal component analysis (RPCA) has been recently employed to this problem producing very successful results. The method decomposes the signal into a low-rank component corresponding to the accompaniment with its repetitive structure, and...
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ژورنال
عنوان ژورنال: IEEE Signal Processing Letters
سال: 2019
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2019.2935867