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Shifted and convolutive source-filter non-negative matrix factorization for monaural audio source separation
2016
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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This paper proposes an extension of non-negative matrix factorization (NMF), which combines the shifted NMF model with the source-filter model. ...
Thus, we further incorporate the non-negative matrix factor deconvolution (NMFD) model into the above model to describe the filter spectrogram. ...
INTRODUCTION One major approach to monaural source separation involves applying non-negative matrix factorization (NMF) to an observed magnitude (or power) spectrogram interpreted as a non-negative matrix ...
doi:10.1109/icassp.2016.7471723
dblp:conf/icassp/NakamuraK16
fatcat:kybitvb4dzdinnryo3q6tfvslu
A Wavenet For Music Source Separation
2018
Zenodo
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In order to avoid discarding potentially useful information, we propose an end-to-end learning model based on Wavenet for music source separation. ...
Currently, most successful source separation techniques use magnitude spectrograms as input, and are therefore by default discarding part of the signal: the phase. ...
are non-negative signals. ...
doi:10.5281/zenodo.1475940
fatcat:fr627ufa2zddrghpeemhg4jzsa
Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation
2014
Frontiers in Computational Neuroscience
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Representation of the auditory space is therefore learned in a purely unsupervised way by maximizing the coding efficiency and without any task-specific constraints. ...
This results imply that efficient coding is a useful strategy for learning structures which allow for making behaviorally vital inferences about the environment. ...
Acknowledgement I would like to thank Nils Bertschinger, Timm Lochmann, Philipp Benner and Pierre Baudot for helpful discussions and proofreading of this paper. ...
doi:10.3389/fncom.2014.00026
pmid:24639644
pmcid:PMC3945936
fatcat:bavznlg3mne7nbc32kgcgyosny
End-to-End Music Source Separation: Is it Possible in the Waveform Domain?
2019
Interspeech 2019
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To avoid omitting potentially useful information, we study the viability of using end-to-end models for music source separation -which take into account all the information available in the raw audio signal ...
Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase. ...
[25] for speech denoising, and we adapt it for monaural music source separation. ...
doi:10.21437/interspeech.2019-1177
dblp:conf/interspeech/LluisPS19
fatcat:y7a6jn6ftjaxvjonjhkt7zi7ye
Generating Data To Train Convolutional Neural Networks For Classical Music Source Separation
2017
Proceedings of the SMC Conferences
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Acknowledgments The TITANX used for this research was donated by the Proceedings of the 14th Sound and Music Computing Conference, July 5-8, Espoo, Finland SMC2017-232 ...
In the case of Non-negative matrix factorization (NMF), instruments are assigned a set of timbre basis which are previously learned and kept fixed during the separation stage [6] . ...
We propose a timbre-informed and score-constrained system to train neural networks for monaural source separation of classical music mixtures. ...
doi:10.5281/zenodo.1401922
fatcat:z4bq6dksynhodglcsybhby34iu
A Multi-Phase Gammatone Filterbank for Speech Separation via TasNet
[article]
2020
arXiv
pre-print
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In this work, we investigate if the learned encoder of the end-to-end convolutional time domain audio separation network (Conv-TasNet) is the key to its recent success, or if the encoder can just as well ...
shifts. ...
And finally, at least for the convolutional time domain audio separation network (Conv-TasNet) and FurcaNext, the separation section of the network is implemented as a temporal convolutional network with ...
arXiv:1910.11615v2
fatcat:7fteorqbxzcdxk2gf2sactb5me
Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data
2013
IEEE Transactions on Audio, Speech, and Language Processing
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Index Terms-Blind source separation, clustering, convolutive mixture, multichannel, non-negative matrix factorization. ...
This paper presents new formulations and algorithms for multichannel extensions of non-negative matrix factorization (NMF). ...
NON-NEGATIVE MATRIX FACTORIZATION This section reviews the formulation and algorithm of standard single-channel NMF [17] - [19] . ...
doi:10.1109/tasl.2013.2239990
fatcat:rzextzfqfngatmp3krlxygvpqq
A Recurrent Encoder-Decoder Approach with Skip-filtering Connections for Monaural Singing Voice Separation
[article]
2018
arXiv
pre-print
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We employ recurrent neural networks and train them using prior knowledge only for the magnitude spectrum of the target source. ...
The objective of deep learning methods based on encoder-decoder architectures for music source separation is to approximate either ideal time-frequency masks or spectral representations of the target music ...
Acknowledgements We would like to thank the authors of [1] for making the results of the evaluation available. ...
arXiv:1709.00611v2
fatcat:cugmiwk37jf6zbrdtgt3vkihzq
Latent-variable decomposition based dereverberation of monaural and multi-channel signals
2010
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
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We present an algorithm to dereverberate single-and multi-channel audio recordings. ...
The proposed algorithm models the magnitude spectrograms of clean audio signals as histograms drawn from a multinomial process. ...
In [5] , a non-negative matrix factorization based method is presented that only makes assumptions about the sparsity of the distribution of energy in spectral bands, and is similar in concept to the ...
doi:10.1109/icassp.2010.5495326
dblp:conf/icassp/SinghRS10
fatcat:wq3j5spskneqvdxpzg22grbore
Speaker-Aware Monaural Speech Separation
2020
Interspeech 2020
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Predicting and applying Time-Frequency (T-F) masks on mixture signals have been successfully utilized for speech separation. ...
However, existing studies have not well utilized the identity context of a speaker for the inference of masks. In this paper, we propose a novel speaker-aware monaural speech separation model. ...
Many approaches have been developed to tackle monaural speech separation, such as computational auditory scene analysis (CASA) [6, 7] , non-negative matrix factorization (NMF) [8, 9, 10] and improved ...
doi:10.21437/interspeech.2020-2483
dblp:conf/interspeech/XuHXT020
fatcat:zg4xuzossjerbdpqx4e2dwedua
Single Channel Source Separation Using Filterbank and 2D Sparse Matrix Factorization
2013
Journal of Signal and Information Processing
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We present a novel approach to solve the problem of single channel source separation (SCSS) based on filterbank technique and sparse non-negative matrix two dimensional deconvolution (SNMF2D). ...
The proposed approach does not require training information of the sources and therefore, it is highly suited for practicality of SCSS. ...
In this paper, we proposed a novel unsupervised SCSS method based on non-negative matrix factorization approach. ...
doi:10.4236/jsip.2013.42026
fatcat:nr4aolj3ajgtrc5r4lq7fqnz3e
Audio Source Separation with Discriminative Scattering Networks
[article]
2015
arXiv
pre-print
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As study case, we use Non-Negative Matrix Factorizations (NMF) that has been widely considered in many audio application. ...
In this report we describe an ongoing line of research for solving single-channel source separation problems. ...
Non-negative matrix factorization (NMF) (Lee & Seung (1999) ), have been widely adopted in various audio processing tasks, including in particular source separation, see Smaragdis et al. (2014) for ...
arXiv:1412.7022v3
fatcat:tuadtrukhzahvf3vmhib6mq2ba
Audio Source Separation Using Deep Neural Networks
2016
Zenodo
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system's performance is comparable with other state-ofthe- art algorithms like non-negative matrix factorization, in terms of separation performance, while improving significantly on processing time. ...
This thesis presents a low latency online source separation algorithm based on convolutional neural networks. ...
Non-Negative Matrix Factorization (NMF) NMFs have been widely used for supervised source separation in the past. ...
doi:10.5281/zenodo.3755620
fatcat:girvxhgbv5cqplktyzmv22gaqu
Audio Source Separation with Discriminative Scattering Networks
[chapter]
2015
Lecture Notes in Computer Science
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As study case, we use Non-Negative Matrix Factorizations (NMF) that has been widely considered in many audio application. ...
In this report we describe an ongoing line of research for solving single-channel source separation problems. ...
Non-negative matrix factorization (NMF) (Lee & Seung (1999) ), have been widely adopted in various audio processing tasks, including in particular source separation, see Smaragdis et al. (2014) for ...
doi:10.1007/978-3-319-22482-4_30
fatcat:j7n2ibddebhk3doy5rj5fuq4dy
Sound Source Separation
[chapter]
2011
DAFX: Digital Audio Effects
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Acknowledgements The authors would like to thank Beiming Wang, Harald Viste and Mathieu Lagrange for assistance with some of the figures in this chapter. ...
Chapter 1 Sound Source Separation
Non-negative Matrix Factorization (NMF) In its simplest form, NMF attempts to minimize a cost function J = D(V ; W H) between a non-negative matrix V and a product of ...
To account for this, Virtanen [Vir04] and Smaragdis [Sma04, Sma07] introduced a Convolutive NMF approach, known as non-negative matrix factor deconvolution (NMFD). ...
doi:10.1002/9781119991298.ch14
fatcat:afqzxgyhzre2znstqgtgdh3uqi
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