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Shifted and convolutive source-filter non-negative matrix factorization for monaural audio source separation

Tomohiko Nakamura, Hirokazu Kameoka
2016 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
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

Francesc Lluís Salvadó
2018 Zenodo  
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

Wiktor Młynarski
2014 Frontiers in Computational Neuroscience  
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?

Francesc Lluís, Jordi Pons, Xavier Serra
2019 Interspeech 2019  
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

Marius Miron, Jordi Janer, Emilia Gómez
2017 Proceedings of the SMC Conferences  
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]

David Ditter, Timo Gerkmann
2020 arXiv   pre-print
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

Hiroshi Sawada, Hirokazu Kameoka, Shoko Araki, Naonori Ueda
2013 IEEE Transactions on Audio, Speech, and Language Processing  
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]

Stylianos Ioannis Mimilakis, Konstantinos Drossos, Tuomas Virtanen, Gerald Schuller
2018 arXiv   pre-print
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

Rita Singh, Bhiksha Raj, Paris Smaragdis
2010 2010 IEEE International Conference on Acoustics, Speech and Signal Processing  
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

Jiahao Xu, Kun Hu, Chang Xu, Duc Chung Tran, Zhiyong Wang
2020 Interspeech 2020  
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

Xiangying Lu, Bin Gao, Li Chin Khor, Wai Lok Woo, Satnam Dlay, Wingkuen Ling, Cheng S. Chin
2013 Journal of Signal and Information Processing  
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]

Pablo Sprechmann, Joan Bruna, Yann LeCun
2015 arXiv   pre-print
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

Pritish Chandna, Jordi Janer, Marius Miron
2016 Zenodo  
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]

Pablo Sprechmann, Joan Bruna, Yann LeCun
2015 Lecture Notes in Computer Science  
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]

G. Evangelista, S. Marchand, M. D. Plumbley, E. Vincent
2011 DAFX: Digital Audio Effects  
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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