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Drum extraction in single channel audio signals using multi-layer Non negative Matrix Factor Deconvolution

Clement Laroche, Helene Papadopoulos, Matthieu Kowalski, Gael Richard
2017 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Drum extraction in single channel audio signals using multi-layer non negative matrix factor deconvolution.  ...  Our method decomposes the audio signals in sparse orthogonal components which capture the harmonic content, while the drum is represented by an extension of non negative matrix factorization which is able  ...  NON NEGATIVE MATRIX FACTOR DECONVOLUTION The NMFD is proposed as an extension of the NMF in [13] .  ... 
doi:10.1109/icassp.2017.7952115 dblp:conf/icassp/LarochePKR17 fatcat:vm6m7efzbvcfnhijyqytgl4dvq

Deep Representation-Decoupling Neural Networks for Monaural Music Mixture Separation

Zhuo Li, Hongwei Wang, Miao Zhao, Wenjie Li, Minyi Guo
., non-negative matrix factorization), or do not explicitly address the coupling and tangling of multiple sources in original input signals, hence they do not perform satisfactorily in real-world scenarios  ...  Monaural source separation (MSS) aims to extract and reconstruct different sources from a single-channel mixture, which could facilitate a variety of applications such as chord recognition, pitch estimation  ...  Conventional methods for spectrogram factorization are typically based on linear matrix factorization methods, for example, non-negative matrix factorization (NMF).  ... 
doi:10.1609/aaai.v32i1.11300 fatcat:2f6uhuetnza2vgog7hnhvllvq4

A Wavenet For Music Source Separation

Francesc Lluís Salvadó
2018 Zenodo  
As a result, the model we propose directly operates over the waveform, enabling, in that way, to consider any information available in the raw audio signal.  ...  ., is not parallelizable and hence slow), in this work we make use of a discriminative non-causal adaptation of Wavenet capable to predict more than one sample at a time, thus permitting to overcome the  ...  are non-negative signals.  ... 
doi:10.5281/zenodo.1475940 fatcat:fr627ufa2zddrghpeemhg4jzsa

Binaural Source Separation with Convolutional Neural Networks [article]

Gerard Erruz, Marius Miron, Adan Garriga
2017 Zenodo  
This masks are used to extract the different sources from the original two-channel mixture signal.  ...  It has been extended to perform separation in two-channel signals, being the first two-channel CNN joint estimation architecture.  ...  In the technical aspect: thanks to NVIDIA Corporation for the donation of the TITANX GPU, which has been deeply used to obtain the results discussed in this thesis.  ... 
doi:10.5281/zenodo.1095835 fatcat:mye3jo4smng7jbvvnpk57ysgdi

Audio Source Separation Using Deep Neural Networks

Pritish Chandna, Jordi Janer, Marius Miron
2016 Zenodo  
We try to adapt these ideas to the audio domain, focusing on low-latency extraction of 4 tracks (vocals, bass, drums and other instruments) from a single-channel (monaural) musical recording.  ...  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.  ...  Non-Negative Matrix Factorization (NMF) NMFs have been widely used for supervised source separation in the past.  ... 
doi:10.5281/zenodo.3755620 fatcat:girvxhgbv5cqplktyzmv22gaqu

Non-negative mixtures [chapter]

M.D. Plumbley, A. Cichocki, R. Bro
2010 Handbook of Blind Source Separation  
In this chapter we discuss some algorithms for the use of non-negativity constraints in unmixing problems, including positive matrix factorization (PMF) [71], non-negative matrix factorization (NMF), and  ...  For example, in the field of air quality, the amount of a particulate from a given source in a particular sample must be non-negative; and in musical audio signal processing, each musical note contributes  ...  Multi-layer NMF In multi-layer NMF the basic matrix A is replaced by a set of cascaded (factor) matrices.  ... 
doi:10.1016/b978-0-12-374726-6.00018-7 fatcat:tmy5i4jpvnb27fwjlxnpiw3dxi

Melody Extraction from Polyphonic Music by Deep Learning Approaches: A Review [article]

Gurunath Reddy M and K. Sreenivasa Rao and Partha Pratim Das
2022 arXiv   pre-print
Until recently, classical signal processing-based melody extraction methods were quite popular among melody extraction researchers.  ...  The interfering background accompaniment with the vocals makes extracting the melody from the mixture signal much more challenging.  ...  Figure 39 : 39 Figure39: Illustration of the source-filter non-negative matrix factorization decomposed salience representation [22] of the source of a music excerpt.  ... 
arXiv:2202.01078v1 fatcat:ptmc2gl455ezrburr7lvbmpxqq

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.  ...  For example, one popular approach to the single channel source separation problem is to use non-negative matrix factorization (NMF) of the short-term Fourier transform (STFT) power spectogram of the audio  ...  But, since A and S are also non-negative, we can use non-negative matrix factorization (NMF) [LS99] to approximately recover A and S from X.  ... 
doi:10.1002/9781119991298.ch14 fatcat:afqzxgyhzre2znstqgtgdh3uqi

PodcastMix: A dataset for separating music and speech in podcasts

Nicolás Schmidt, Marius Miron, Jordi Pons
2021 Zenodo  
The benchmark was performed using the Asteroid toolkit and the evaluation metrics were computed using BSSEval tool in order to measure the quality of the separations.  ...  Licensed music in these shows is frequently used, but the precision of song identification services could be a˙ected by the speakers voice in the mix.  ...  Non-Negative Matrix Factorization Non-negative Matrix Factorization is a source separation approach that performs in the time frequency domain.  ... 
doi:10.5281/zenodo.5554789 fatcat:75wg7qrslnez5buxw46mublk54

2021 Index IEEE Signal Processing Letters Vol. 28

2021 IEEE Signal Processing Letters  
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  Note that the item title is found only under the primary entry in the Author Index.  ...  ., +, LSP 2021 1380-1384 Total Variation Constrained Graph-Regularized Convex Non-Negative Matrix Factorization for Data Representation.  ... 
doi:10.1109/lsp.2022.3145253 fatcat:a3xqvok75vgepcckwnhh2mty74

Table of Contents

2021 IEEE Signal Processing Letters  
Dehak Total Variation Constrained Graph-Regularized Convex Non-Negative Matrix Factorization for Data Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Fan Single Image Super-Resolution Using Asynchronous Multi-Scale Network . . . . . . . . . . . . . . J. Ji, B. Zhong, and K.-K.  ... 
doi:10.1109/lsp.2021.3134549 fatcat:m6obtl7k7zdqvd62eo3c4tptfy

Table of Contents

2021 IEEE Signal Processing Letters  
Mateos Total Variation Constrained Graph-Regularized Convex Non-Negative Matrix Factorization for Data Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Fan Single Image Super-Resolution Using Asynchronous Multi-Scale Network . . . . . . . . . . . . . . J. Ji, B. Zhong, and K.-K.  ... 
doi:10.1109/lsp.2021.3134551 fatcat:ab4b4tb5rrcu5cq6aifdekrizq

The Sound of Motions [article]

Hang Zhao, Chuang Gan, Wei-Chiu Ma, Antonio Torralba
2019 arXiv   pre-print
It exploits the inherent coherence of audio-visual signals from a large quantities of unlabeled videos.  ...  Quantitative and qualitative evaluations show that comparing to previous models that rely on visual appearance cues, our motion based system improves performance in separating musical instrument sounds  ...  Algorithms based on Non-negative Matrix Factorization (NMF) [46, 10, 42] were the major solutions to this problem.  ... 
arXiv:1904.05979v1 fatcat:tbjwa6uoorgrnouke5tjcyryny

TWO!EARS Deliverable D4.3 - Final Integration and Evaluation Report (WP4: Active listening, feedback loops & integration of cross-modal information; FP7-ICT-2013-C TWO!EARS FET-Open Project 618075)

Jens Blauert, Benjamin Cohen-L'hyver, Chungeun Ryan Kim, Hagen Wierstorf, Youssef Kashef, Johannes Mohr, Jonas Braasch, Ning Ma, Sylvain Argentieri, Thomas Walther, Gabriel Bustamante, Patrick Danès (+4 others)
2016 Zenodo  
In consequence, auditory worlds are rarely purely auditive but to a certain extent multimodal. In the context of Two!  ...  Turn-to reflex Sensorimotor feedback Signal-adapted control of ear filters Head turning modified by temporal salience Head turning modified by spatial salience Localization accuracy The Precedence effec  ...  Multi-source binaural speech localization is presently still a challenge, with most published literature limiting models to extracting a single speaker in the presence of non-speech stimuli.  ... 
doi:10.5281/zenodo.2591202 fatcat:ymsgbgr64vgo5fyvbapecn55l4

Artificial Musical Intelligence: A Survey [article]

Elad Liebman, Peter Stone
2020 arXiv   pre-print
Computers have been used to analyze and create music since they were first introduced in the 1950s and 1960s.  ...  Beginning in the late 1990s, the rise of the Internet and large scale platforms for music recommendation and retrieval have made music an increasingly prevalent domain of machine learning and artificial  ...  nonnegative matrix factorization to recover structure in audio signals [192] .  ... 
arXiv:2006.10553v1 fatcat:2j6i27wrsfawpgcr2unxdgngd4
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