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Reconstructing individual monophonic instruments from musical mixtures using scene completion
"... Monaural sound source separation is the process of separating sound sources from a single channel mixture. In mixtures of pitched musical instruments, the problem of overlapping harmonics poses a significant challenge to source separation and reconstruction. One standard method to resolve overlapp ..."
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Monaural sound source separation is the process of separating sound sources from a single channel mixture. In mixtures of pitched musical instruments, the problem of overlapping harmonics poses a significant challenge to source separation and reconstruction. One standard method to resolve overlapped harmonics is based on the assumption that harmonics of the same source have correlated amplitude envelopes: common amplitude modulation (CAM). Based on CAM, overlapped harmonics are approximated using the amplitude envelope from the nonoverlapped harmonics of the same note. CAM assumes nonoverlapped harmonics from the same noteare available and have similar amplitude envelopes to the overlapped harmonics. This is not always the case. A technique is proposed for harmonic temporal envelope estimation based on the idea of scene completion. The system learns the harmonic envelope for each instruments notes from the nonoverlapped harmonics of other notes played by that instrument, wherever they
HIGHRESOLUTION SINUSOIDAL ANALYSIS FOR RESOLVING HARMONIC COLLISIONS IN MUSIC AUDIO SIGNAL PROCESSING BY
"... Many music signals can largely be considered an additive combination of multiple sources, such as musical instruments or voice. If the musical sources are pitched instruments, the spectra they produce are predominantly harmonic, and are thus well suited to an additive sinusoidal model. However, due ..."
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Many music signals can largely be considered an additive combination of multiple sources, such as musical instruments or voice. If the musical sources are pitched instruments, the spectra they produce are predominantly harmonic, and are thus well suited to an additive sinusoidal model. However, due to resolution limits inherent in timefrequency analyses, when the harmonics of multiple sources occupy equivalent timefrequency regions, their individual properties are additively combined in the timefrequency representation of the mixed signal. Any such timefrequency point in a mixture where multiple harmonics overlap produces a single observation from which the contributions owed to each of the individual harmonics cannot be trivially deduced. These overlaps are referred to as overlapping partials or harmonic collisions. If one wishes to infer some information about individual sources in music mixtures, the information carried in regions where collided harmonics exist becomes unreliable due to interference from other sources. This inter
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"... With the strong growth of assistive and personal listening devices, natural sound rendering over headphones is becoming a necessity for prolonged listening in multimedia and virtual reality applications. The aim of natural sound rendering is to recreate the sound scenes with the spatial and timbral ..."
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With the strong growth of assistive and personal listening devices, natural sound rendering over headphones is becoming a necessity for prolonged listening in multimedia and virtual reality applications. The aim of natural sound rendering is to recreate the sound scenes with the spatial and timbral quality as natural as possible, so as to achieve a truly immersive listening experience. However, rendering natural sound over headphones encounters many challenges. This tutorial paper presents signal processing techniques to tackle these challenges to assist human listening.
Extended Nonnegative Tensor Factorisation models for Musical Sound Source Separation
"... Recently, shift invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments. However, existing algorithms require the use of logfrequency spectrograms to allow shift invariance in frequency which causes problems when attemp ..."
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Recently, shift invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments. However, existing algorithms require the use of logfrequency spectrograms to allow shift invariance in frequency which causes problems when attempting to resynthesise the separated sources. Further, it is difficult to impose harmonicity constraints on the recovered basis functions. This paper proposes a new additive synthesisbased approach which allows the use of linearfrequency spectrograms as well as imposing strict harmonic constraints, resulting in an improved model. Further, these additional constraints allow the addition of a source filter model to the factorisation framework, and an extended model which is capable of separating mixtures of pitched and percussive instruments simultaneously. 1
doi:10.1155/2009/785152 Research Article Bayesian Inference for Nonnegative Matrix Factorisation Models
"... We describe nonnegative matrix factorisation (NMF) with a KullbackLeibler (KL) error measure in a statistical framework, with a hierarchical generative model consisting of an observation and a prior component. Omitting the prior leads to the standard KLNMF algorithms as special cases, where maximu ..."
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We describe nonnegative matrix factorisation (NMF) with a KullbackLeibler (KL) error measure in a statistical framework, with a hierarchical generative model consisting of an observation and a prior component. Omitting the prior leads to the standard KLNMF algorithms as special cases, where maximum likelihood parameter estimation is carried out via the ExpectationMaximisation (EM) algorithm. Starting from this view, we develop full Bayesian inference via variational Bayes or Monte Carlo. Our construction retains conjugacy and enables us to develop more powerful models while retaining attractive features of standard NMF such as monotonic convergence and easy implementation. We illustrate our approach on model order selection and image reconstruction. Copyright © 2009 Ali Taylan Cemgil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
entitled “Probabilistic Modelling of Musical Audio for Machine Listening”).Bayesian Inference for Nonnegative Matrix Factorisation Models ∗
, 2008
"... We describe nonnegative matrix factorisation (NMF) with a KullbackLeibler error measure in a statistical framework, with a hierarchical generative model consisting of an observation and a prior component. Omitting the prior leads to standard NMF algorithms as special cases, where maximum likelihoo ..."
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We describe nonnegative matrix factorisation (NMF) with a KullbackLeibler error measure in a statistical framework, with a hierarchical generative model consisting of an observation and a prior component. Omitting the prior leads to standard NMF algorithms as special cases, where maximum likelihood parameter estimation is carried out via the ExpectationMaximisation (EM) algorithm. Starting from this view, we develop Bayesian extensions that facilitate more powerful modelling and allow full Bayesian inference via variational Bayes or Monte Carlo. Our construction retains conjugacy and enables us to develop models that fit better to real data while retaining attractive features of standard NMF such as fast convergence and easy implementation. We illustrate our approach on model order selection and image reconstruction. 1
doi:10.1155/2009/130567 Research Article Musical Sound Separation Based on Binary TimeFrequency Masking
"... The problem of overlapping harmonics is particularly acute in musical sound separation and has not been addressed adequately. We propose a monaural system based on binary timefrequency masking with an emphasis on robust decisions in timefrequency regions, where harmonics from different sources over ..."
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The problem of overlapping harmonics is particularly acute in musical sound separation and has not been addressed adequately. We propose a monaural system based on binary timefrequency masking with an emphasis on robust decisions in timefrequency regions, where harmonics from different sources overlap. Our computational auditory scene analysis system exploits the observation that sounds from the same source tend to have similar spectral envelopes. Quantitative results show that utilizing spectral similarity helps binary decision making in overlapped timefrequency regions and significantly improves separation performance. Copyright © 2009 Y. Li and D. Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
On Inpainting the Adress Algorithm
"... Abstract — The Adress algorithm has been demonstrated to be capable of separating sound sources from instantaneous linear mixtures, provided that the sources have a unique pan position in the stereo field. However, a shortcoming of the Adress algorithm is that all timefrequency bins outside of the ..."
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Abstract — The Adress algorithm has been demonstrated to be capable of separating sound sources from instantaneous linear mixtures, provided that the sources have a unique pan position in the stereo field. However, a shortcoming of the Adress algorithm is that all timefrequency bins outside of the chosen azimuth range are set to zero, resulting in audible artifacts in the resynthesised sound. Here we show that an inpainting algorithm based on NMF is capable of estimating these missing values and improves on the results obtained using Adress only.