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  1. Transactions on Audio, Speech and Language Processing (TASLP)
  2. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 24
  3. Issue 9, September 2016
  4. Projection-based demixing of spatial audio
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IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 24
Issue 9, September 2016
Combination of language models for word prediction: an exponential approach
An expectation-maximization algorithm for multimicrophone speech dereverberation and noise reduction with coherence matrix estimation
Signal-dependent spatial filtering based on weighted-orthogonal beamformers in the spherical harmonic domain
Semi-supervised acoustic model training by discriminative data selection from multiple ASR systems' hypotheses
Reverse engineering the amen break: score-informed separation and restoration applied to drum recordings
Reduced-order robust superdirective beamforming with uniform linear microphone arrays
Projection-based demixing of spatial audio
Over-determined source separation and localization using distributed microphones
Relation classification via modeling augmented dependency paths
Maximum likelihood PSD estimation for speech enhancement in reverberation and noise
Computationally efficient and noise robust DOA and pitch estimation
Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization
Similarity search of acted voices for automatic voice casting
Issue 8, August 2016
Issue 7, July 2016
Issue 6, June 2016
Issue 5, May 2016
Issue 4, April 2016
Issue 3, March 2016
Issue 2, February 2016
Issue 1, January 2016
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 23
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 22

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Projection-based demixing of spatial audio

Content Provider ACM Digital Library
Author Liutkus, Antoine Badeau, Roland Fitzgerald, Derry
Copyright Year 2016
Abstract We propose a method to unmix multichannel audio signals into their different constitutive spatial objects. To achieve this, we characterize an audio object through both a spatial and a spectro-temporal modeling. The particularity of the spatial model we pick is that it neither assumes an object has only one underlying source point, nor does it attempt to model the complex room acoustics. Instead, it focuses on a listener perspective, and takes each object as the superposition of many contributions with different incoming directions and interchannel delays. Our spectro-temporal probabilistic model is based on the recently proposed α-harmonisable processes, which are adequate for signals with large dynamics, such as audio. Then, the main originality of this paper is to provide a new way to estimate and exploit interchannel dependences of an object for the purpose of demixing. In the Gaussian α = 2 case, previous research focused on covariance structures. This approach is no longer valid for α < 2 where covariances are not defined. Instead, we show how simple linear combinations of the mixture channels can be used to learn the model parameters, and the method we propose consists in pooling the estimates based on many projections to correctly account for the original multichannel audio. Intuitively, each such downmix of the mixture provides a new perspective where some objects are canceled or enhanced. Finally, we also explain how to recover the different spatial audio objects when all parameters have been computed. Performance of the method is illustrated on the separation of stereophonic music signals.
Starting Page 1556
Ending Page 1568
Page Count 13
File Format PDF
ISSN 23299290
e-ISSN 23299304
Volume Number 24
Issue Number 9
Journal IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-09-01
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Musical source separation Nonnegative matrix factorization (NMF) Probabilistic models Source separation
Content Type Text
Resource Type Article
Subject Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology
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