Audio source separation using a redundant library of source spectral bases for non-negative tensor factorization

Fakhry, Mahmoud; Svaizer, Piergiorgio; Omologo, Maurizio

doi:10.1109/icassp.2015.7177970

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…This framework and its variants have been applied for the separation of noisy speech [15], [16], movie soundtracks [17], [18], and music [11]. They have also been used as a preprocessing step for automatic speech recognition [19], musical instrument recognition [5], singing voice detection [20], and audio declipping [21].…”

Section: Introductionmentioning

confidence: 99%

Variational Bayesian Inference for Source Separation and Robust Feature Extraction

Adiloğlu

2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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We consider the task of separating and classifying individual sound sources mixed together. The main challenge is to achieve robust classification despite residual distortion of the separated source signals. A promising paradigm is to estimate the uncertainty about the separated source signals and to propagate it through the subsequent feature extraction and classification stages. We argue that variational Bayesian (VB) inference offers a mathematically rigorous way of deriving uncertainty estimators, which contrasts with state-of-theart estimators based on heuristics or on maximum likelihood (ML) estimation. We propose a general VB source separation algorithm, which makes it possible to jointly exploit spatial and spectral models of the sources. This algorithm achieves 6% and 5% relative error reduction compared to ML uncertainty estimation on the CHiME noise-robust speaker identification and speech recognition benchmarks, respectively, and it opens the way for more complex VB approximations of uncertainty.

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Section: Introductionmentioning

confidence: 99%

Variational Bayesian Inference for Source Separation and Robust Feature Extraction

Adiloğlu

2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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Estimation of the spatial information in Gaussian model based audio source separation using weighted spectral bases

Fakhry

Svaizer

Omologo

2016

2016 24th European Signal Processing Conference (EUSIPCO)

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Spectro-temporal Filtering based on The Beta-divergence for Speech Separation using Nonnegative Matrix Factorization

Fakhry¹

2021

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

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Audio source separation using a redundant library of source spectral bases for non-negative tensor factorization

Cited by 4 publications

References 18 publications

Variational Bayesian Inference for Source Separation and Robust Feature Extraction

Variational Bayesian Inference for Source Separation and Robust Feature Extraction

Estimation of the spatial information in Gaussian model based audio source separation using weighted spectral bases

Spectro-temporal Filtering based on The Beta-divergence for Speech Separation using Nonnegative Matrix Factorization

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