Missing feature mask generation in BSS outputs using pitch frequency

Shabani, Hasti; Kahaei, Mohammad Hossein

doi:10.1109/icdsp.2011.6004930

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…For the AV16.3 corpus since only the real microphone (mixture) measurements are available and we do not have access to the original speech sources, the performance can not be evaluated using [34]. We thus use pitch as a feature to compare separation performance [35], considering the fact that speech sections at different time slots have different pitches [36] and given that the original sources do not have substantially overlapping pitch characteristics. The pitch difference is given as,…”

Section: ) Evaluation Of Separation Performancementioning

confidence: 99%

Video-Aided Model-Based Source Separation in Real Reverberant Rooms

Khan

Naqvi

ur-Rehman

et al. 2013

IEEE Trans. Audio Speech Lang. Process.

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Section: ) Evaluation Of Separation Performancementioning

confidence: 99%

Video-Aided Model-Based Source Separation in Real Reverberant Rooms

Khan

Naqvi

ur-Rehman

et al. 2013

IEEE Trans. Audio Speech Lang. Process.

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“…In order to evaluate the results objectively, the features of the separated signals should be used. Pitch information is one of the http://asp.eurasipjournals.com/content/2012/1/183 features which can help to evaluate the separation performance, because different speech sections at different time slots have different pitches [31] provided that the original sources do not have substantially overlapping pitch characteristics. We adopt the sawtooth waveform inspired pitch estimator (SWIPE) method [32], which has better performance compared with traditional pitch estimators.…”

Section: Pitch Based Evaluation For Real Recordingsmentioning

confidence: 99%

Audio video based fast fixed-point independent vector analysis for multisource separation in a room environment

Liang

Naqvi

Chambers

2012

EURASIP J. Adv. Signal Process.

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Fast fixed-point independent vector analysis (FastIVA) is an improved independent vector analysis (IVA) method, which can achieve faster and better separation performance than original IVA. As an example IVA method, it is designed to solve the permutation problem in frequency domain independent component analysis by retaining the higher order statistical dependency between frequencies during learning. However, the performance of all IVA methods is limited due to the dimensionality of the parameter space commonly encountered in practical frequency-domain source separation problems and the spherical symmetry assumed with the source model. In this article, a particular permutation problem encountered in using the FastIVA algorithm is highlighted, namely the block permutation problem. Therefore a new audio video based fast fixed-point independent vector analysis algorithm is proposed, which uses video information to provide a smart initialization for the optimization problem. The method cannot only avoid the ill convergence resulting from the block permutation problem but also improve the separation performance even in noisy and high reverberant environments. Different multisource datasets including the real audio video corpus AV16.3 are used to verify the proposed method. For the evaluation of the separation performance on real room recordings, a new pitch based evaluation criterion is also proposed.

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An Audio-Video Based IVA Algorithm for Source Separation and Evaluation on the AV16.3 Corpus

Liang

Chambers

2012

Latent Variable Analysis and Signal Separation

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Missing feature mask generation in BSS outputs using pitch frequency

Cited by 3 publications

References 9 publications

Video-Aided Model-Based Source Separation in Real Reverberant Rooms

Video-Aided Model-Based Source Separation in Real Reverberant Rooms

Audio video based fast fixed-point independent vector analysis for multisource separation in a room environment

An Audio-Video Based IVA Algorithm for Source Separation and Evaluation on the AV16.3 Corpus

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