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Underdetermined reverberant acoustic source separation using weighted full-rank nonnegative tensor models
Ahmed Al Tmeme1, W L Woo1, S S Dlay1
1School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, Tyne and Wear NE1 7RU, England, United Kingdom.
The Journal of the Acoustical Society of America
|January 3, 2016
Summary
A new K-model fusion method, K-wNTF2D, effectively separates mixed acoustic sources in reverberant environments. This unsupervised approach optimizes sparsity for improved source separation performance.
Area of Science:
- Signal Processing
- Acoustics
- Machine Learning
Background:
- Acoustic source separation in underdetermined reverberant environments is challenging.
- Existing methods struggle with complex mixtures and reverberation.
Purpose of the Study:
- To propose a novel K-model fusion method for enhanced acoustic source separation.
- To adapt the model in an unsupervised manner using hybrid algorithms.
- To incorporate variable sparsity parameters for improved source modeling.
Main Methods:
- Developed a full-rank weighted nonnegative tensor factorization 2D deconvolution (K-wNTF2D) model.
- Employed a hybrid framework of generalized expectation maximization and multiplicative update algorithms.
- Integrated variable sparsity parameters derived from Gibbs distribution for time-varying variance modeling.
- Proposed an initialization method for K-wNTF2D parameters.
Main Results:
- The proposed K-wNTF2D algorithm effectively separates mixed acoustic sources.
- Experimental results demonstrate improved performance in underdetermined reverberant environments.
- Achieved an average signal-to-distortion ratio of 3 dB.
Conclusions:
- The K-wNTF2D fusion model offers a robust solution for acoustic source separation.
- Unsupervised adaptation and sparsity optimization enhance separation accuracy.
- The method shows significant potential for real-world acoustic applications.
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