OpenBliSSART: Design and evaluation of a research toolkit for Blind Source Separation in Audio Recognition Tasks
Acoustics, Speech and Signal Processing(2011)
摘要
We describe and evaluate our toolkit openBliSSART (open-source Blind Source Separation for Audio Recognition Tasks), which is the C++ framework and toolbox that we have successfully used in a multiplicity of research on blind audio source separation and feature extraction. To our knowledge, it provides the first open-source implementation of a widely applicable algorithmic framework based on non-negative matrix factorization (NMF), including several pre processing, factorization, and signal reconstruction algorithms for monaural signals. Apart from blind source separation using super vised and unsupervised NMF, we show how the framework is useful for the increasingly popular audio feature extraction methods by NMF. Furthermore, we point out a numerical optimization for NMF, and show that NMF source separation in real-time on a desktop PC is feasible with our implementation. We conclude with an evaluation of our toolkit on supervised speaker separation, demonstrating how our algorithmic framework allows to tune the real-time factors to the desired perceptual quality.
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关键词
C++ language,blind source separation,feature extraction,matrix decomposition,optimisation,public domain software,signal reconstruction,speaker recognition,C++,NMF,audio recognition,blind source separation,feature extraction,nonnegative matrix factorization,numerical optimization,open source,openBliSSART,signal reconstruction,supervised speaker separation,toolkit,Blind Source Separation,Instrument Separation,Real-Time Signal Processing,Speech Enhancement
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