TY - GEN
T1 - OpenBliSSART
T2 - 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
AU - Weninger, Felix
AU - Lehmann, Alexander
AU - Schuller, Björn
PY - 2011
Y1 - 2011
N2 - 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 preprocessing, factorization, and signal reconstruction algorithms for monaural signals. Apart from blind source separation using supervised 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.
AB - 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 preprocessing, factorization, and signal reconstruction algorithms for monaural signals. Apart from blind source separation using supervised 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.
KW - Blind Source Separation
KW - Instrument Separation
KW - Real-Time Signal Processing
KW - Speech Enhancement
UR - http://www.scopus.com/inward/record.url?scp=80051618211&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2011.5946809
DO - 10.1109/ICASSP.2011.5946809
M3 - Conference contribution
AN - SCOPUS:80051618211
SN - 9781457705397
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1625
EP - 1628
BT - 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Y2 - 22 May 2011 through 27 May 2011
ER -