Discrete-time signal processing
Discrete-time signal processing
Computational auditory scene analysis
Computational auditory scene analysis
Prediction-driven computational auditory scene analysis
Prediction-driven computational auditory scene analysis
Independent Component Analysis: A Tutorial Introduction
Independent Component Analysis: A Tutorial Introduction
Robust speech separation using time-frequency masking
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Musical source separation using time-frequency source priors
IEEE Transactions on Audio, Speech, and Language Processing
Monaural speech segregation based on pitch tracking and amplitude modulation
IEEE Transactions on Neural Networks
Stereo audio source separation based on time--frequency masking and multilevel thresholding
Digital Signal Processing
Graph-Based Representation of Symbolic Musical Data
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Monaural musical sound separation based on pitch and common amplitude modulation
IEEE Transactions on Audio, Speech, and Language Processing
Modeling perceptual similarity of audio signals for blind source separation evaluation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
A watermarking-based method for informed source separation of audio signals with a single sensor
IEEE Transactions on Audio, Speech, and Language Processing
Single channel music sound separation based on spectrogram decomposition and note classification
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
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Recent work in blind source separation applied to anechoic mixtures of speech allows for improved reconstruction of sources that rarely overlap in a time-frequency representation. While the assumption that speech mixtures do not overlap significantly in time-frequency is reasonable, music mixtures rarely meet this constraint, requiring new approaches. We introduce a method that uses spatial cues from anechoic, stereo music recordings and assumptions regarding the structure of musical source signals to effectively separate mixtures of tonal music. We discuss existing techniques to create partial source signal estimates from regions of the mixture where source signals do not overlap significantly. We use these partial signals within a new demixing framework, in which we estimate harmonic masks for each source, allowing the determination of the number of active sources in important time frequency frames of the mixture.We then propose a method for distributing energy from time-frequency frames of the mixture to multiple source signals. This allows dealing with mixtures that contain time-frequency frames in whichmultiple harmonic sources are active without requiring knowledge of source characteristics.