Event formation and separation in musical sound
Event formation and separation in musical sound
Computational auditory scene analysis: exploiting principles of perceived continuity
Speech Communication - Speech science and technology: a selection from the papers presented at the Fourth International Conference in Speech Science and Technology (SST-92)
A blackboard architecture for computational auditory scene analysis
Speech Communication
A theory and computational model of auditory monaural sound separation (stream, speech enhancement, selective attention, pitch perception, noise cancellation)
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
On the optimality of ideal binary time-frequency masks
Speech Communication
Monaural musical sound separation based on pitch and common amplitude modulation
IEEE Transactions on Audio, Speech, and Language Processing
Harmonic source separation using prestored spectra
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Separating voices in polyphonic music: a contig mapping approach
CMMR'04 Proceedings of the Second international conference on Computer Music Modeling and Retrieval
Unsupervised Single-Channel Music Source Separation by Average Harmonic Structure Modeling
IEEE Transactions on Audio, Speech, and Language Processing
Monaural speech segregation based on pitch tracking and amplitude modulation
IEEE Transactions on Neural Networks
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The problem of overlapping harmonics is particularly acute in musical sound separation and has not been addressed adequately. We propose a monaural system based on binary time-frequency masking with an emphasis on robust decisions in time-frequency regions, where harmonics from different sources overlap. Our computational auditory scene analysis system exploits the observation that sounds from the same source tend to have similar spectral envelopes. Quantitative results show that utilizing spectral similarity helps binary decision making in overlapped time-frequency regions and significantly improves separation performance.