EURASIP Journal on Applied Signal Processing
A classifier-based approach to score-guided source separation of musical audio
Computer Music Journal
Empirical methods to determine the number of sources in single-channel musical signals
IEEE Transactions on Audio, Speech, and Language Processing
Adaptive harmonic spectral decomposition for multiple pitch estimation
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
Discriminant feature analysis for music timbre recognition and automatic indexing
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Orchive: digitizing and analyzing orca vocalizations
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
IEEE Transactions on Neural Networks
Single-Channel mixture decomposition using bayesian harmonic models
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Audio source separation using hierarchical phase-invariant models
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
Multiple instrument mixtures source separation evaluation using instrument-dependent NMF models
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Musical audio source separation based on user-selected f0 track
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Multi-pitch Streaming of Harmonic Sound Mixtures
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
| Hi-index | 0.00 |
This article deals with the source separation problem for stereo musical mixtures using prior information about the sources (instrument names and localization). After a brief review of existing methods, we design a family of probabilistic mixture generative models combining modified positive independent subspace analysis (ISA), localization models, and segmental models (SM). We express source separation as a Bayesian estimation problem and we propose efficient resolution algorithms. The resulting separation methods rely on a variable number of cues including harmonicity, spectral envelope, azimuth, note duration, and monophony. We compare these methods on two synthetic mixtures with long reverberation. We show that they outperform methods exploiting spatial diversity only and that they are robust against approximate localization of the sources.