Mixtures of Gamma Priors for Non-negative Matrix Factorization Based Speech Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Monaural musical sound separation based on pitch and common amplitude modulation
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Signal Processing
Adaptive harmonic spectral decomposition for multiple pitch estimation
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Source/filter model for unsupervised main melody extraction from polyphonic audio signals
IEEE Transactions on Audio, Speech, and Language Processing
Re-texturing the sonic environment
Proceedings of the 5th Audio Mostly Conference: A Conference on Interaction with Sound
Automatic recognition of lyrics in singing
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on atypical speech
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
IEEE Transactions on Neural Networks
Correlation-based amplitude estimation of coincident partials in monaural musical signals
EURASIP Journal on Audio, Speech, and Music 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
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Pattern induction and matching in music signals
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Sparse nonnegative matrix factorization with ℓ0-constraints
Neurocomputing
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
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
NMF-based environmental sound source separation using time-variant gain features
Computers & Mathematics with Applications
Journal of Signal Processing Systems
SMIAE '12 Proceedings of the 1st Workshop on Speech and Multimodal Interaction in Assistive Environments
Digital Signal Processing
Modelling non-stationary noise with spectral factorisation in automatic speech recognition
Computer Speech and Language
Information Sciences: an International Journal
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An unsupervised learning algorithm for the separation of sound sources in one-channel music signals is presented. The algorithm is based on factorizing the magnitude spectrogram of an input signal into a sum of components, each of which has a fixed magnitude spectrum and a time-varying gain. Each sound source, in turn, is modeled as a sum of one or more components. The parameters of the components are estimated by minimizing the reconstruction error between the input spectrogram and the model, while restricting the component spectrograms to be nonnegative and favoring components whose gains are slowly varying and sparse. Temporal continuity is favored by using a cost term which is the sum of squared differences between the gains in adjacent frames, and sparseness is favored by penalizing nonzero gains. The proposed iterative estimation algorithm is initialized with random values, and the gains and the spectra are then alternatively updated using multiplicative update rules until the values converge. Simulation experiments were carried out using generated mixtures of pitched musical instrument samples and drum sounds. The performance of the proposed method was compared with independent subspace analysis and basic nonnegative matrix factorization, which are based on the same linear model. According to these simulations, the proposed method enables a better separation quality than the previous algorithms. Especially, the temporal continuity criterion improved the detection of pitched musical sounds. The sparseness criterion did not produce significant improvements