Information Retrieval
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Nonsmooth Nonnegative Matrix Factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonnegative matrix factorization with Gaussian process priors
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
IEEE Transactions on Audio, Speech, and Language Processing
A generative model for music transcription
IEEE Transactions on Audio, Speech, and Language Processing
A connectionist approach to automatic transcription of polyphonic piano music
IEEE Transactions on Multimedia
Algorithms for nonnegative independent component analysis
IEEE Transactions on Neural Networks
Optimal filter designs for separating and enhancing periodic signals
IEEE Transactions on Signal Processing
IEEE Transactions on Neural Networks
Bayesian non-negative matrix factorization with learned temporal smoothness priors
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Supervised dictionary learning for music genre classification
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
A convergent algorithm for orthogonal nonnegative matrix factorization
Journal of Computational and Applied Mathematics
Automatic music transcription: challenges and future directions
Journal of Intelligent Information Systems
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This paper presents theoretical and experimental results about constrained non-negative matrix factorization (NMF) in a Bayesian framework. A model of superimposed Gaussian components including harmonicity is proposed, while temporal continuity is enforced through an inverse-Gamma Markov chain prior. We then exhibit a space-alternating generalized expectation-maximization (SAGE) algorithm to estimate the parameters. Computational time is reduced by initializing the system with an original variant of multiplicative harmonic NMF, which is described as well. The algorithm is then applied to perform polyphonic piano music transcription. It is compared to other state-of-the-art algorithms, especially NMF-based. Convergence issues are also discussed on a theoretical and experimental point of view. Bayesian NMF with harmonicity and temporal continuity constraints is shown to outperform other standard NMF-based transcription systems, providing a meaningful mid-level representation of the data. However, temporal smoothness has its drawbacks, as far as transients are concerned in particular, and can be detrimental to transcription performance when it is the only constraint used. Possible improvements of the temporal prior are discussed.