Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Latent variable framework for modeling and separating single-channel acoustic sources
Latent variable framework for modeling and separating single-channel acoustic sources
User guided audio selection from complex sound mixtures
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Anechoic Blind Source Separation Using Wigner Marginals
The Journal of Machine Learning Research
Online PLCA for real-time semi-supervised source separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Real-Time speech separation by semi-supervised nonnegative matrix factorization
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
A non-negative approach to language informed speech separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Synthesis of a video of performers appearing to play user-specified band music
ACM SIGGRAPH 2012 Posters
Journal of Signal Processing Systems
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In this paper we describe a methodology for model-based single channel separation of sounds. We present a sparse latent variable model that can learn sounds based on their distribution of time/ frequency energy. This model can then be used to extract known types of sounds from mixtures in two scenarios. One being the case where all sound types in the mixture are known, and the other being being the case where only the target or the interference models are known. The model we propose has close ties to non-negative decompositions and latent variable models commonly used for semantic analysis.