What is the goal of sensory coding?
Neural Computation
Relation between PLSA and NMF and implications
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Speech enhancement based on a priori signal to noise estimation
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
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We propose an algorithm for single-channel speech enhancement that requires no pre-trained models - neither speech nor noise models - using non-negative spectrogram decomposition with sparsity constraints. To this end, before staring the EM algorithm for spectrogram decomposition, we divide the spectral basis vectors into two disjoint groups - speech and noise groups - and impose sparsity constraints only on those in the speech group as we update the parameters. After the EM algorithm converges, the proposed algorithm successfully separates speech from noise, and no post-processing is required for speech reconstruction. Experiments with various types of real-world noises show that the proposed algorithm achieves performance significantly better than other classical algorithms or comparable to the spectrogram decomposition method using pre-trained noise models.