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Found 3 papers, 2 papers with code
Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding
This interpretability also provides principled initializations that enable faster training and convergence to better solutions compared to conventional random initialization.
Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery
Recurrent neural networks (RNNs) are powerful and effective for processing sequential data.
Enhancement and Recognition of Reverberant and Noisy Speech by Extending Its Coherence
Our approach centers around using a single-channel minimum mean-square error log-spectral amplitude (MMSE-LSA) estimator proposed by Habets, which scales coefficients in a time-frequency domain to suppress noise and reverberation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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