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Found 7 papers, 2 papers with code
Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learning
Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results.
Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separation
In the spectrogram of a mixture of singing voices and music signals, there is usually more information about the voice in the low frequency bands than the high frequency bands.
Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks
Current performance evaluation for audio source separation depends on comparing the processed or separated signals with reference signals.
Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders
Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals.
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation
In deep neural networks with convolutional layers, each layer typically has fixed-size/single-resolution receptive field (RF).
Single Channel Audio Source Separation using Convolutional Denoising Autoencoders
Each CDAE is trained to separate one source and treats the other sources as background noise.
Sound 68T01 H.5.5; I.5; I.2.6; I.4.3
Deep neural networks for single channel source separation
In the training stage, the training data for the source signals are used to train a DNN.
