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Found 3 papers, 2 papers with code
A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications
Auditory models are commonly used as feature extractors for automatic speech-recognition systems or as front-ends for robotics, machine-hearing and hearing-aid applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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iSEGAN: Improved Speech Enhancement Generative Adversarial Networks
Popular neural network-based speech enhancement systems operate on the magnitude spectrogram and ignore the phase mismatch between the noisy and clean speech signals.
Machines hear better when they have ears
Deep-neural-network (DNN) based noise suppression systems yield significant improvements over conventional approaches such as spectral subtraction and non-negative matrix factorization, but do not generalize well to noise conditions they were not trained for.
