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Found 4 papers, 2 papers with code
A Neural-Network Framework for the Design of Individualised Hearing-Loss Compensation
Sound processing in the human auditory system is complex and highly non-linear, whereas hearing aids (HAs) still rely on simplified descriptions of auditory processing or hearing loss to restore hearing.
A comparative study of eight human auditory models of monaural processing
A number of auditory models have been developed using diverging approaches, either physiological or perceptual, but they share comparable stages of signal processing, as they are inspired by the same constitutive parts of the auditory system.
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)
+1
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.
