most citedHow to train your ears: Auditory-model emulation for large-dynamic-range inputs and mild-to-severe hearing losses

4 citations · 7 across the 7 of their papers we have counts for

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eess.AS2024

Noise-Robust Hearing Aid Voice Control

Iván López-Espejo, Eros Roselló, Amin Edraki +2

Advancing the design of robust hearing aid (HA) voice control is crucial to increase the HA use rate among hard of hearing people as well as to improve HA users' experience. In thi…

eess.AS2024

Deep low-latency joint speech transmission and enhancement over a gaussian channel

Mohammad Bokaei, Jesper Jensen, Simon Doclo +1

Ensuring intelligible speech communication for hearing assistive devices in low-latency scenarios presents significant challenges in terms of speech enhancement, coding and transmi…

eess.AS20244 cited

How to train your ears: Auditory-model emulation for large-dynamic-range inputs and mild-to-severe hearing losses

Peter Leer, Jesper Jensen, Zheng-Hua Tan +2

Advanced auditory models are useful in designing signal-processing algorithms for hearing-loss compensation or speech enhancement. Such auditory models provide rich and detailed de…

eess.AS20242 cited

Binaural Speech Enhancement Using Deep Complex Convolutional Transformer Networks

Vikas Tokala, Eric Grinstein, Mike Brookes +3

Studies have shown that in noisy acoustic environments, providing binaural signals to the user of an assistive listening device may improve speech intelligibility and spatial aware…

eess.AS2024

On Speech Pre-emphasis as a Simple and Inexpensive Method to Boost Speech Enhancement

Iván López-Espejo, Aditya Joglekar, Antonio M. Peinado +1

Pre-emphasis filtering, compensating for the natural energy decay of speech at higher frequencies, has been considered as a common pre-processing step in a number of speech process…