2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-11.3
Paper Title COMPUTATIONALLY EFFICIENT DNN-BASED APPROXIMATION OF AN AUDITORY MODEL FOR APPLICATIONS IN SPEECH PROCESSING
Authors Anil Nagathil, Florian Göbel, Alexandru Nelus, Ruhr-Universität Bochum, Germany; Ian C. Bruce, McMaster University, Canada
SessionAUD-11: Auditory Modeling and Hearing Instruments
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-AMHI] Auditory Modeling and Hearing Instruments
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Abstract Computational models of the auditory periphery are important tools for understanding mechanisms of normal and impaired hearing and for developing advanced speech and audio processing algorithms. However, the simulation of accurate neural representations entails a high computational effort. This prevents the use of auditory models in applications with real-time requirements and the design of speech enhancement algorithms based on efficient bio-inspired optimization criteria. Hence, in this work we propose and evaluate DNN-based approximations of a state-of-the-art auditory model. The DNN models yield accurate neurogram predictions for previously unseen speech signals with processing times shorter than signal duration, thus indicating their potential to be applied in real-time.