Hearing aid Research Data Set for Acoustic Environment Recognition
| Autor: | Andreas Huwel, Kamil Adiloglu, Jörg-Hendrik Bach |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Hearing aid
Signal processing Artificial neural network Computer science business.industry medicine.medical_treatment 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Set (abstract data type) Data set 030507 speech-language pathology & audiology 03 medical and health sciences Robustness (computer science) 0202 electrical engineering electronic engineering information engineering medicine Active listening Artificial intelligence 0305 other medical science business Binaural recording computer |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp40776.2020.9053611 |
| Popis: | State-of-the-art hearing aids (HA) are limited in recognizing acoustic environments. Much effort is spent on research to improve listening experience for HA users in every acoustic situation. There is, however, no dedicated public database to train acoustic environment recognition algorithms with a specific focus on HA applications accounting for their requirements. Existing acoustic scene classification databases are inappropriate for HA signal processing. In this work we propose a novel, binaural HA acoustic environment recognition data set (HEAR-DS) suitable for the environment recognition needs of HAs. We present the details about each individual environment provided within the data set. To show separability of these acoustic environments we trained a group of deep neural network-based classifiers which vary in complexity. The obtained classification accuracies provide a reliable indicator about the validity and separability of the provided data set. Finally, as we do not aim at providing the best possible neural network architecture to perform such a classification, but propose solely a novel data set, further research is needed to streamline such networks and optimize them for robustness, real-time and limited computational capability to fit into modern HAs. |
| Databáze: | OpenAIRE |
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