Abstract
Audio based event recognition becomes quite challenging in real world noisy environments. To alleviate the noise issue, time-frequency mask based feature enhancement methods have been proposed. While these methods with fixed filter settings have been shown to be effective in familiar noise backgrounds, they become brittle when exposed to unexpected noise. To address the unknown noise problem, we develop an approach based on dynamic filter generation learning. In particular, we propose a dual stage dynamic filter generator networks that can be trained to generate a time-frequency mask specifically created for each input audio. Two alternative approaches of training the mask generator network are developed for feature enhancements in high noise environments. Our proposed method shows improved performance and robustness in both clean and unseen noise environments.
Original language | English |
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Pages (from-to) | 836-840 |
Number of pages | 5 |
Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
Volume | 2020-October |
DOIs | |
Publication status | Published - 2020 |
Event | 21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, China Duration: 2020 Oct 25 → 2020 Oct 29 |
Bibliographical note
Funding Information:This work was supported by the Korea Environmental Industry & Technology Institute (KEITI) through the Public Technology Program based on environmental policy funded by the Korean Ministry of Environment (MOE; 2017000210001), and the contribution of David Han was supported by the US Army Research Laboratory.
Publisher Copyright:
Copyright © 2020 ISCA
Keywords
- Audio recognition
- Dual stage
- Dynamic filter network
- Feature enhancement
ASJC Scopus subject areas
- Language and Linguistics
- Human-Computer Interaction
- Signal Processing
- Software
- Modelling and Simulation