Monaural Speech Enhancement on Drone via Adapter Based Transfer Learning
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2024 18th International Workshop on Acoustic Signal Enhancement (IWAENC), 2024, 00, pp. 85-89
- Issue Date:
- 2024-10-04
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Conference Monaural speech enhancement on drone via Adapter based transfer learning.pdf | Published version | 4.62 MB |
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Monaural Speech enhancement on drones is challenging because the ego noise from the rotating motors and propellers leads to extremely low signal to noise ratios at onboard microphones Although recent masking based deep neural network methods excel in monaural speech enhancement they struggle in the challenging drone noise scenario Furthermore existing drone noise datasets are limited causing models to overfit Considering the harmonic nature of drone noise this paper proposes a frequency domain bottleneck adapter to enable transfer learning Specifically the adapter s parameters are trained on drone noise while retaining the parameters of the pre trained Frequency Recurrent Convolutional Recurrent Network FRCRN fixed Evaluation results demonstrate the proposed method can effectively enhance speech quality Moreover it is a more efficient alternative to fine tuning models for various drone types which requires substantial computational resources
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