Enhancing a Stationary Noise Suppressor with Artificial Neural Networks

Perrone M, Faller C. Enhancing a Stationary Noise Suppressor with Artificial Neural Networks. In: Express Paper. Audio Engineering Society; 2024. Paper 192. Available from: https://aes.org/publications/elibrary-page/?id=22538

@inproceedings{Perrone2024_22538,
  author = {Perrone, Michele and Faller, Christof},
  title = {{Enhancing a Stationary Noise Suppressor with Artificial Neural Networks}},
  note = {Paper 192},
  year = {2024},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22538}
}

TY  - CPAPER
TI  - Enhancing a Stationary Noise Suppressor with Artificial Neural Networks
AU  - Perrone, Michele
AU  - Faller, Christof
M1  - Paper 192
PY  - 2024
DA  - 2024/06/06
UR  - https://aes.org/publications/elibrary-page/?id=22538
PB  - Audio Engineering Society
LA  - en
AB  - Despite artificial neural networks (ANNs) making rapid progress in the field of noise removal for audio signals, their computational complexity and unpredictable behavior on unseen noise types constitute an issue for many applications. Noise suppression systems often need to be adopted in low-resource communications systems that cannot meet the requirements of most deep learning models. These systems also require real-time low-delay processing, and are adopted in a wide variety of noise situations. To overcome these limitations, we propose an innovative hybrid noise suppressor (HNS) for speech signals which combines the robustness of a traditional stationary noise suppressor (SNS) with the generalization capabilities of an ANN. A low-complexity ANN is used to enhance the performance of the SNS by removing non-stationary noise and perceptually unpleasant artifacts. Our evaluation shows that the proposed HNS is able to perform effective real-time denoising on unseen noise types, while retaining a lower complexity than the vast majority of state of the art deep learning techniques.
ER  -