Abstract
In this paper, we propose a deep neural network (DNN)-based single-microphone speech enhancement algorithm characterized by a short latency and low computational resources. Many speech enhancement algorithms suffer from low noise reduction capabilities between pitch harmonics, and in severe cases, the harmonic structure may even be lost. Recognizing this drawback, we propose a new weighted loss that emphasizes pitch-dominated frequency bands. For that, we propose a method, applied only at the training stage, to detect these frequency bands. The proposed method is applied to speech signals contaminated by several noise types, and in particular, typical domestic noise drawn from ESC-50 and DEMAND databases, demonstrating its applicability to 'stay-at-home' scenarios.
| Original language | English |
|---|---|
| Title of host publication | 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 8807-8811 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781665405409 |
| DOIs | |
| State | Published - 2022 |
| Event | 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore Duration: 23 May 2022 → 27 May 2022 |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
|---|---|
| Volume | 2022-May |
| ISSN (Print) | 1520-6149 |
Conference
| Conference | 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 |
|---|---|
| Country/Territory | Singapore |
| City | Virtual, Online |
| Period | 23/05/22 → 27/05/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE
Keywords
- DNN
- Ideal ratio mask
- Single-microphone speech enhancement
- Speech harmonics presence detection