Abstract
Recent work on acoustic parameter estimation indicates that geometric room volume can be useful for modeling the character of an acoustic environment. However, estimating volume from audio signals remains a challenging problem. Here we propose using a convolutional neural network model to estimate the room volume blindly from reverberant single-channel speech signals in the presence of noise. The model is shown to produce estimates within approximately a factor of two to the true value, for rooms ranging in size from small offices to large concert halls.
| Original language | English |
|---|---|
| Title of host publication | 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings |
| Publisher | IEEE |
| Pages | 231-235 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-4799-8131-1 |
| ISBN (Print) | 978-1-4799-8132-8 |
| DOIs | |
| Publication status | Published - 1 May 2019 |
| MoE publication type | A4 Article in a conference publication |
| Event | IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 Conference number: 44 |
Publication series
| Name | Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing |
|---|---|
| ISSN (Print) | 1520-6149 |
| ISSN (Electronic) | 2379-190X |
Conference
| Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing |
|---|---|
| Abbreviated title | ICASSP |
| Country/Territory | United Kingdom |
| City | Brighton |
| Period | 12/05/2019 → 17/05/2019 |
Keywords
- Acoustics
- Volume measurement
- Training
- Solid modeling
- Noise measurement
- Data models
- Acoustic measurements
- Room acoustics
- room size
- non-intrusive parameter estimation
- signal processing
- convolutional neural network