Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2023 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (elektronisch) | 9798350323726 |
| ISBN (Print) | 979-8-3503-2373-3 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) - New Paltz, USA / Vereinigte Staaten Dauer: 22 Okt. 2023 → 25 Okt. 2023 |
Publikationsreihe
| Name | IEEE Workshop on Applications of Signal Processing to Audio and Acoustics |
|---|---|
| ISSN (Print) | 1931-1168 |
| ISSN (elektronisch) | 1947-1629 |
Abstract
For an accurate representation of a virtual sound source in an Augmented Reality environment, it is crucial to understand the acoustic properties of the current room where the source is to be rendered. Reverberation Time (RT60) and Clarity (C50) are two of the most significant parameters that could negatively impact the plausibility of the source when estimated incorrectly. We propose using an audio transformer to estimate these parameters blindly and solely from a single-channel noisy speech signal. Furthermore, to ensure efficiency and minimum computational complexity, the use of an efficient lightweight architecture is proposed for its suitability for mobile-friendly applications. We evaluate the proposed model with regard to its complexity and accuracy against the existing state-of-the-art approaches in unseen and real acoustic settings. Results demonstrate that the proposed model surpasses the traditional CNN models in terms of complexity, size, speed, and precision.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
- Informatik (insg.)
- Angewandte Informatik
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Proceedings of the 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Blind Room Acoustic Parameters Estimation Using Mobile Audio Transformer
AU - Saini, Shivam
AU - Peissig, Jürgen
PY - 2023
Y1 - 2023
N2 - For an accurate representation of a virtual sound source in an Augmented Reality environment, it is crucial to understand the acoustic properties of the current room where the source is to be rendered. Reverberation Time (RT60) and Clarity (C50) are two of the most significant parameters that could negatively impact the plausibility of the source when estimated incorrectly. We propose using an audio transformer to estimate these parameters blindly and solely from a single-channel noisy speech signal. Furthermore, to ensure efficiency and minimum computational complexity, the use of an efficient lightweight architecture is proposed for its suitability for mobile-friendly applications. We evaluate the proposed model with regard to its complexity and accuracy against the existing state-of-the-art approaches in unseen and real acoustic settings. Results demonstrate that the proposed model surpasses the traditional CNN models in terms of complexity, size, speed, and precision.
AB - For an accurate representation of a virtual sound source in an Augmented Reality environment, it is crucial to understand the acoustic properties of the current room where the source is to be rendered. Reverberation Time (RT60) and Clarity (C50) are two of the most significant parameters that could negatively impact the plausibility of the source when estimated incorrectly. We propose using an audio transformer to estimate these parameters blindly and solely from a single-channel noisy speech signal. Furthermore, to ensure efficiency and minimum computational complexity, the use of an efficient lightweight architecture is proposed for its suitability for mobile-friendly applications. We evaluate the proposed model with regard to its complexity and accuracy against the existing state-of-the-art approaches in unseen and real acoustic settings. Results demonstrate that the proposed model surpasses the traditional CNN models in terms of complexity, size, speed, and precision.
KW - AudMobNet
KW - Mobile Audio Transformer
KW - Reverberation Time Estimation
KW - Room Acoustic Parameter Estimation
UR - http://www.scopus.com/inward/record.url?scp=85173012743&partnerID=8YFLogxK
U2 - 10.1109/WASPAA58266.2023.10248186
DO - 10.1109/WASPAA58266.2023.10248186
M3 - Conference contribution
AN - SCOPUS:85173012743
SN - 979-8-3503-2373-3
T3 - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
BT - Proceedings of the 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2023
Y2 - 22 October 2023 through 25 October 2023
ER -