Details
| Original language | English |
|---|---|
| Title of host publication | 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings |
| Pages | 1015-1019 |
| Number of pages | 5 |
| ISBN (electronic) | 9789082797060 |
| Publication status | Published - 2021 |
| Event | 29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland Duration: 23 Aug 2021 → 27 Aug 2021 |
Publication series
| Name | European Signal Processing Conference |
|---|---|
| Volume | 2021-August |
| ISSN (Print) | 2219-5491 |
| ISSN (electronic) | 2076-1465 |
Abstract
Convolutional recurrent neural networks provide state of the art results in direction of arrival estimation based on first-order Ambisonics signals, especially in the presence of noise and/or interfering sound sources. In this work, we investigate whether increasing the order of Ambisonics up to the fourth order further improves the estimation results in a challenging multi-speaker setting with two or three simultaneously active speakers. Our results show that each additional order of the Ambisonics representation further improves the localization performance for both speech signals based on simulated and real measured spatial room impulse responses. The greatest gains in accuracy can be observed in the particularly demanding scenarios with three speakers and poor signal-to-interference-ratio.
Keywords
- Convolutional recurrent neural network, Higher-order ambisonics, Multi-source direction of arrival estimation, Spherical harmonics
ASJC Scopus subject areas
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
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29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings. 2021. p. 1015-1019 (European Signal Processing Conference; Vol. 2021-August).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Multi-Source Direction of Arrival Estimation of Noisy Speech using Convolutional Recurrent Neural Networks with Higher-Order Ambisonics Signals
AU - Poschadel, Nils
AU - Preihs, Stephan
AU - Peissig, Jürgen
PY - 2021
Y1 - 2021
N2 - Convolutional recurrent neural networks provide state of the art results in direction of arrival estimation based on first-order Ambisonics signals, especially in the presence of noise and/or interfering sound sources. In this work, we investigate whether increasing the order of Ambisonics up to the fourth order further improves the estimation results in a challenging multi-speaker setting with two or three simultaneously active speakers. Our results show that each additional order of the Ambisonics representation further improves the localization performance for both speech signals based on simulated and real measured spatial room impulse responses. The greatest gains in accuracy can be observed in the particularly demanding scenarios with three speakers and poor signal-to-interference-ratio.
AB - Convolutional recurrent neural networks provide state of the art results in direction of arrival estimation based on first-order Ambisonics signals, especially in the presence of noise and/or interfering sound sources. In this work, we investigate whether increasing the order of Ambisonics up to the fourth order further improves the estimation results in a challenging multi-speaker setting with two or three simultaneously active speakers. Our results show that each additional order of the Ambisonics representation further improves the localization performance for both speech signals based on simulated and real measured spatial room impulse responses. The greatest gains in accuracy can be observed in the particularly demanding scenarios with three speakers and poor signal-to-interference-ratio.
KW - Convolutional recurrent neural network
KW - Higher-order ambisonics
KW - Multi-source direction of arrival estimation
KW - Spherical harmonics
UR - http://www.scopus.com/inward/record.url?scp=85123163406&partnerID=8YFLogxK
U2 - 10.23919/EUSIPCO54536.2021.9616002
DO - 10.23919/EUSIPCO54536.2021.9616002
M3 - Conference contribution
AN - SCOPUS:85123163406
SN - 978-1-6654-0900-1
T3 - European Signal Processing Conference
SP - 1015
EP - 1019
BT - 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
T2 - 29th European Signal Processing Conference, EUSIPCO 2021
Y2 - 23 August 2021 through 27 August 2021
ER -