Search
Abstract
A new algorithm for double-talk detection, intended for use in the acoustic echo canceller for voice communication applications, is proposed. The communication system developed by the authors required the use of a double-talk detection algorithm with low complexity and good accuracy. The authors propose an approach to doubletalk detection based on the signal envelopes. For each of three signals: the far-end speech, the microphone signal and the echo estimate, an envelope is detected. Next, using these envelopes, a detection function is determined and compared to the threshold. Additionally, a dynamic threshold is introduced in order to improve the accuracy of the algorithm. The results of the simulations presented in the paper proved that the accuracy of double-talk detection obtained using the proposed algorithm is higher than in the Geigel algorithm and comparable to the correlation-based methods, while the computational complexity of the proposed method remains at an acceptable level. The double-talk detection algorithm presented here may be used in voice communication systems having limited resources, allowing for accurate double-talk detection and, as a consequence, efficient acoustic echo cancellation.
Citations
-
1 7
CrossRef
-
0
Web of Science
-
1 9
Scopus
Authors (3)
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.sigpro.2008.05.013
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuł w czasopiśmie z listy filadelfijskiej
- Published in:
-
SIGNAL PROCESSING
no. 88,
pages 2856 - 2862,
ISSN: 0165-1684 - Language:
- English
- Publication year:
- 2008
- Bibliographic description:
- Szwoch G., Czyżewski A., Kulesza M.: A low complexity double-talk detector based on the signal envelope// SIGNAL PROCESSING. -Vol. 88., nr. iss. 11 (2008), s.2856-2862
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.sigpro.2008.05.013
- Verified by:
- Gdańsk University of Technology
seen 167 times