LiTasNeT: A Bird Sound Separation Algorithm Based on Deep Learning

Amira Boulmaiz, Billel Meghni, Abdelghani Redjati and Ahmad Taher Azar
Additional contact information
Amira Boulmaiz: Laboratory of L.E.R.I.C.A., University of Badji Mokhtar, Annaba, Algeria
Billel Meghni: University of Badji Mokhtar, Annaba, Algeria
Abdelghani Redjati: Laboratory of L.E.R.I.C.A., University of Badji Mokhtar, Annaba, Algeria
Ahmad Taher Azar: College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha

International Journal of Sociotechnology and Knowledge Development (IJSKD), 2022, vol. 14, issue 1, 1-19

Abstract: Recent advances in deep learning techniques and acoustic sensor networks offer a new way for continuously monitoring birds. Deep learning methods have led to considerable progresses in audio source separation (ASS). However, it is still a challenge to deploy models based on deep learning on embedded devices. Therefore, find an efficient solution to compact these large models without affecting ASS performance has become an important research topic. In birds' natural habitat, it is common for several birds to sing simultaneously. This phenomenon will lead to false results when identifying a particular bird species. Separate required bird sound from the recorded mixture becomes indispensable. In this paper, a novel so-called Lite TasNet (LiTasNeT) is proposed. Based on conventional ASS methods, LiTasNeT has obtained leading results in several standardized ASS areas. LiTasNeT is designed with parameter-sharing scheme to lower the memory consumption. Moreover, his low latency natures make it definitely suitable for real-time on-device applications.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSKD.301261 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jskd00:v:14:y:2022:i:1:p:1-19

Access Statistics for this article

International Journal of Sociotechnology and Knowledge Development (IJSKD) is currently edited by Lincoln Christopher Wood

More articles in International Journal of Sociotechnology and Knowledge Development (IJSKD) from IGI Global
Bibliographic data for series maintained by Journal Editor ().