TY - GEN
T1 - Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives
AU - Castro, Jorge
AU - Meneses, Esteban
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/12
Y1 - 2018/9/12
N2 - Automatic segmentation and classification methods for bioacoustic signals enable real-time monitoring, population estimation, as well as other important tasks for the conservation, management, and study of wildlife. These methods normally require a filter or a denoising strategy to enhance relevant information in the input signal and avoid false positive detections. This denoising stage is usually the performance bottleneck of such methods. In this paper, we parallelize a denoising algorithm for tonal bioacoustic signals using mainly OpenACC directives. The implemented program was executed in both multicore and GPU architectures. The proposed parallelized algorithm achieves a higher speedup on GPU than CPU, leading to a 10.67 speedup compared to the original sequential algorithm in C++.
AB - Automatic segmentation and classification methods for bioacoustic signals enable real-time monitoring, population estimation, as well as other important tasks for the conservation, management, and study of wildlife. These methods normally require a filter or a denoising strategy to enhance relevant information in the input signal and avoid false positive detections. This denoising stage is usually the performance bottleneck of such methods. In this paper, we parallelize a denoising algorithm for tonal bioacoustic signals using mainly OpenACC directives. The implemented program was executed in both multicore and GPU architectures. The proposed parallelized algorithm achieves a higher speedup on GPU than CPU, leading to a 10.67 speedup compared to the original sequential algorithm in C++.
KW - Bioacoustics
KW - Denoising
KW - Graphic Processing Unit (GPU)
KW - OpenACC
KW - Parallel computing
UR - http://www.scopus.com/inward/record.url?scp=85054483188&partnerID=8YFLogxK
U2 - 10.1109/IWOBI.2018.8464129
DO - 10.1109/IWOBI.2018.8464129
M3 - Contribución a la conferencia
AN - SCOPUS:85054483188
SN - 9781538675069
T3 - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
BT - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018
Y2 - 18 July 2018 through 20 July 2018
ER -