Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives

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++.