# Development details and computational benchmarking of DEPAM

## TL;DR

This paper introduces a scalable, cloud-based system for FFT-based acoustic data analysis in oceanography, demonstrating promising performance and scalability compared to existing tools.

## Contribution

We developed and benchmarked a Hadoop/Spark system for acoustic feature extraction, showing improved scalability and performance over traditional methods.

## Key findings

- System performs well on single-node setups
- System scales almost linearly with dataset size
- Benchmarking shows competitive performance with existing tools

## Abstract

In the big data era of observational oceanography, passive acoustics datasets are becoming too high volume to be processed on local computers due to their processor and memory limitations. As a result there is a current need for our community to turn to cloud-based distributed computing. We present a scalable computing system for FFT (Fast Fourier Transform)-based features (e.g., Power Spectral Density) based on the Apache distributed frameworks Hadoop and Spark. These features are at the core of many different types of acoustic analysis where the need of processing data at scale with speed is evident, e.g. serving as long-term averaged learning representations of soundscapes to identify periods of acoustic interest. In addition to provide a complete description of our system implementation, we also performed a computational benchmark comparing our system to three other Scala-only, Matlab and Python based systems in standalone executions, and evaluated its scalability using the speed up metric. Our current results are very promising in terms of computational performance, as we show that our proposed Hadoop/Spark system performs reasonably well on a single node setup comparatively to state-of-the-art processing tools used by the PAM community, and that it could also fully leverage more intensive cluster resources with a almost-linear scalability behaviour above a certain dataset volume.