# Theory-plus-code documentation of the DEPAM workflow for soundscape description

## TL;DR

This paper presents a detailed theoretical and coded workflow for PAM soundscape analysis, aiming to standardize methods and enable scalable processing using Spark/Hadoop frameworks for large datasets.

## Contribution

It introduces a comprehensive theory-plus-code documentation of a PAM analysis workflow and implements it in Scala for scalable, high-performance processing.

## Key findings

- Workflow is standardized and documented for community review
- Scala implementation enables scalable processing on clusters
- Addresses challenges of big data in PAM analysis

## Abstract

In the Big Data era, the community of PAM faces strong challenges, including the need for more standardized processing tools accross its different applications in oceanography, and for more scalable and high-performance computing systems to process more efficiently the everly growing datasets. In this work we address conjointly both issues by first proposing a detailed theory-plus-code document of a classical analysis workflow to describe the content of PAM data, which hopefully will be reviewed and adopted by a maximum of PAM experts to make it standardized. Second, we transposed this workflow into the Scala language within the Spark/Hadoop frameworks so it can be directly scaled out on several node cluster.