# A Bayesian binary algorithm for RMS-based acoustic signal segmentation

## TL;DR

This paper introduces a Bayesian binary algorithm for segmenting long acoustic signals based on RMS power changes, with applications to marine acoustic data, offering a probabilistic approach to change point detection.

## Contribution

It proposes a novel Bayesian segmentation algorithm for RMS-based acoustic signals, including two parameterizations and prior options, applied to real marine data.

## Key findings

- Effective segmentation of marine acoustic signals demonstrated
- Bayesian approach provides probabilistic confidence in change points
- Algorithm performs well on annotated real-world data

## Abstract

Changepoint analysis (also known as segmentation analysis) aims at analyzing an ordered, one-dimensional vector, in order to find locations where some characteristic of the data changes. Many models and algorithms have been studied under this theme, including models for changes in mean and / or variance, changes in linear regression parameters, etc. In this work, we are interested in an algorithm for the segmentation of long duration acoustic signals; the segmentation is based on the change of the RMS power of the signal. We investigate a Bayesian model with two possible parameterizations, and propose a binary algorithm in two versions, using non-informative or informative priors. We apply our algorithm to the segmentation of annotated acoustic signals from the Alcatrazes marine preservation park in Brazil.