# RAVEN X High Performance Data Mining Toolbox for Bioacoustic Data Analysis

arXiv: https://arxiv.org/abs/1610.03772 · HTML: https://mlsanity.com/paper/1610.03772/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2016.

## Built on by (most cited first, 1 of 1)

- [Big Data Approaches to Bovine Bioacoustics: A FAIR-Compliant Dataset and Scalable ML Framework for Precision Livestock Welfare](https://mlsanity.com/paper/2510.14443/) (2025)

## Abstract (arXiv)

Objective of this work is to integrate high performance computing (HPC) technologies and bioacoustics data-mining capabilities by offering a MATLAB-based toolbox called Raven-X. Raven-X will provide a hardware-independent solution, for processing large acoustic datasets - the toolkit will be available to the community at no cost. This goal will be achieved by leveraging prior work done which successfully deployed MATLAB based HPC tools within Cornell University's Bioacoustics Research Program (BRP). These tools enabled commonly available multi-core computers to process data at accelerated rates to detect and classify whale sounds in large multi-channel sound archives. Through this collaboration, we will expand on this effort which was featured through Mathworks research and industry forums incorporate new cutting-edge detectors and classifiers, and disseminate Raven-X to the broader bioacoustics community.