RAVEN X High Performance Data Mining Toolbox for Bioacoustic Data Analysis
This provides a practical tool for bioacoustics researchers to handle large acoustic data more efficiently, though it is incremental as it builds on prior work.
The authors tackled the problem of processing large bioacoustic datasets by developing Raven-X, a MATLAB-based toolbox that integrates high-performance computing to accelerate data analysis, resulting in a hardware-independent solution offered for free to the community.
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.