Overcomplete Frame Thresholding for Acoustic Scene Analysis
Audio and Speech Processing
2017-12-27 v1 Sound
Machine Learning
Abstract
In this work, we derive a generic overcomplete frame thresholding scheme based on risk minimization. Overcomplete frames being favored for analysis tasks such as classification, regression or anomaly detection, we provide a way to leverage those optimal representations in real-world applications through the use of thresholding. We validate the method on a large scale bird activity detection task via the scattering network architecture performed by means of continuous wavelets, known for being an adequate dictionary in audio environments.
Cite
@article{arxiv.1712.09117,
title = {Overcomplete Frame Thresholding for Acoustic Scene Analysis},
author = {Romain Cosentino and Randall Balestriero and Richard Baraniuk and Ankit Patel},
journal= {arXiv preprint arXiv:1712.09117},
year = {2017}
}