2017/12/25 by Romain Cosentino, Cosentino, Romain, Randall Balestriero +6 · 1 citation
Computer Science · Engineering · Mathematics · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Machine Learning (stat.ML) #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #cs.SD #eess.AS #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.1712.09117
arxiv created 2017/12/25 · openalex publication_date 2017/12/25 · arxiv updated 2017/12/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.