  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.
