Robust Non-negative Block Sparse Coding for Acoustic Novelty Detection

Abstract

In this paper we address the problem of detecting previously unseen novel audio events in the presence of real-life acoustic backgrounds. Specifically, during training, we learn subspaces corresponding to each acoustic background, and during testing the audio frame in question is decomposed into a component that lies on the mixture of subspaces and a supergaussian outlier component. Based on the energy in the estimated outlier component a decision is made, whether or not the current frame is an acoustic novelty. We compare our proposed method with state of the art auto-encoder based approaches and also with a traditional supervised Nonnegative Matrix Factorization (NMF) based method using a publicly available dataset - A3Novelty. We also present results using our own dataset created by mixing novel/rare sounds such as gunshots, glass-breaking and sirens, with normal background sounds for various event to background ratios (in dB).