Robust Non-negative Block Sparse Coding for Acoustic Novelty Detection
|Citation:||R. Giri, A. Krishnaswamy & K. Helwani, "Robust Non-negative Block Sparse Coding for Acoustic Novelty Detection", Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019), pages 74–78, New York University, NY, USA, Oct. 2019|
|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).|
|Appears in Collections:||Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)|
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