Sobieraj, Iwona and Plumbley, Mark (2016) Coupled Sparse NMF vs. Random Forest Classification for Real Life Acoustic Event Detection. In: Detection and Classification of Acoustic Scenes and Events 2016, 3 Sept 2016, Budapest, Hungary.

Abstract In this paper, we propose two methods for polyphonic Acoustic Event Detection (AED) in real life environments. The first method is based on Coupled Sparse Non-negative Matrix Factorization (CSNMF) of spectral representations and their corresponding class activity annotations. The second method is based on Multi-class Random F orest (MRF) classification of time-frequency patches. We compare the performance of the two methods on a recently published dataset TUT Sound Events 2016 containing data from home and residential area environments. Both methods show comparable performance to the baseline system proposed for DCASE 2016 Challenge on the development dataset with MRF outperforming the baseline on the evaluation dataset.

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