abstract = {Detection of anomalous sound events in audio surveillance is a challenging task when applied to realistic settings. Part of the difficulty stems from properly defining the `normal' behavior of a crowd or an environment (e.g. airport, train station, sport field). By successfully capturing the heterogeneous nature of sound events in an acoustic environment, we can use it as a reference against which anomalous behavior can be detected in continuous audio recordings. The current study proposes a methodology for representing sound classes using a hierarchical network of convolutional features and mixture of temporal trajectories (MTT). The framework couples unsupervised and supervised learning and provides a robust scheme for detection of abnormal sound events in a subway station. The results reveal the strength of the proposed representation in capturing non-trivial commonalities within a single sound class and variabilities across different sound classes as well as high degree of robustness in noise.},
author = {Chakrabarty, Debmalya and Elhilali, Mounya},
booktitle = {2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
doi = {10.1109/ICASSP.2016.7471668},
isbn = {978-1-4799-9988-0},
issn = {15206149},
keywords = {Anomalous sound events,Convolutional feature representation,Hierarchical network,Mixture of temporal trajectory models},
pages = {216--220},
publisher = {IEEE},
title = {{Abnormal sound event detection using temporal trajectories mixtures}},
url = {http://ieeexplore.ieee.org/document/7471668/},
year = {2016}