City-Identification of Flickr Videos Using Semantic Acoustic Features

Benjamin Elizalde, Guan-Lin Chao, Ming Zeng, Ian Lane
In IEEE International Conference on Multimedia Big Data (BigMM) 2016
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title={City-identification of flickr videos using semantic acoustic features},
author={Elizalde, Benjamin and Chao, Guan-Lin and Zeng, Ming and Lane, Ian},
booktitle={International Conference on Multimedia Big Data (BigMM)},

City-identification of videos aims to determine the likelihood of a video belonging to a set of cities. In this paper, we present an approach using only audio, thus we do not use any additional modality such as images, user-tags or geo-tags. In this manner, we show to what extent the city-location of videos correlates to their acoustic information. Success in this task suggests improvements can be made to complement the other modalities. In particular, we present a method to compute and use semantic acoustic features to perform city-identification and the features show semantic evidence of the identification. The semantic evidence is given by a taxonomy of urban sounds and expresses the potential presence of these sounds in the city-soundtracks. We used the MediaEval Placing Task set, which contains Flickr videos labeled by city. In addition, we used the UrbanSound8K set containing audio clips labeled by sound-type. Our method improved the state-of-the-art performance and provides a novel semantic approach to this task.