City-Identification of Flickr Videos Using Semantic Acoustic Features

TL;DR

This paper introduces a novel audio-only method for city-identification of videos using semantic acoustic features, demonstrating that urban sounds can effectively indicate city location and improve identification accuracy.

Contribution

The paper presents a new semantic acoustic feature extraction method for city-identification, showing improved performance without relying on visual or metadata modalities.

Findings

Improved state-of-the-art accuracy in city-identification

Semantic acoustic features correlate strongly with city location

Urban sound taxonomy enhances identification performance

Abstract

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