An i-Vector Representation of Acoustic Environments for Audio-Based Video Event Detection on User Generated Content

Abstract

Audio-based video event detection (VED) on user-generated content (UGC) aims to find videos that show an observable event such as a wedding ceremony or birthday party rather than a sound, such as music, clapping or singing. The difficulty of video content analysis on UGC lies in the acoustic variability and lack of structure of the data. The UGC task has been explored mainly by computer vision, but can be benefited by the used of audio. The i-vector system is state-of-the-art in Speaker Verification, and is outperforming a conventional Gaussian Mixture Model (GMM)-based approach. The system compensates for undesired acoustic variability and extracts information from the acoustic environment, making it a meaningful choice for detection on UGC. This paper employs the i-vector-based system for audio-based VED on UGC and expands the understanding of the system on the task. It also includes a performance comparison with the conventional GMM-based and state-of-the-art Random Forest (RF)-based systems. The i-vector system aids audio-based event detection by addressing UGC audio characteristics. It outperforms the GMM-based system, and is competitive with the RF-based system in terms of the Missed Detection (MD) rate at 4% and 2.8% False Alarm (FA) rates, and complements the RF-based system by demonstrating slightly improvement in combination over the standalone systems.