Deep Learning Frameworks Applied For Audio-Visual Scene Classification
This work addresses scene classification for audio-visual applications, presenting incremental improvements over existing methods.
The paper tackled audio-visual scene classification by developing deep learning frameworks to analyze the impact of audio, visual, and combined features on performance, achieving classification accuracies of 82.2% (audio only), 91.1% (visual only), and 93.9% (audio-visual ensemble), with the ensemble showing a 16.5% improvement over the baseline.
In this paper, we present deep learning frameworks for audio-visual scene classification (SC) and indicate how individual visual and audio features as well as their combination affect SC performance. Our extensive experiments, which are conducted on DCASE (IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events) Task 1B development dataset, achieve the best classification accuracy of 82.2%, 91.1%, and 93.9% with audio input only, visual input only, and both audio-visual input, respectively. The highest classification accuracy of 93.9%, obtained from an ensemble of audio-based and visual-based frameworks, shows an improvement of 16.5% compared with DCASE baseline.