2015/08/20 by Alain Rakotomamonjy, Rakotomamonjy, Alain, Gilles Gasso +1
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Music Technology and Sound Studies #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #cs.LG #cs.SD
paper · pdf · doi:10.48550/arxiv.1508.04909
arxiv created 2015/08/20 · openalex publication_date 2015/08/20 · arxiv updated 2015/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper addresses the problem of audio scenes classification and contributes to the state of the art by proposing a novel feature. We build this feature by considering histogram of gradients (HOG) of time-frequency representation of an audio scene. Contrarily to classical audio features like MFCC, we make the hypothesis that histogram of gradients are able to encode some relevant informations in a time-frequency representation: namely, the local direction of variation (in time and frequency) of the signal spectral power. In addition, in order to gain more invariance and robustness, histogram of gradients are locally pooled. We have evaluated the relevance of the novel feature by comparing its performances with state-of-the-art competitors, on several datasets, including a novel one that we provide, as part of our contribution. This dataset, that we make publicly available, involves 19 classes and contains about 900 minutes of audio scene recording. We thus believe that it may be the next standard dataset for evaluating audio scene classification algorithms. Our comparison results clearly show that our HOG-based features outperform its competitors