  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
