Abstract: To recognize audio scene in a complex environment according to an audio stream, a constant-Q transform is chosen to obtain the time-frequency representation (TFR) of the signal. Due to the lack of prior knowledge on the signal and noise, a mean filtering is used to smooth the TFR image, then the features based on the histogram of gradients (HOG) of the TFR image are extracted, which can reflect the local direction of variation (both in time and frequency) of the signal power spectrum. Consequently the Local Binary Pattern (LBP) feature is considered, which captures the texture information of the signal. As for the classification algorithm, support vector machine with linear kernel function is used. Classification experiment has been done on the data of different acoustic scenes. Compared with the classical audio features such as MFCCs, the proposed features capture the discriminative power of a given audio scene to show good performance in classification, and the combined features achieve the best results. It is valuable in the field of feature extraction of acoustic signal.