Abstract: A sound event recognition method based on texture features of cochleagram is proposed for improving sound event recognition in various environments. Firstly, the original sound sample is converted into a grayscale cochleagram by Gammatone filter bank. Then, the cochleagram is processed by Curvelet transform to obtain Curvelet sub-bands with different scales and directions. The texture features of Curvelet sub-bands are extracted by using the improved completed local binary pattern (ICLBP) to generate the block statistical histograms which are cascaded as a new sound event feature for recognition. Finally, the support vector machine is used as a classifier to identify 16 kinds of sound events under different noise environments and different signal-to-noise ratios. The experimental results show that the proposed algorithm can effectively identify different kinds of sound events in various noise environments compared with other sound features.