Abstract: In this paper, obtaining robust different properties of audio signal by Tucker decomposition was presented and applied to audio classification via Gaussian Mixture Model (GMM). Firstly, acoustics feature sets, perceptual feature sets and psychoacoustic feature sets were extracted after pre-processing. Next, a 3-order Tucker tensor was constructed by the three sets. Projection matrices and principal components in each mode were obtained via Tucker decomposition. Finally, the feature sets were measured for 300 audio recordings consisting of 3 different classes including music, speech and noise. A class of supervised classifiers was developed based on GMM. The experiments compared the percent correct classification alongside different feature sets. The results demonstrate the features via Tucker decomposition outperform the traditional feature sets in the aforementioned experiment.