A joint time-frequency audio feature extraction algorithm based on Gabor dictionary and weight tensor of sparse representation was proposed to describe the characteristic of non-stationary audio signal. Conventional sparse representation uses a predefined dictionary to encode the audio signal as sparse weight vector. In this paper, the elements in the weight vector were reorganized into tensor format. Each order of the tensor respectively characterized time, frequency and duration property of signal, making it the joint time-frequency-duration representation of the signal. The frequency factors and duration factors were concatenated as audio features through tensor decomposition. To solve the over-fitting problem of sparse tensor factorization, an automatic-adjust-penalty-coefficient factorization algorithm was proposed. The experimental results show that the proposed feature outperforms MFCC (Mel-Frequency Cepstrum Coefficient) feature, MFCC+MP feature concatenated by MFCC and Matching Pursuit (MP) features, and nonuniform scale-frequency map feature by 28.0%, 19.8% and 6.7% respectively, in 15-category audio classification.

To deal with the difficulties of lacking the discrimination method of network's traffic self-similarity and producing negative traffic based on classical Fractal Brown Motion (FBM), a discrimination method was proposed based on multiple order moment and a generation method was provided based on modified FBM model. Firstly, the mathematical formula of sample moment was studied. The discrimination method of self-similarity traffic was obtained on account of fractal moment analysis. Secondly, the classical Random Midpoint Displacement (RMD) algorithm was modified. At last, taking account of the real traffic of Bellcore and LBL, the discrimination method and generation method were given. The comparison of the simulation results with the actual experimental data proves that the method is feasible.