Focusing on the difficulty in monitoring and judging the credibility of user behaviors in online applications and the problem of weak theorey of user behavior credibility analysis, a credibility analysis method of online user behavior was proposed based on non-interference theory. Firstly, the static credibility of single behavior was defined from three aspects-the behavioral entity identity, state and environment of the single behavior, and the static credibility verification strategy was given. Thereafter, dynamic behavioral credibility was defined from the perspectives of execution process and result, and dynamic credibility verification strategy was given. Finally, the user behavior process was constructed based on the single behavior, and the credibility determination theorem of user behavior process was proposed based on the idea of credibility extension, and the theorem was proved by using non-interference theory. The correctness and validity of the proposed method were verified by the provement process and result.

Aiming at the problem that the discontinuity of the pitch sequence of the same sound source was caused by the interference of different sound sources in polyphonic music which reduced the accuracy of pitch estimation, an improved pitch contour creation and selection algorithm for melody extraction was proposed. Firstly, a method based on auditory streaming cues and the continuity of pitch salience was proposed to create pitch contour by calculating the pitch salience of each point in the time-frequency spectrum. In order to further select the melody pitch contour, the non-melodic pitch contours were removed according to the repetitive characteristics of the accompaniment, and dynamic time warping algorithm was used to calculate the similarity between the melodic and non-melodic pitch contours. Finally, the octave errors in the melodic pitch contours was detected based on the long term relationship of the adjacent pitch contours. Simulation experiments on the data set ORCHSET show that the pitch estimation accuracy and the overall accuracy of the proposed algorithm are improved by 2.86% and 3.32% respectively compared with the oringinal algorithm, which can effectively solve the pitch estimation problem.

To solve the problem that Explicit Shape Regression (ESR) has low precision in face alignment, an improved explicit shape regression for face alignment algorithm was proposed. Firstly, in order to get a more accurate initial shape, three-point face shape was used as an initial shape mapping standard to replace face rectangle. Then, pixel block feature was used against illumination variations instead of pixel feature, which improved the algorithm robustness. Finally, instead of average method, the accuracy of algorithm was further improved by multiple hypothesis fusion strategy which merged multiple estimations. Compared with explicit shape regression algorithm, the simulation experimental results show that the accuracy is improved by 7.96%, 5.36% and 1.94% respectively by using the proposed algorithm on LFPW, HELEN and 300-W face datasets.

Aiming at time or frequency feature, cepstrum feature, sparse feature and probability feature's poor classification performance for kindred and percussion instrument, an enhanced model for extracting time-frequency information and with lower redundancy was proposed. Firstly, a cochlea model was set to filter music signal, whose output was called Auditory Spectrum (AS) containing harmonic information and close to human perception. Secondly, time-frequency feature was acquired by Multiscale Time-Frequency Modulation (MTFM). Then, dimension reduction was implied by Multilinear Principal Component Analysis (MPCA) to preserve the structure and intrinsic correlation. Finally, classification was conducted using Support Vector Machine (SVM). The experimental results show that MTFM's average accuracy is 92.74% on IOWA database and error rate of percussion or kindred instrument is 3% and 9.12%, which wins out the features mentioned above. The accuracy of MPCA was higher 6.43% than that of Principle Component Analysis (PCA). It is proved that the proposed model is an option for kindred and percussion instrument identification.

Iteration-based Active Semi-Supervised Clustering Framework (IASSCF) is a popular semi-supervised clustering framework. There are two problems in this framework. The initial prior information is too less, which leads to poor clustering results in the initial iteration and infects the subsequent clustering. In addition, in each iteration only the sample with the largest information is selected to label, which results in a slow speed and improvement of the performance. Aiming to the existing problems, a semi-supervised K-means clustering algorithm based on active learning priors was designed, which consisted of initializing phase and iterating phase. In the initializing phase, the representative samples were selected actively to build an initial neighborhood set and a constraint set. Each iteration in iterating phase includes three steps:1) Pairwise Constrained K-means (PCK-means) was used to cluster data based on the current constraints. 2) Unlabeled samples with the largest information in each cluster were selected based on the clustering results. 3) The selected samples were extended into the neighborhood set and the constraint set. The iterating phase ends until the convergence thresholds were reached. The experimental results show that the proposed algorithm runs faster and has better performance than the algorithm based on the original IASSCF framework.