On the basis of Time Domain Analysis, this paper proposes a method for music beat tracking. Through this method, music beats are detected and tracked by timestamp and intensity value. Generally, the beat-areas of music signal converge more energy than other areas, therefore, the spots of beat can be filtered out by a tracking algorithm with a dynamic threshold value. In this paper, dynamic threshold value in tracking algorithm is modified by using two sliding windows, which are Prediction Window and Detection Window. Also, a new indicator which indicates the stationarity of the signal is proposed. This factor can distinguishes the music signal with rhythmic beats from which with lone-tone and noise in time-domain. The experimental result proves the simplicity, adaptability, and robustness of this method, and it is an efficient algorithm on music beat tracking.

Based on the theories of frequency domain and time domain signal processing, wavelet analysis, and singular value decomposition (SVD), an effective method for content based music feature extraction is proposed in this paper. Music feature can be divided into three parts by this method, which are frequency feature, auditory perceptual feature, and statistical characteristic of beat. The characteristic of each music can be well described by these features. The results of logistic regression classification model and linear support vector machine (SVM) classification model which is on a data set consists of several different styles of music and use the feature extraction method in this paper show the high precision of 95.33% in average, and also prove the effectiveness of the proposed method. Feature extraction is the foundation of content based recommendation, retrieval, classification, and cluster. Hence this method has good prospect in these area.

Based on the gray symbiotic matrix and gray characteristic values, an extraction feature method is proposed for medical image of magnetic resonance imaging (MRI), which can reduce the dimensions of characteristic values, and improve the operation rate as well as classification accuracy. For a single image, by selecting its outline as characteristic values, support vector machine (SVM) is trained by using interval samples to realize the segmentation of other similar images. The problem of low computation efficiency due to large amount of samples is also avoided by reducing the number of samples. The result shows that, by taking the main feature of one picture as training sample, the identification of the same picture can reach 90% and the identification of other similar images can reach 80%. This method can reduce the lesion area and improve processing speed. Meanwhile, the image compress ratio can reach 1/4 when PSNR is 20.49% by utilizing compressed sensing technology on the background of the image based on this image segmentation method.