In data mining such as time series classification, the similarity performance based on category of different datasets are significantly different from each other. Therefore, a reasonable and effective similarity measure is crucial to data mining. The traditional methods such as Euclidean Distance (ED), cosine distance and Dynamic Time Warping (DTW) only focus on the similarity formula of the data themselves, but ignore the influence of the knowledge annotation contained in different datasets on the similarity measure. To solve this problem, a learning method of time series similarity measure based on Siamese Neural Network (SNN) was proposed. In the method, the neighborhood relationship between the data was learnt from the supervision information of sample tags, and an efficient distance measure between time series was established. The similarity measurement and confirmatory classification experiments were performed on UCR-provided time series datasets. Experimental results show that compared with ED/DTW-1NN(one Nearest Neighbors), the overall classification quality of SNN is improved significantly. The Dynamic Time Warping (DTW)-based 1NN calssification method outperforms the SNN-based 1NN classification method on some data, but SNN outperforms DTW in complexity and speed of similarity calculation during the classification. The results show that the proposed method can significantly improve the measurement efficiency of the classification of dataset similarity, and has good performance for high-dimensional and complex time-series data classification.

In order to explore the possibility of using two microphones for separation and locating of multiple sound sources in a two-dimensional plane, an indoor voice separation and sound source localization system based on dual-microphone was proposed. According to the signal collected by microphones, a dual-microphone time delay-attenuation model was established. Then, Degenerte Unmixing Estimation Technique (DUET) algorithm was used to estimate the delay-attenuation parameters of model, and the parameter histogram was drawn. In the speech separation stage, Binary Time-Frequency Masking (BTFM) was established. According to the parameter histogram, binary masking method was combined to separate the mixed speech. In the sound source localization stage, the mathematical equations for determining the location of sound source were obtained by deducing the relationship between the model attenuation parameters and the signal energy ratio. Roomsimove toolbox was used to simulate the indoor acoustic environment. Through Matlab simulation and geometric coordinate calculation, the locating in the two-dimensional plane was completed while separating multiple targets of sound source. The experimental results show that, the locating errors of the proposed system for multiple signals of sound source are less than 2%. Therefore, it contributes to the research and development of small system.

Aiming at the problem that most of the existing time-domain modal parameter identification methods are difficult to set order and resist noise poorly, an unsupervised learning Convolution Neural Network (CNN) method for vibration signal modal identification was proposed. The proposed algorithm was improved on the basis of CNN. Firstly, the CNN applied to two-dimensional image processing was changed into the CNN to deal with one-dimensional signal. The input layer was changed into the vibration signal set of modal parameters to be extracted, and the intermediate layer was changed into several one-dimensional convolution layers, sampled layers, and output layer was the set of N-order modal parameters corresponding to the signal. Then, in the error evaluation, the network calculation result ( N-order modal parameter set) was reconstructed by the vibration signals. Finally, the squared sum of the difference between the reconstructed signal and the input signal was taken as the network learning error, which makes the network become an unsupervised learning network, and avoids the ordering problem of modal parameter extraction algorithm. The experimental results show that when the constructed CNN is applied to modal parameter extraction, compared with the Stochastic Subspace Identification (SSI) algorithm and its Local Linear Embedding (LLE) algorithm, the convolutional neural network identification accuracy is higher than that of the SSI algorithm and the LLE algorithm under noise interference. It has strong noise resistance and avoids the ordering problem.

To improve the spectrum sensing performance of cognitive radio technology, in this paper, a new Agent-based cooperative spectrum sensing algorithm was proposed. This algorithm used multiple local energy detection threshold in the local detection, while the Signal-to-Noise Ratio (SNR) estimated by the cognitive user was sent to the main control center of Agent, then the control center based on SNR and the distance between the transmitter and cognitive nodes to balance, to select the cognitive nodes with high reliability and validity of to decision fusion. The simulation results show that the algorithm can improve the cooperative spectrum sensing capabilities of cognitive radio networks, and at the same time reduce the number of nodes involved in the original perception of cooperative sensing algorithm to some extent.