As a key field in music information retrieval, music emotion recognition is indeed a challenging task. To enhance the accuracy of music emotion classification and recognition, this paper uses the idea of inception structure to use different receptive fields to extract features of different dimensions and perform compression, expansion, and recompression operations to mine more effective features and connect the timing signals in the residual network to the GRU module to extract timing features. A one-dimensional (1D) residual Convolutional Neural Network (CNN) with an improved Inception module and Gate Recurrent Unit (GRU) was presented and tested on the Soundtrack dataset. Fast Fourier Transform (FFT) was used to process the samples experimentally and determine their spectral characteristics. Compared with the shallow learning methods such as support vector machine and random forest and the deep learning method based on Visual Geometry Group (VGG) CNN proposed by Sarkar et al., the proposed deep learning method of the 1D CNN with the Inception-GRU residual structure demonstrated better performance in music emotion recognition and classification tasks, achieving an accuracy of 84%.
With the implementation of China’s high supersonic aircraft science and technology engineering and the key basic scientific problems of near space vehicles major research programs.In this context, China is also vigorously developing high-speed high-mobility, long range and recyclable aircraft. In view of the tracking problem of a hypersonic vehicle when it reaches the capture segment, a mathod based on overload guidance law is proposed. The relationship between force and head-on angle can be deduced from the relationship between overload and head-on angle, thus transforming the overload control problem into the head-on control problem. Inversion controller design method is adopted for its parameter uncertainty rigid body model, and the derivative of virtual control amount is obtained by dynamic surface method to avoid the problem of differential expansion. Based on the secound-order tracking-differential, a nonlinear interference observer is designed to estimate and compensate for model uncertainties. The simulation results show that the controller can achieve stable tracking of the angle and high reference instructions, and has a strong robustness to the uncertainty effect of the model.
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