The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First, by analyzing the application of machine learning and deep learning in the field of music, the algorithm model is designed to realize the function of intelligent music generation, which provides a theoretical basis for relevant research. Then, by (...) selecting massive music data, the music style discrimination and generation model is tested. The experimental results show that when the number of hidden layers of the designed model is 4 and the number of neurons in each layer is 1,024, 512, 256, and 128, the training result difference of the model is the smallest. The classification accuracy of jazz, classical, rock, country, and disco music types can be more than 60% using the designed algorithm model. Among them, the classification effect of jazz schools is the best, which is 77.5%. Moreover, compared with the traditional algorithm, the frequency distribution of the music score generated by the designed algorithm is almost consistent with the spectrum of the original music. Therefore, the methods and models proposed can distinguish music signals and generate different music, and the discrimination accuracy of different music signals is higher, which is superior to the traditional restricted Boltzmann machine method. (shrink)

The quality of Innovation and Entrepreneurship Education in higher institutions is closely related to the degree to which the undergraduates absorb relevant innovation and entrepreneurship knowledge and their entrepreneurial motivation. Thus, an effective Evaluation of Educational Quality is essential. In particular, fault tree analysis, a common EEQ approach, has some disadvantages, such as fault data reliance and insufficient uncertainties handleability. Thereupon, this article first puts forward a theoretical model based on the deep learning method to analyze the factors of IEE (...) quality; consequently, based on the traditional FTA, fuzzy fault tree analysis is proposed to evaluate the reliability of IEE classroom teaching for college teachers and students. Finally, based on the top event of entrepreneurial teaching failure, the hyper-ellipsoid model is implemented to restrict the interval probability of basic events and describe the deviation of uncertain events. Furthermore, the model accuracy is verified by a questionnaire survey, based upon which the factors of IEE quality are analyzed. The results show that the designed QS has good reliability, validity, and fitness; the path coefficients of cooperative ability to critical thinking and innovative thinking are 0.9 and 0.66, respectively, indicating that the students’ cooperative ability plays a vital role in the classroom teaching. By calculation, the probability of “teaching failure” in entrepreneurial classroom teaching is 0.395, 3, 0.462, and 5. To sum up, the proposed method can effectively and quantitatively evaluate the quality of IEE in higher institutions, thus providing a certain basis for formulating relevant improvement strategies. The purpose is to provide important technical support for improving the IEE quality. (shrink)

The purpose of this study was to apply deep learning to music perception education. Music perception therapy for autistic children using gesture interactive robots based on the concept of educational psychology and deep learning technology is proposed. First, the experimental problems are defined and explained based on the relevant theories of pedagogy. Next, gesture interactive robots and music perception education classrooms are studied based on recurrent neural networks. Then, autistic children are treated by music perception, and an electroencephalogram is used (...) to collect the music perception effect and disease diagnosis results of children. Due to significant advantages of signal feature extraction and classification, RNN is used to analyze the EEG of autistic children receiving different music perception treatments to improve classification accuracy. The experimental results are as follows. The analysis of EEG signals proves that different people have different perceptions of music, but this difference fluctuates in a certain range. The classification accuracy of the designed model is about 72–94%, and the average classification accuracy is about 85%. The average accuracy of the model for EEG classification of autistic children is 85%, and that of healthy children is 84%. The test results with similar models also prove the excellent performance of the design model. This exploration provides a reference for applying the artificial intelligence technology in music perception education to diagnose and treat autistic children. (shrink)