Application of Video Game Algorithm Based on Deep Q-Network Learning in Music Rhythm Teaching

The difficulty of music rhythm teaching makes its teaching effect limited. Some game algorithms show problems of stuttery and unclear picture when they are introduced into teaching, which greatly restricts the development of teaching content. Therefore, it is proposed to activate the deep Q network and improve the filter to improve the processing performance of game image data and the integrity of information retention. The model fuses the extracted features and processes them through the neural network. According to the performance characteristics and forms of music rhythm, constant Q transformation, dynamic programming algorithm and hidden Markov model are proposed to analyze and design music signal, beat point analysis and signal traveling speed, so as to realize the identification and matching of music rhythm. The performance test and application analysis of the proposed fusion improvement algorithm are carried out: the video game algorithm can effectively extract data feature information, and its accuracy rate is basically 90% or above. The improved Deep Q-network (DQN) and Artificial Fish Swarms Algorithm (AFSA) algorithms can maintain an accuracy of over 75%, followed by the ABC algorithm, which maintains an accuracy of over 70%, but exhibits significant fluctuations in the curve changes and nodes. The recognition accuracy of music signal feature extraction is over 70%, and it is less affected by signal-to-noise ratio. The improved activation function showed that the highest game score exceeded 10 points, while the scores of Rectified Linear Unit (ReLU) function and Hyperbolic tangent function (Tanh) function were basically hovering below 5 points, and the overall score curve fluctuated significantly. The recognition accuracy of music signal features extraction is above 70% and is less affected by Signal to Noise Ratio. The algorithm has a good effect of music rhythm teaching, the students' interest in classroom learning is more than 45%, and the learning weakness is less. The average score of students in music style judgment, rhythm division and index is 85 points and 90 points respectively, which is much higher than the 40 points and 63 points of traditional teaching. The proposed algorithm can better combine video games with music teaching and help students improve their learning effect under the gamification teaching method, and has good application value