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In the backdrop of evolving technological landscapes, this paper delves into the utilization of computer technology, specifically Artificial Neural Networks (ANN), in enhancing piano improvisation instruction. Recognizing the growing demand for innovative teaching methods, our study aims to evaluate the practical effectiveness of ANN-based teaching evaluation in real-world piano improvisation settings. Through rigorous testing, we found that ANN demonstrates remarkable precision and consistency in assessing piano improvisation skills. In comparison to the traditional decision tree algorithm, ANN excels in managing complex nonlinear relationships, providing more accurate and reliable scoring results. This integration of technology not only elevates students’ performance but also fosters their musical creativity and perception. Our findings suggest potential improvements in refining instructional methodologies and expanding the use of computer technology in piano teaching. This underscores the importance of personalized teaching, blended learning models, and technical proficiency training for educators. Overall, our research methodologies and findings significantly contribute to advancing the modernization and technological progress of music education, equipping piano instructors with cutting-edge teaching tools and strategies.

Piano is a pass in the field of music and has been popular among the public. With more and more people joining the ranks of learning piano, traditional piano teaching can no longer meet the needs of piano learners in time and space. In order to improve the quality and effectiveness of piano teaching, this paper effectively integrates machine learning algorithms into the teaching system. Specifically, we constructed a piano gesture recognition model based on the Extreme Learning Machine (ELM) algorithm to achieve accurate recognition and analysis of student piano playing gestures. The experimental results show that the piano gesture recognition model based on the extreme learning machine algorithm combined with sensors can effectively obtain the dynamic information of the learner’s hand joints and present it intuitively. Compared with the traditional recognition methods, the model has a substantial improvement in the recognition fight rate. In addition, in the comparison experiments, the k-means model shows better comprehensive classification performance and is able to recommend appropriate piano learning resources for learners based on the classification results. The results of the application experiments show that the model is still able to respond to the corresponding requests in a shorter period of time as the number of learner requests increases, and recommend the corresponding resources for the learners, with a success rate of over 99%.

In order to improve the teaching level of digital piano collective course for preschool education major in normal universities and provide high-quality music education for children, a knowledge-based system for questionnaires evaluation of digital piano collective course for preschool education major in normal universities is proposed. The system can collect sufficient data and information, the exact orientation of piano learning, the exact choice of teaching mode by questionnaires. A comprehensive analysis of the students’ characteristics, teachers’ post skills and the orientation of digital piano syllabus in the preschool collective course teaching of digital piano in normal universities is made. Moreover, some countermeasures for perfecting digital piano teaching mode are proposed. As a result, it promotes the level of piano teaching in preschool education major, realizes the goal of arousing students’ enthusiasm for study and reduces teachers’ pressure.

With the development of Internet technology, music videos on the network are becoming increasingly rich. How to extract concert video clips for specific scenes or shots from massive video libraries or ultra-long video files is a relatively difficult issue. Traditional music video retrieval methods are mostly based on key text retrieval. However, they cannot meet the needs of users. At the same time, in response to the demand for specific videos in piano performance teaching, it is also difficult for these methods to filter out key music clips from numerous videos. Therefore, a music video retrieval technology is constructed based on video feature similarity calculation. Aiming at the shortcomings of video similarity calculation methods, a dynamic programming algorithm is used to improve it. The improved music video retrieval technology is applied to the classroom learning practice of piano performance teaching, verifying the actual effect of this technology. The experimental results show that the accuracy of the music video retrieval technology reaches 91.02%. After being applied to piano classroom teaching, the overall performance of students has been improved. This shows that the proposed music video retrieval technology can effectively achieve the retrieval of required videos and improve the effectiveness of piano classroom teaching.