Research Article
Design of Mobile Teaching Platform for Vocal Piano Accompaniment Course Based on Feature Comparison
@INPROCEEDINGS{10.1007/978-3-030-94554-1_34, author={Ying Chen and Jia-yin Chen and Ai-ping Zhang}, title={Design of Mobile Teaching Platform for Vocal Piano Accompaniment Course Based on Feature Comparison}, proceedings={Advanced Hybrid Information Processing. 5th EAI International Conference, ADHIP 2021, Virtual Event, October 22-24, 2021, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2022}, month={1}, keywords={Feature comparison Vocal music Piano accompaniment course Mobile teaching platform}, doi={10.1007/978-3-030-94554-1_34} }- Ying Chen
Jia-yin Chen
Ai-ping Zhang
Year: 2022
Design of Mobile Teaching Platform for Vocal Piano Accompaniment Course Based on Feature Comparison
ADHIP PART 2
Springer
DOI: 10.1007/978-3-030-94554-1_34
Abstract
With the rapid development of the Internet, the current education system courses often use mobile teaching platforms to improve teaching effects. However, due to the large and unclear feature extraction range, the established mobile teaching platform database information is confused, and the stored data resources are not comprehensive. Poor operation stability, etc. In order to improve the mobile teaching effect of vocal piano accompaniment courses, the text designs a mobile teaching platform for vocal piano accompaniment courses based on feature comparison. By constructing a mobile teaching platform framework for vocal piano accompaniment courses, we will improve and update the mobile teaching platform resource database and expand the content of resources. Design the course teaching evaluation module, use feature comparison technology to extract the fundamental frequency parameters of vocal piano accompaniment and Mel cepstrum parameters, create a scoring mechanism to compare vocal piano accompaniment, to evaluate the teaching effect of vocal piano accompaniment course, and complete the course teaching evaluation module design. Experimental results show that in the actual application process of the mobile teaching platform in this paper, the delay time is 1.9 s, the response speed is fast, the memory occupancy rate is only 35%, and the stability is high, which can effectively improve the actual teaching effect.
