Abstract
In the piano network teaching system, in order to ensure the teaching effect, it is necessary to monitor and evaluate the piano performance of students. In order to provide a better online piano teaching experience, it is necessary to combine machine learning and language activity detection algorithms. The machine learning algorithm can build an accurate language activity detection model by analyzing a large number of piano performance data and corresponding language activity data. As a non-contact monitoring method, light image detection can effectively detect students' piano performance while protecting their privacy. This paper presents an optical image detection method based on language activity detection algorithm. By analyzing the relationship between image features and language activities in light images, a model between light images and language activities is established. Then the model is used to detect and recognize the light image to judge whether the students are playing the piano. The experimental results show that the light image detection method based on language activity detection algorithm can accurately detect students' piano playing activities, and maintain good monitoring effect while protecting students' privacy. This method provides a new optical monitoring scheme for the simulation of piano network teaching system, which has high practicability and feasibility.
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Xiao, Y. Simulation of optical image detection based on language activity detection algorithm in piano network teaching system. Opt Quant Electron 56, 115 (2024). https://doi.org/10.1007/s11082-023-05752-2
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DOI: https://doi.org/10.1007/s11082-023-05752-2