Wireless Play Music Beep Sensor to Assist in Music Fountain Control

J. Sensors Pub Date : 2022-08-18 DOI:10.1155/2022/7163937
Qian Wang
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Abstract

Musical water landscape combines landscape design with audiovisual art and is loved by the general public. However, the performance control form of musical water landscape generally adopts the manual offline preprogramming method, which needs to use the human subject’s consciousness to perceive the style, emotion, and other advanced features of the music first and then choreographs the corresponding landscape performance, which is a high-cost design form and low usage rate of the landscape and is not conducive to the expansion of the music library. In this paper, we design and implement a musical water landscape simulation system using OpenGL and 3Dmax technology in combination with wireless music buzzer sensors, extract and analyze the music features based on this system, and study the system control of musical water landscape. Open GL particle system technology is used to realize the dynamic simulation of weather and fountain inside the landscape and improve the drawing scheme from both modeling and rendering aspects. For the fountain, the physical model and the wireless music beep sensor are combined to design a variety of water type actions for a single spout, and the parameter interface to control the water type changes is reserved. We propose a style recognition method based on CRNN and residual network, which has a higher accuracy rate compared with the existing methods; we use the DP algorithm to segment the pitch sequence of the music to reduce the redundancy of information and improve the calculation speed of the system.
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无线播放音乐蜂鸣声传感器,以协助音乐喷泉控制
音乐水景将景观设计与视听艺术相结合,受到广大市民的喜爱。而音乐水景观的表演控制形式一般采用人工线下预编程的方式,需要利用人类主体的意识,先感知音乐的风格、情感等高级特征,再编排相应的景观表演,这是一种成本高、景观使用率低的设计形式,不利于音乐库的扩充。本文利用OpenGL和3Dmax技术,结合无线音乐蜂鸣器传感器,设计并实现了一个音乐水景仿真系统,并基于该系统对音乐水景的音乐特征进行了提取和分析,研究了音乐水景的系统控制。采用Open GL粒子系统技术,实现景观内部天气和喷泉的动态模拟,从建模和渲染两个方面对绘图方案进行改进。对于喷泉,将物理模型与无线音乐蜂鸣声传感器相结合,为单个喷口设计多种水型动作,并保留控制水型变化的参数界面。提出了一种基于CRNN和残差网络的风格识别方法,与现有方法相比具有更高的准确率;采用DP算法对音乐音高序列进行分割,减少了信息冗余,提高了系统的计算速度。
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