基于GPU通用计算平台的乐谱自动识别系统设计

Musical Notation Automatic Detection Design Based on ACF and GPU

在GPU通用计算平台上实现了一个钢琴独奏乐曲的乐谱识别系统,它读取WAV格式音频文件,利用GPU通用计算技术加速自相关函数算法来实现音高的识别,并综合考虑短时能量和基音周期的变化进行节拍划分。通过实际测试,验证了该乐谱识别系统的准确性,并证明了GPU并行计算对系统计算效率提升的效果:将计算时间减少到传统CPU计算时间的16%左右。

An automatic musical notation detection design based on autocorrelation function（ ACF） and general-purpose computing on graphics processing units（ GPU） is proposed. Audio file in. WAV format is analyzed and ACF is accelerated by the high efficiency architecture of GPU. The pitch of piano audio and short-time energy are analyzed comprehensively for rhythm calculation. An experiment is also designed to test the algorithm,as well as to check the performance of GPU. As shown from the results,the computing speed has been increased about six times with GPU.