Bayesian estimation of simultaneous musical notes based on frequency domain modelling
Bayesian estimation of simultaneous musical notes based on frequency domain modelling
复制标题
基于频域建模的同时音符贝叶斯估计
DOI：
10.1109/icassp.2004.1326824
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
S. Godsill
中科院分区：
文献类型：
--
作者：
K. Kashino;S. Godsill
本文提出了一种用于复调音乐描述的贝叶斯方法。该方法首先将输入音频信号分成一系列称为快照的部分，然后估计每个快照中包含的音符的基本频率和幅度等参数。参数估计过程是基于频域建模和吉布斯采样。从测试音符模式的音频信号中得到的实验结果令人鼓舞；当同时音符数为2时，根据半音和乐器名称估计基本频率的准确率优于80%。
The paper proposes a Bayesian method for polyphonic music description. The method first divides an input audio signal into a series of sections called snapshots, and then estimates parameters such as fundamental frequencies and amplitudes of the notes contained in each snapshot. The parameter estimation process is based on a frequency domain modelling and Gibbs sampling. Experimental results obtained from audio signals of test note patterns are encouraging; the accuracy is better than 80% for the estimation of fundamental frequencies in terms of semitones and instrument names when the number of simultaneous notes is two.