基于卡尔曼滤波的乐音基频小波自相关检测法  被引量:1

An autocorrelation detection method of wavelet for musical pitch based on Kalman filter

在线阅读下载全文

作  者:张皓斐 张皓博 ZHANG Haofei;ZHANG Haobo(School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266525,China;School of Electrical Engineering and Automation,Qilu University of Technology,Jinan 250353,China)

机构地区:[1]青岛理工大学信息与控制工程学院,山东青岛266525 [2]齐鲁工业大学电气工程与自动化学院,山东济南250353

出  处:《电子设计工程》2022年第7期77-81,共5页Electronic Design Engineering

摘  要:针对乐器演奏音域宽广的特点以及加性环境噪声的干扰,传统的基频检测算法无法在整个乐音音域内都能获得精准的基音识别。鉴于此,提出了一种基于卡尔曼滤波和小波变换的乐音基频自相关检测法。该方法利用卡尔曼滤波器滤除全频段大部分噪声,而后对小波变换的各层细节分量阈值进行处理,以提高信号自相关函数波形的准确性和平滑性,从而提高乐音基频检测的精度。实验表明,该方法能够于低信噪比背景下很好地抑制乐音中混杂的白噪声,实现在全频域段内对音频质量的增强以及乐音基频的准确检测,且在全频域段内的平均识别误差率与傅里叶变换对小波变换低通分量的基频检测算法相比下降了0.89%,其鲁棒性更为理想。Inasmuch as the wide pitch range of musical instruments and the interference of additive environmental noise,the conventional algorithms of pitch detection cannot obtain accurate pitch recognition in the whole musical tone range.In consideration of this,a method of musical pitch autocorrelation detection based on Kalman filter and wavelet transform is proposed.In this method,the accuracy and smoothness of the signal’s autocorrelation function waveform are improved by utilizing Kalman filter combined with the wavelet threshold method for denoising,so as to improve the accuracy of pitch frequency detection of music.Experimental results show that the proposed method can effectively suppress the white noise,enhance the audio quality and detect the pitch frequency accurately in the full frequency domain under the condition of low Signal-to-Noise Ratio(SNR),and it is proved to be a robust method for the fact that the average error rate in the full frequency domain is reduced by 0.89%compared with the pitch detection algorithm of the Fourier transform combined with the wavelet transform method.

关 键 词:基音检测 乐音识别 卡尔曼滤波 小波变换 自相关函数 音频增强 

分 类 号:TP391.4[自动化与计算机技术—计算机应用技术]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象