Computer Science ›› 2020, Vol. 47 ›› Issue (3): 149-155.doi: 10.11896/jsjkx.190100224

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Musical Note Recognition of Musical Instruments Based on MFCC and Constant Q Transform

CHEN Yan-wen1,LI Kun1,HAN Yan1,WANG Yan-ping2   

  1. (Shanxi Key Laboratory of Signal Capturing & Processing, North University of China, Taiyuan 030051, China)1;
    (School of Arts, North University of China, Taiyuan 030051, China)2
  • Received:2019-01-27 Online:2020-03-15 Published:2020-03-30
  • About author:CHEN Yan-wen,born in 1994,master.His main research interests is intelligent information processing. HAN Yan,born in 1957,Ph.D,professor,Ph.D supervisor.His main research interests include information detection and processing,array information inversion and computational imaging,machine learning and artificial intelligence.

Abstract: Musical note recognition is a very important research content in the field of music signal analyzing and processing.It provides a technical basis for automatic music transcription,musical instrument tuning,music database retrieval and electronic music synthesis.In the conventional note recognition method,the musical note of one-to-one correspondence is identified by estimating the fundamental frequency of the note and the standard frequency.However,one-to-one correspondence is more difficult to identify,and the error increases as the fundamental frequency of the musical note increases.And the identifiable musical note frequency range is not wide.To this end,the paper used the idea of classification for musical note recognition,and established the required musical note library.For the importance of the low frequency information of the music signal,the Mel Frequency Cepstrum Coefficient (MFCC) and the Constant Q Transform (CQT) are selected as the note signal extraction features.The extracted features MFCC and CQT are respectively input as a note recognition single feature,and the feature fusion input is performed.Combining the advantages of Softmax regression model in multi-classification problem and the good nonlinear mapping ability and self-learning ability of BP neural network,the BP neural network multi-classification recognizer is constructed based on Softmax regression model.In the simulation environment of MATLAB R2016a,the characteristic parameters were input into the multi-classifier for learning and training,and the optimal solution was found by adjusting the network parameters.The comparative experi-ment was performed by changing the number of training samples.The experimental result data shows that when the fusion feature (MFCC+CQT) is used as the feature input,25 types of notes from the big character group to the small character group can be identified,and the average recognition rate of 95.6% can be obtained.And the feature CQT has a greater contribution than the feature MFCC in the recognition process.The experimental data fully demonstrates that using classification ideas for musical note recognition can achieve good recognition results and is not limited by the range of the musical note’s fundamental frequency.

Key words: BP neural network, Constant Q transform, Feature fusion, Mel frequency cepstrum coefficients, Music note library, Softmax regression model

CLC Number: 

  • TP391
[1]RABINER L.On the use of autocorrelation analysis for pitch detection[J].IEEE Transactions on Acoustics,Speech,and Signal Processing,2003,25(1):24-33.
[2]RODET X,DOVAL B.Maximum-likelihood harmonic matc- hing for fundamental frequency estimation[J].Journal of the Acoustical Society of America,1992,92(4):2428-2429.
[3]SUN X.Pitch determination and voice quality analysis using Subharmonic-to-Harmonic Ratio[C]∥International Conference on Acoustics.IEEE Computer Society,2002.
[4]GU Y R,YANG L.Comparison of Several Music Recognition Algorithms[J].Journal of Nanjing University of Posts and Te-lecommunications,1998(2):36-40.
[5]KADAMBE S,BOUDREAUXBARTELS G F.Application of the wavelet transform for pitch detection of speech signals[J].IEEE Transactions on Information Theory,1992,38(2):917-924.
[6]WU J J,MENG L L.Basic frequency identification of musical notes[J].Electronic Measurement Technology,2009,32(4):126-128.
[7]ZHAI J W,WANG L,DU X W.Improved pitch recognition algorithm[J].Computer Engineering and Applications,2009,45(20):228-230.
[8]XU P J,GUO L,LIU S C.Note recognition algorithm based on joint detection of pitch and endpoint[J].Journal of Computer Applications,2011,31(s2):172-175.
[9]LIU Y,ZHAO T Z,JIANG Y Q,et al.Improved recognition algorithm for piano music based on autocorrelation function[J].Journal of Wuhan University of Technology,2018,40(2):208-213.
[10]LIU T.Application and research of musical note recognition algorithm based on nonlinear feature [J].Computer and Digital Engineering,2013,41(8):1246-1248.
[11]GUERRERO-TURRUBIATES J D J,GONZALEZ-REYNA S E,LEDESMA-OROZCO S E,et al.Pitch estimation for musical note recognition using Artificial Neural Networks[C]∥International Conference on Electronics.IEEE,2014.
[12]HONG L,XIAOLI X,GUOXIN W,et al.Research on speech emotion feature extraction based on MFCC[J/OL].Journal of Electronic Measurement and Instrumentation,http://www.en.cnki.com.cn/Article_en/CJFDTotal-DZIY201703023.html.
[13]SONG Z Y.Application of MATLAB in speech signal analysis and synthesis[M].Beijing:Beijing Aerospace University Press,2013.
[14]BROWN J C.Calculation of a constant Q spectral transform[J].Journal of the Acoustical Society of America,1998,89(1):425-434.
[15]BROWN J C,PUCKETTE M S.An efficient algorithm for the calculation of a constant Q transform[J].Journal of the Acoustical Society of America,1992,92(5):2698.
[16]DOBRE R A,NEGRESCU C.Automatic music transcription software based on constant Q transform[C]∥International Conference on Electronics.IEEE,2017.
[17]ZHAO H X,YANG W S.Audio recognition of vehicle type based on short-time energy and Mel cepstrum coefficient[J].Science Technology and Engineering,2018,18(18):197-201.
[18]SUN T T.Analysis of the timbre characteristics of musical instruments [D].Jinan:Shandong University,2012.
[19]DONG C H.Matlab neural network and its application[M].Beijing:National Defence Industry Press,2007.
[20]LIU Y C,TANG Z L.Multi-classification identification method for communication signal cyclic spectrum based on softmax regression[J].Modern Electronic Technology,2018,41(3):1-5.
[21]Musical instrument music signal acquisition specification:GB/T 30414-2013[S].Beijing:China Standard Press,2013.
[22]YAN K.Research on piano sound field approximation based on microphone array [D].Guangzhou:South China University of Technology,2018.
[23]JING L,XIE L.Comparison of Performance in Automatic Classification between Chinese and Western Musical Instruments[C]∥Wase International Conference on Information Enginee-ring.2010.
[1] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[2] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[3] YU Shu-hao, ZHOU Hui, YE Chun-yang, WANG Tai-zheng. SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion [J]. Computer Science, 2022, 49(6A): 256-260.
[4] LIU Bao-bao, YANG Jing-jing, TAO Lu, WANG He-ying. Study on Prediction of Educational Statistical Data Based on DE-LSTM Model [J]. Computer Science, 2022, 49(6A): 261-266.
[5] YANG Yue, FENG Tao, LIANG Hong, YANG Yang. Image Arbitrary Style Transfer via Criss-cross Attention [J]. Computer Science, 2022, 49(6A): 345-352.
[6] CHEN Yong-ping, ZHU Jian-qing, XIE Yi, WU Han-xiao, ZENG Huan-qiang. Real-time Helmet Detection Algorithm Based on Circumcircle Radius Difference Loss [J]. Computer Science, 2022, 49(6A): 424-428.
[7] SUN Jie-qi, LI Ya-feng, ZHANG Wen-bo, LIU Peng-hui. Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation [J]. Computer Science, 2022, 49(6A): 434-440.
[8] XU Jia-nan, ZHANG Tian-rui, ZHAO Wei-bo, JIA Ze-xuan. Study on Improved BP Wavelet Neural Network for Supply Chain Risk Assessment [J]. Computer Science, 2022, 49(6A): 654-660.
[9] LAN Ling-xiang, CHI Ming-min. Remote Sensing Change Detection Based on Feature Fusion and Attention Network [J]. Computer Science, 2022, 49(6): 193-198.
[10] FAN Xin-nan, ZHAO Zhong-xin, YAN Wei, YAN Xi-jun, SHI Peng-fei. Multi-scale Feature Fusion Image Dehazing Algorithm Combined with Attention Mechanism [J]. Computer Science, 2022, 49(5): 50-57.
[11] LI Fa-guang, YILIHAMU·Yaermaimaiti. Real-time Detection Model of Insulator Defect Based on Improved CenterNet [J]. Computer Science, 2022, 49(5): 84-91.
[12] DONG Qi-da, WANG Zhe, WU Song-yang. Feature Fusion Framework Combining Attention Mechanism and Geometric Information [J]. Computer Science, 2022, 49(5): 129-134.
[13] LI Peng-zu, LI Yao, Ibegbu Nnamdi JULIAN, SUN Chao, GUO Hao, CHEN Jun-jie. Construction and Classification of Brain Function Hypernetwork Based on Overlapping Group Lasso with Multi-feature Fusion [J]. Computer Science, 2022, 49(5): 206-211.
[14] GAO Xin-yue, TIAN Han-min. Droplet Segmentation Method Based on Improved U-Net Network [J]. Computer Science, 2022, 49(4): 227-232.
[15] XU Tao, CHEN Yi-ren, LYU Zong-lei. Study on Reflective Vest Detection for Apron Workers Based on Improved YOLOv3 Algorithm [J]. Computer Science, 2022, 49(4): 239-246.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!