Analysis of Tibetan Folk Music Style Based on Audio Signal Processing
Journal of Electrical and Electronic Engineering
Volume 7, Issue 6, December 2019, Pages: 151-154
Received: Dec. 2, 2019;
Published: Dec. 3, 2019
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Ma Ying, National Experimental Teaching Demonstration Center of Communication Engineering, Qinghai University for Nationalities, Xining, China
Li Kaiyong, National Experimental Teaching Demonstration Center of Communication Engineering, Qinghai University for Nationalities, Xining, China
Hou Jiayu, National Experimental Teaching Demonstration Center of Communication Engineering, Qinghai University for Nationalities, Xining, China
Ga Zangjia, National Experimental Teaching Demonstration Center of Communication Engineering, Qinghai University for Nationalities, Xining, China
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National folk music has different styles, has extremely strong regional and national characteristics, and has a high cultural and artistic value. It carries the profound connotation of national culture. Music has non-semantic symbolicity and strong ambiguity, which makes the related research topics of music signals more challenging than speech signals. With the rapid increase of the number of digital music, due to the complexity of music itself, the ambiguity of the definition of the category of music and the limitation of the understanding of the characteristics of human auditory perception, Therefore, the analysis of the characteristics of folk music is a prerequisite for realizing the rapid and effective retrieval of folk music resources, and plays an important role in audio signal processing. However, there are few studies on the classification and information extraction of folk music. The article is based on the St-EN and St-ZCR feature extraction of the three styles of music in Aze, Le, and playing and singing in Amdo Tibetan folk music. Three kinds of musical styles have adopted a time-domain analysis is briefly analyzed Amdo Tibetan folk music signal, by extracting signal features music, We can find short-term energy than the short-time average zero-crossing rate of all types of music more clearly reflect the unique characteristics of the signal.
National Folk Music, Extraction of Music Features , Classification of Music, Short-term Energy, Short-term Zero-crossing Rate
To cite this article
Analysis of Tibetan Folk Music Style Based on Audio Signal Processing, Journal of Electrical and Electronic Engineering.
Vol. 7, No. 6,
2019, pp. 151-154.
Zhuo Macuo. Research on the Arrangement of Tibetan Folk Music [J]. Drama House, 2016 (11): 59-60.
Luo Hongyu, Li Wei. Categories and characteristics of the qinghai anduo Tibetan folk songs [J]. Journal of Qinghai normal university (philosophy and social sciences), 2012, 34 (06): 104-107.
Jue. The Classification and Distribution of Tibetan Traditional Music and Related Natural Information and Humanities [J]. Musical Art (Journal of Shanghai Conservatory of Music), 2015 (01): 123-136.
Zhang Chunlei, Zeng Xiangyang, Wang Shuguang. Endpoint detection based on critical band power spectrum variance [J]. Acoustic technology 2012, 31 (02): 204-208.
Zhao Li. Speech Signal Processing (Second Edition) [M]. Mechanical Industry Press, 2010. 37-44.
Sun ke. Research on feature extraction and classification of Chinese national music [D]. Donghua university，2017.
Feng Shang, Xu Zhongliang, Ma Lin, Li Haifeng. Study on the sparse analysis method of music signals [J]. Journal of Fudan University (Natural Science), 2018, 57 (03): 328-334.
Xu Jincai. Research on audio signal separation based on deep neural network [D]. Beijing University of Posts and Telecommunications, 2018.
Guan Xin. Research on algorithm for automatic classification of music signals [D]. Tianjin University, 2009.
Li Min, Zhang Weiwei, Jiang Mingxin, Liu Yan. Design of Music Analysis and Synthesis Based on MATLAB [J]. Journal of Dalian Nationalities University, 2010, 12 (03): 269-271.
Qin Jing. Research and application of music retrieval technology based on content and semantics [D]. Dalian University of Technology, 2018.
Deng Jianguang, Pan Xiaoheng, Lin Yuzhi. Review of instrument recognition based on acoustic characteristics [J]. Journal of dongguan institute of technology, 2012 19 (03): 58-64.
Zhou mingzhong, ji lixin. VAD algorithm based on average amplitude and weighted zero-crossing rate and its FPGA implementation [J]. Journal of university of information engineering, 2010, 11 (06): 713-718.
Y. Fuji, K. Yamamoto, and S. Nakagawa, Hidden Conditional Neural Fields for Continuous Phome Speech Recognition, IEICE Trans. Inf. & Syst., Vol. 95-D, No. 8, pp. 2094-2104, Aug. 2012.
T. N. Sainath, D. Nahamoo, B. Ramabhadran, D. Kanevsky, V. Goel, and P. M. Shah, Exemplar-based sparse representation phone identification features,” Proc. ICASSP, pp. 4492–4495, 2011.