政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/100461

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/100461

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政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 > Item 140.119/100461

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/100461

題名:	吉他和弦把位音訊特徵萃取與辨識系統研究 Guitar Chord Fret Position Audio Feature Extraction and Recognition System
作者:	莊淳中 Chuang, Chun Chung
貢獻者:	蔡瑞煌 Tsaih, Rua Huan 莊淳中 Chuang, Chun Chung
關鍵詞:	音樂資訊檢索和弦辨識音級輪廓梅爾倒頻譜係數支撐向量機吉他
日期:	2016
上傳時間:	2016-08-22 10:45:00 (UTC+8)
摘要:	和弦在現代音樂當中扮演重要的角色，它能構成音樂的基礎並能表現多種變化性的聽覺感受。而吉他是一種適合作為演奏和弦的樂器，透過手指選擇在吉他指板上的音符並按壓琴弦，再配合撥弦或刷扣彈奏可以變化出許多不同的和弦。和弦辨識系統是結合音樂理論與電腦運算能力，將聲音訊號當中出現的和弦辨識出來，其已經在音樂資訊檢索領域有許多研究，也開發出許多的應用系統，以往的系統通常只辨識出和弦的名稱，但對於吉他演奏者來說，在吉他上面按壓和弦的把位，會造成音色與和聲的不同，因此本研究透過相關文獻整理，實作一個系統，觀察到音級輪廓與梅爾倒頻譜係數兩種音訊特徵，與支撐向量機監督式機器學習，能達到辨識吉他和弦把位，進而希望得到吉他音樂背後音色與和聲的高階音樂意涵。
參考文獻:	[1] Baniya, B. K., Ghimire, D. and Lee, J., "Automatic Music Genre Classification Using Timbral Texture and Rhythmic Content Features," ICACT TACT, (3:3), 2014 [2] Bharucha, J., Krumhansl, C. L., "The representation of harmonic structure in music: Hierarchies of stability as a function of context", Cognition 13, pp. 63-102, 1983 [3] Corrigall, K. A., and Schellenber., E. G., Handbook of psychology of emotions: Recent theoretical perspectives and novel empirical findings, Nova, Canada, pp. 299-326 [4] Chien, H. C., Essentials of Guitar (4th ed.), OverTop Music, Taiwan, 2004 (Chinese version) [5] Casey, M. A., Veltkamp, R., Goto, M., Leman, M., Rhodes, C. and Slaney, M., "Content-Based Music Information Retrieval: Current Directions and Future Challenges,", Proc. of the IEEE (96:4), April 2008 [6] Chuan, C. H., and Chew, E., "Audio onset detection using machine learning techniques: the effect and applicability of key and tempo information," Computer Science Department Technical Report, University of Southern California, 2008 [7] Davis, S. B. and Mermelstein, P., "Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences," IEEE Transactions on ASSP, (28:4), pp. 357-366, 1980 [8] Dosenbach, K., Fohl, W. and Meisel, A., "Identification of individual guitar sound by support vector machines," Proc. of the 11th Int. Conference on Digital Audio Effects, 2008 [9] Dixon, S., "Onset Detection Revisited," Proc. of the 9th International Conference on Digital Audio Effects, 2006 [10] Fujishima, T., "Real time chord recognition of musical sound: A system using common lisp music," ICMC, pp. 464-467, 1999. [11] Fohl, W., Turkalj, I., and Meisel A., "A Feature Relevance Study for Guitar Tone Classification," Proc. of the 13th ISMIR, 2013. [12] Gomez, E., Tonal description of music audio signals, Ph.D. thesis, UPF Barcelona, 2006. [13] Hrybyk, A. and Kim, Y. E. "Combined audio and video analysis for guitar chord identification," Proc. of the 11th ISMIR, pp.159-164, 2010. [14] Lee, J. H., "Supervised Learning for Guitar Chord Voicing Identification Aided by the Use of MIDI Pickups", 2013. [15] Lee, K., and Slaney, M., "Automatic Chord Recognition from Audio Using an HMM with Supervised Learning," Proc. of the 7th ISMIR, 2006. [16] Liu, J. and Xie, L., "SVM-Based Automatic Classification of Musical Instruments," International Conference on Intelligent Computation Technology and Automation, 2010. [17] McFadden, A., "Why 44.1 kHz? Why not 48 kHz?, CD-Recordable FAQ,", March 2016 (available online at http://stason.org/TULARC/pc/cd-recordable/2-35-Why-44-1KHz-Why-not-48KHz.html) [18] Mitra, S. L, Digital Signal Processing: A Computer-Based Approach (3rd ed.), 2006. [19] Oudre, L., Grenier, Y., and Févotte, C., "Chord Recognition by Fitting Rescaled Chroma Vectors to Chord Templates," IEEE Transactions on Audio, Speech and Language Processing, (19:7), pp.2222-2233, 2011 [20] Pan, S.W., Guitar Chord Encyclopedia (8th ed.), Vision Quest, Taiwan, 2013 (Chinese version) [21] PyMIR, https://github.com/jsawruk/pymir [22] Stark, A. M., and Plumbley, M. D., "Real-time Chord Recognition for Live Performance," ICMC, 2009. [23] Sheh, A. and Ellis, D. P., "Chord segmentation and recognition using EM-trained hidden Markov models," ISMIR, 2003. [24] Shepard, R. N., The Psychology of Music: Structural representations of musical pitch (1st ed.), Swets & Zeitlinger, Deutsch, 1982. [25] scikit-learn, http://scikit-learn.org/ [26] Spark 1.6.1 Mllib Logistic regression, http://spark.apache.org/docs/latest/ml-classification-regression.html#logistic-regression [27] Tzanetakis, G., Music Data Mining: An Introduction, pp. 44-46 pp.52. [28] Zhang, X., and Ras, Z., "Discriminant feature analysis for music timbre recognition," ECML/PKDD Third International Workshop on Mining Complex Data (MCD 2007), pp. 59-70
描述:	碩士國立政治大學資訊管理學系 103356018
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0103356018
資料類型:	thesis
顯示於類別:	[資訊管理學系] 學位論文

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