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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 22-Aug-2016 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
dc.contributor.advisor 蔡瑞煌zh_TW
dc.contributor.advisor Tsaih, Rua Huanen_US
dc.contributor.author (Authors) 莊淳中zh_TW
dc.contributor.author (Authors) Chuang, Chun Chungen_US
dc.creator (作者) 莊淳中zh_TW
dc.creator (作者) Chuang, Chun Chungen_US
dc.date (日期) 2016en_US
dc.date.accessioned 22-Aug-2016 10:45:00 (UTC+8)-
dc.date.available 22-Aug-2016 10:45:00 (UTC+8)-
dc.date.issued (上傳時間) 22-Aug-2016 10:45:00 (UTC+8)-
dc.identifier (Other Identifiers) G0103356018en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/100461-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 103356018zh_TW
dc.description.abstract (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。zh_TW
dc.description.tableofcontents Chapter 1 Introduction 1
Background and Motivation 1

Chapter 2 Literature Review 3
2.1 Music Information Retrieval 3
2.2 Levels for Content Description 4
2.3 Audio Signal Processing 6
2.3.1 Terms 6
2.3.2 Framing 6
2.3.3 Spectral Analysis 7
2.4 Chord Recognition 8
2.5 Timbre Recognition 11
2.5.1 Mel-Frequency Cepstral Coefficients 11
2.6 Onset Detection 12
2.6.1 Spectral Flux 13
2.7 The Applications of to Timbre Recognition 14
2.8 Basic Musical Theory 15
2.8.1 Frequency, Pitch and Pitch Class 15
2.8.2 Interval 16
2.8.3 Scale 17
2.8.4 Chord 18
2.9 Acoustic Guitar 19
2.9.1 Guitar Chord 20

Chapter 3 Experiment 21
3.1 Data collection 21
3.2 System Architecture 22
3.2.1 Preprocessing 23
3.2.2 Model Training and Testing 24
3.2.3 Testing in noisy condition 28

Chapter 4 Evaluation 32
4.1 Comparison of Chord Type Classification 32
4.2 Comparison of Fret Position Classification 33
Chapter 5 Discussion and Conclusion 39
5.1 Conclusion 39
5.2 Limitation and Future Work 39
Reference 41		zh_TW
dc.format.extent 2057654 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103356018en_US
dc.subject (關鍵詞) 音樂資訊檢索zh_TW
dc.subject (關鍵詞) 和弦辨識zh_TW
dc.subject (關鍵詞) 音級輪廓zh_TW
dc.subject (關鍵詞) 梅爾倒頻譜係數zh_TW
dc.subject (關鍵詞) 支撐向量機zh_TW
dc.subject (關鍵詞) 吉他zh_TW
dc.title (題名) 吉他和弦把位音訊特徵萃取與辨識系統研究zh_TW
dc.title (題名) Guitar Chord Fret Position Audio Feature Extraction and Recognition Systemen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (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		zh_TW