A Survey of Music Structure Analysis Techniques for Music Applications
Chapter
Abstract
Music carries multilayer information which forms different structures. The information embedded in the music can be categorized into time information, harmony/melody, music regions, music similarities, song structures and music semantics. In this chapter, we first survey existing techniques for the music structure information extraction and analysis. We then discuss how the music structure information extraction helps develop music applications. Experimental studies indicate that the success of long term music research is based on how well we integrate domain knowledge of relevant disciplines such as musicology, psychology and signal processing.
Keywords
Music Information Retrieval Music Structure Music Signal Music Content Singing Voice
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Allen, D.: Octave Discriminability of Musical and Non-musical Subjects. Journal of the Psychonomic Science 7, 421–422 (1967)Google Scholar
- 2.Alonso, M., Badeau, R., David, B., Richard, G.: Musical Tempo Estimation using Noise Subspace Projections. In: Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, New York, October 19-22 (2003)Google Scholar
- 3.Allen, P.E., Dannenberg, R.B.: Tracking Musical Beats in Real Time. In: Proc. of the International Computer Music Conference (ICMA), Glasgow, pp. 140–143 (1990)Google Scholar
- 4.Attneave, F., Olson, R.: Pitch as a Medium: A New Approach to Psychophysical Scaling. American Journal of Psychology 84, 147–166 (1971)CrossRefGoogle Scholar
- 5.Bachem, A.: A Tone Height and Tone Chroma as Two Different Pitch Qualities. International Journal of Psychonomics (Acta Psychological) 7, 80–88 (1950)Google Scholar
- 6.Bachem, A.: Time Factors in Relative and Absolute Pitch Determination. Journal of the Acoustical Society of America (JASA) 26, 751–753 (1954)CrossRefGoogle Scholar
- 7.Baratè, A., Ludovico, L.A.: An XML-based Synchronization of Audio and Graphical Representations of Music Scores. In: Proc. 8th International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2007 (2007)Google Scholar
- 8.Bartsch, M.A., Wakefield, G.H.: To Catch a Chorus: Using Chroma-based Representations for Audio Thumbnailing. In: Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, New York, October 21-24 (2001)Google Scholar
- 9.Bartsch, M.A., Wakefield, G.H.: Singing Voice Identification Using Spectral Envelope Estimation. IEEE Transaction on Speech and Audio Processing 12(2), 100–109 (2004)CrossRefGoogle Scholar
- 10.Bello, J.P., Sandler, M.B.: Phase-Based Note Onset Detection for Music Signals. In: Proc. International conference on Acoustics, Speech, and Signal processing (ICASSP), Hong Kong, April 6-10 (2003)Google Scholar
- 11.Berenzweig, A.L., Ellis, D.P.W.: Location singing voice segments within music signals. In: Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, New York, October 21-24, 2001, pp. 119–122 (2001)Google Scholar
- 12.Bharucha, J.J., Stoeckig, K.: Reaction Time and Musical Expectancy: Priming of Chords. Journal of Experimental Psychology: Human Perception and Performance 12, 403–410 (1986)CrossRefGoogle Scholar
- 13.Bharucha, J.J., Stoeckig, K.: Priming of Chords: Spreading Activation or Overlapping Frequency Spectra? Journal of Perception and Psychophysics 41(6), 519–524 (1987)Google Scholar
- 14.Biasutti, M.: Sharp Low-and High-Frequency Limits on Musical Chord Recognition. Journal of Hearing Research 105, 77–84 (1997)CrossRefGoogle Scholar
- 15.Brown, J.C.: Calculation of a Constant Q Spectral Transform. Journal of the Acoustical Society of America (JASA) 89, 425–434 (1991)CrossRefGoogle Scholar
- 16.Brown, J.C., Puckette, M.S.: An efficient algorithm for the calculation of a constant Q transform. Journal of Acoustic Society America 92(5), 1933–1941 (1992)Google Scholar
- 17.Brown, J.C., Cooke, M.: Perceptual Grouping of Musical Sounds: A Computational Model. Journal of New Music Research 23, 107–132 (1994)CrossRefGoogle Scholar
- 18.Brown, J.C.: Computer identification of musical instruments using pattern recognition with Capstral coefficients as features. Journal of Acoustic Society America 105(3), 1933–1941 (1999)CrossRefGoogle Scholar
- 19.Cemgil, A.T., Kappen, H.J., Desain, P.W.M., Honing, H.J.: On tempo tracking: Tempogram representation and Kalman filtering. Journal of New Music Research 29(4), 259–273 (2001)CrossRefGoogle Scholar
- 20.Chai, W., Vercoe, B.: Music Thumbnailing via Structural Analysis. In: Proc. ACM International conference on Multimedia (ACM MM), Berkeley, CA, USA, November 2-8, 2003, pp. 223–226 (2003)Google Scholar
- 21.Cooper, M., Foote, J.: Automatic Music Summarization via Similarity Analysis. In: Proc. 3rd International Symposium of Music Information Retrieval (ISMIR), Paris, France, October 13-17 (2002)Google Scholar
- 22.Cosi, P., DePoli, G., Prandoni, P.: Timbre characterization with Mel- Cepstrum and neural nets. In: Proc. of International Computer Music Conference (ICMC), Aarhus, Denmark, September 12 - 17, pp. 42–45 (1994)Google Scholar
- 23.Dannenberg, R.B.: An On-Line Algorithm for Real-Time Accompaniment. In: Proc. International Computer Music Conference, pp. 193–198 (1984)Google Scholar
- 24.Dannenberg, R.B., Hu, N.: Discovering Musical structure in Audio Recordings. In: Proc. 2nd International Conference of Music and Intelligence (ICMAI), Edinburgh, Scotland, UK, September 12-14, 2002, pp. 43–57 (2002)Google Scholar
- 25.Davies, M.E.P., Plumbley, M.D.: Causal Tempo Tracking of Audio. In: Proc. of 5th International Symposium/Conference of Music Information Retrieval (ISMIR), Barcelona, Spain, October 10-15 (2004)Google Scholar
- 26.Deutsch, D.: The Psychology of Music, 2nd edn. Series in Cognition and Perception. Academic Press, San Diego (1999)Google Scholar
- 27.Dixon, S.: Automatic Extraction of Tempo and Beat from Expressive Performances. Journal of New Music Research 30(1), 39–58 (2001)CrossRefGoogle Scholar
- 28.Dowling, W.J., Harwood, D.L.: Music Cognition. Series in Cognition and Perception. Academic Press, San Diego (1986)Google Scholar
- 29.Dubnov, S., Rodet, X.: Timbre Recognition with Combined Stationary and Temporal Features. In: Proc. International Computer Music Conference (ICMC), Michigan, USA, October 1-6 (1998)Google Scholar
- 30.Duxburg, C., Sandler, M., Davies, M.: A Hybrid Approach to Musical Note Onset Detection. In: Proc. of 5th International Conference on Digital Audio Effects (DAFx 2002), Hamburg, Germany, September 26-28 (2002)Google Scholar
- 31.Eggink, J., Brown, G.J.: Extracting Melody Lines from Complex Audio. In: Proc. of 5th International Symposium/Conference of Music Information Retrieval (ISMIR), Barcelona, Spain, October 10-15 (2004)Google Scholar
- 32.Ellis, D.P.W., Poliner, G.E.: Identifying ‘Cover Songs’ with Chroma Features and Dynamic Programming Beat Tracking. In: Proc. International Conference on Acoustics, Speech, and Signal Processing, ICASSP (2006)Google Scholar
- 33.Eronen, A., Klapuri, A.: Musical Instrument Recognition Using Cepstral Coefficients and Temporal Features. In: Proc. of International Conference on Acoustic, Speech and Signal Processing (ICASSP), Istanbul, Turkey, June 05-09 (2000)Google Scholar
- 34.Fletcher, H.: Some Physical Characteristics of Speech and Music. Journal of Acoustical Society of America 3(2), 1–26 (1931)CrossRefGoogle Scholar
- 35.Foote, J., Cooper, M., Girgensohn, A.: Creating Music Video using Automatic Media Analysis. In: Proc. International ACM Conference on Multimedia (ACM MM), Juan-les-Pins, France, December 1-6 (2002)Google Scholar
- 36.Fujihara, H., Goto, M., Ogata, J., Komatani, K., Ogata, T., Okuno, H.G.: Automatic Synchronization between Lyrics and Music CD Recordings based on Viterbi Alignment of Segregated Vocal Signals. In: Proc. IEEE International Symposium on Multimedia (ISMIR),Google Scholar
- 37.Fujinaga, I.: Machine Recognition of Timbre Using Steady-state Tone of Acoustic Musical Instruments. In: Proc. International Computer Music Conference (ICMC), Michigan, USA, October 1-6, pp. 207–210 (1998)Google Scholar
- 38.Fujishima, T.: Real-time Chord Recognition of Musical Sounds: A System using Common Lisp Music. In: Proc. of International Computer Music Conference (ICMC), 1999, Beijing, pp. 464–467 (1999)Google Scholar
- 39.Gao, S., Lee, C.H.: An Adaptive Learning Approach to Music Tempo and Beat Analysis. In: Proc. of International Conference on Acoustic, Speech and Signal Processing (ICASSP), Montreal, Canada, May 17-21 (2004)Google Scholar
- 40.Gao, S., Lee, C.H., Zhu, Y.: An Unsupervised Learning Approach to Music Event Detection. In: Proc. of IEEE International Conference on Multimedia and Expo. (ICME), Taipei, Taiwan, June 27-30 (2004)Google Scholar
- 41.Ghias, A., Logan, J., Chamberlin, D., Smith, B.C.: Query By Humming: Musical Information Retrieval in an Audio Database. In: 3rd ACM International conference on Multimedia (ACM MM), San Francisco, California, USA, November 5-9, pp. 231–236 (1995)Google Scholar
- 42.Goldstein, J.L.: An Optimum Processor Theory for the Central Formation of the Pitch of Complex Tones. Journal of the Acoustical Society of America (JASA) 54, 1496–1516 (1973)CrossRefGoogle Scholar
- 43.Goto, M., Muraoka, Y.: A Beat Tracking System for Acoustic Signals of Music. In: Proc. 2nd ACM International Conference on Multimedia, San Francisco, California, USA, October 15-20, pp. 365–372 (1994)Google Scholar
- 44.Goto, M.: A Predominant F0 Estimation Method for CD Recordings: MAP Estimation using EM Algorithm for Adaptive Tone Models. In: Proc. of International conference on Acoustics, Speech, and Signal processing (ICASSP), Sault lake city, Utah, May 7-11, pp. 3365–3368 (2001)Google Scholar
- 45.Goto, M.: An Audio-based Real-time Beat Tracking System for Music With or Without Drum-sounds. Journal of New Music Research 30(2), 159–171 (2001)CrossRefGoogle Scholar
- 46.Goto, M.: A Chorus-Section Detecting Method for Musical Audio Signals. In: Proc. International conference on Acoustics, Speech, and Signal processing (ICASSP), Hong Kong, April 6-10 (2003)Google Scholar
- 47.Gouyon, F., Herrera, P., Cano, P.: Pulse-Dependent Analyses of Percussive Music. In: Proc. International Conference on Virtual, Synthetic and Entertainment Audio (AES 22), Espoo, Finland, June 15-17 (2002)Google Scholar
- 48.Han, K.P., Pank, Y.S., Jeon, S.G., Lee, G.C., Ha, Y.H.: Genre Classification System on TV Sound Signals Based on a Spectrogram Analysis. IEEE Transaction on Consumer Electronics 55(1), 33–42 (1998)Google Scholar
- 49.Houtgast, T.: Sub-Harmonic Pitches of a Pure Tone at Low S/N Ratio. Journal of the Acoustical Society of America (JASA) 60(2), 405–409 (1976)CrossRefGoogle Scholar
- 50.Hartmann, W.: On the Origin of the Enlarged Melodic Octaves. Journal of the Acoustical Society of America (JASA) 93, 3400–3409 (1993)CrossRefGoogle Scholar
- 51.International Conference on Computer Music ResearchGoogle Scholar
- 52.International Society for Music Information RetrievalGoogle Scholar
- 53.Jensen, K., Andersen, T.H.: Real-time beat estimation using feature extraction. In: Wiil, U.K. (ed.) CMMR 2003. LNCS, vol. 2771, pp. 13–22. Springer, Heidelberg (2004)Google Scholar
- 54.Jiang, D.N., Lu, L., Zhang, H.J., Tao, J.H., Cai, L.H.: Music Type Classification by Spectral Contrast Feature. In: Proc. of IEEE International Conference on Multimedia and Expo. (ICME), Lausanne, Switzerland (2002)Google Scholar
- 55.Jourdain, R.: Music, The Brain, and Ecstasy: How Music Capture Our Imagination. HarperCollins (1997)Google Scholar
- 56.Journal of New Music Research Google Scholar
- 57.Journal of the Acoustical Society of America Computer Music Journal (JASA)Google Scholar
- 58.Kameoka, H., NIshimoto, T., Sagayama, S.: Separation of Harmonic Structures based on Tied Gaussian Mixture Model and Information Criterion for Concurrent Sounds. In: Proc. of International conference on Acoustics, Speech, and Signal processing (ICASSP), Montreal, Canada (May 2004)Google Scholar
- 59.Kaminskyj, I., Materka, A.: Automatic Source Identification of Monophonic Musical Instrument Sounds. In: Proc. IEEE International Conference on Neural Networks, Perth, Australia, November 27-December 1, pp. 189–194 (1995)Google Scholar
- 60.Kashino, K., Murase, H.: Music Recognition using Note Transition Context. In: Proc. of International conference on Acoustics, Speech, and Signal processing (ICASSP), Seattle, Washington, USA, May 12-15 (1998)Google Scholar
- 61.Kim, Y.K., Brian, W.: Singer Identification in Popular Music Recordings Using Voice Coding Features. In: Proc. 3rd International Symposium of Music Information Retrieval (ISMIR), Paris, France, October 13-17 (2002)Google Scholar
- 62.Klapuri, A.P.: Multiple Fundamental Frequency Estimation Based on Harmonicity and Spectral Smoothness. IEEE Transaction on Speech and Audio Processing 11(6), 804–816 (2003)CrossRefGoogle Scholar
- 63.Krishnaswamy, A.: Application of Pitch Tracking to South Indian Classical Music. In: Proc. of International conference on Acoustics, Speech, and Signal processing (ICASSP), Hong Kong, April 6-10 (2003)Google Scholar
- 64.Krumhansl, C.L.: The Psychological Representation of Musical Pitch in a Tonal Context. Journal of Cognitive Psychology 11(3), 346–374 (1979)CrossRefGoogle Scholar
- 65.Laden, B., Keefe, D.H.: The Representation of Pitch in a Neural Net Model of Chord Classification. Computer Music Journal 13(4), 12–26 (Winter 1989)CrossRefGoogle Scholar
- 66.Leung, T.W., Ngo, C.W.: ICA-FX Features for Classification of Singing Voice and Instrumental Sound. In: Proc. International Conference on Pattern Recognition (ICPR), Cambridge, UK, August 23-26 (2004)Google Scholar
- 67.Logan, B., Chu, S.: Music Summarization Using Key Phrases. In: Proc. International Conference on Acoustics, Speech, and Signal processing (ICASSP), Orlando, USA (2000)Google Scholar
- 68.Lu, L., Zhang, H.J.: Automated Extraction of Music Snippets. In: Proc. ACM International Conference on Multimedia (ACM MM), Berkeley, CA, USA, pp. 140–147 (2003)Google Scholar
- 69.Lu, L., Zhang, H.J.: Automatic Mood Detection and Tracking of Music Audio Signals. IEEE Transactions on Audio, Speech, and Language Processing 14(1) (January 2006)Google Scholar
- 70.Maddage, N.C., Xu, C.S., Kankanhalli, M.S., Shao, X.: Content-based Music Structure Analysis with Applications to Music Semantic Understanding. In: Proc. International ACM Conference on Multimedia (ACM MM), New York, USA, October 10-16 (2004)Google Scholar
- 71.Maddage, N.C.: Content-Based Music Structure Analysis. Ph.D. dissertation, School of Computing, National University of Singapore (2005)Google Scholar
- 72.Maddage, N.C., Kankanhalli, M.S., Li, H.: A Hierarchical Approach for Music Chord Modelling based on the Analysis of Tonal Characteristics. In: IEEE International Conference on Multimedia & Expo. (ICME), Toronto, Canada, July 9-12 (2006)Google Scholar
- 73.Maddage, N.C., Li, H., Kankanhalli, M.S.: Music Structure based Vector Space Retrieval. In: Proc. International Conference of ACM Special Interest Group on Information Retrieval (ACM SIGIR), pp. 67–74 (2006)Google Scholar
- 74.Martin, K.D.: Sound-Source Recognition: A Theory and Computational Model. Ph.D. dissertation, Massachusetts Institute of Technology (MIT), Media Lab, Cambridge, USA (June 1999)Google Scholar
- 75.Marques, J.: An Automatic Annotation System for Audio Data Containing Music. Master’s Thesis, Massachusetts Institute of Technology (MIT), Media Lab, Cambridge, USA (1999)Google Scholar
- 76.McKinney, M.F., Delgutte, B.: A Possible Neurophysiologic Basis of the Octave enlargement Effect. Journal of the Acoustical Society of America (JASA) 106(5), 2679–2692 (1999)CrossRefGoogle Scholar
- 77.McNab, R.J., Smith, L.A., Witten, I.H., Henderson, C.L.: Tune Retrieval in the Multimedia Library. Journal of Multimedia Tools and Applications 10(2-3), 113–132 (2000)MATHCrossRefGoogle Scholar
- 78.Miller, R.: The Structure of Singing: System and Art in Vocal Technique. Wadsworth Group/Thomson Learning, Belmont California, USA (1986)Google Scholar
- 79.Moorer, J.A.: On the Segmentation and Analysis of Continuous Musical Sound by Digital Computer. Ph.D. dissertation, Department of Computer Science, Stanford University (1975)Google Scholar
- 80.Music Information Retrieval Evaluation eXchange (MIREX )Google Scholar
- 81.Nwe, T.L., Wang, Y.: Automatic Detection of Vocal Segments in Popular Songs. In: Proc. of 5th International Symposium/Conference of Music Information Retrieval (ISMIR), Barcelona, Spain, October 10-15 (2004)Google Scholar
- 82.Ohgushi, K.: On the Role of Spatial and Temporal Cues in the Perception of the Pitch of Complex Tones. Journal of the Acoustical Society of America (JASA) 64, 764–771 (1978)CrossRefGoogle Scholar
- 83.Ohgushi, K.: The Origin of Tonality and a Possible Explanation of the Octave Enlargement Phenomenon. Journal of the Acoustical Society of America (JASA) 73, 1694–1700 (1983)CrossRefGoogle Scholar
- 84.Paulus, J., Klapuri, A.: Music Structure Analysis using a Probabilistic Fitness Measure and an Integrated Musicological Model. In: Proc. International Symposium/Conference of Music Information Retrieval, ISMIR (2008)Google Scholar
- 85.Perkins, C., Hodson, O., Hardman, V.: A Survey of Packet Loss Recovery Techniques for Streaming Audio. IEEE Network Magazine, 40–48 (September/October 1998)Google Scholar
- 86.Pikrakis, A., Antonopoulos, I., Theodoridis, S.: Music Meter and Tempo Tracking from Raw Polyphonic Audio. In: Proc. of 5th International Symposium/Conference of Music Information Retrieval (ISMIR), Barcelona, Spain, October 10-15 (2004)Google Scholar
- 87.Pinto, A., Haus, G.: A novel xml music information retrieval method using graph invariants. ACM Transactions on Information Systems (2007)Google Scholar
- 88.Poliner, G., Ellis, D., Ehmann, A., Gómez, E., Streich, S., Ong, B.: Melody Transcription from Music Audio: Approaches and Evaluation. IEEE Transaction on Audio, Speech, and Language Processing 14(4), 1247–1256 (2007)CrossRefGoogle Scholar
- 89.Pye, D.: Content-Based Methods for the management of Digital Music. In: Proc. of International conference on Acoustics, Speech, and Signal processing (ICASSP), Istanbul, Turkey, June 05-09 (2000)Google Scholar
- 90.Ritsma, R.J.: Frequency Dominant in the Perception of the Pitch of Complex Sounds. Journal of Acoustical Society of America 42(1), 191–198 (1967)CrossRefGoogle Scholar
- 91.Rossing, T.D., Moore, F.R., Wheeler, P.A.: Science of Sound, 3rd edn. Addison Wesley, Reading (2001)Google Scholar
- 92.Rudiments and Theory of Music, The associated board of the royal schools of music, 14 Bedford Square, London, WC1B 3JG (1949)Google Scholar
- 93.Saitou, T., Unoki, M., Akagi, M.: Extraction of F0 Dynamic Characteristics and Developments of F0 Control Model in Singing Voice. In: Proc. of the 8th International Conference on Auditory Display, Kyoto, Japan, July 02 – 05 (2002)Google Scholar
- 94.Sakeo, H., Chiba, S.: Dynamic Programming Algorithm Optimization for Spoken Word Recognition. IEEE Transaction on Audio, Speech, and Language Processing 26(1), 43–49 (1978)CrossRefGoogle Scholar
- 95.Scheirer, E.D.: Tempo and Beat Analysis of Acoustic Music Signals. Journal of Acoustical Society of America 103(1), 588–601 (1998)CrossRefGoogle Scholar
- 96.Scaringella, N., Zoia, G.: A Real-Time Beat Tracker for Unrestricted Audio Signals. In: Proc. of the Conference of Sound and Music Computing (JIM/CIM), Paris, France, October 20-22 (2004)Google Scholar
- 97.Scaringella, N., Zoia, G., Mlynek, D.: Automatic Genre Classification of Music Content. IEEE Signal Processing Magazine 23(2) (March 2006)Google Scholar
- 98.Sethares, W.A., Staley, T.W.: Meter and Periodicity in Music Performance. Journal of New Music Research 30(2) (June 2001)Google Scholar
- 99.Sethares, W.A., Morris, R.D., Sethares, J.C.: Beat Tracking of Musical Performances Using Low-Level Audio Features. IEEE Transactions on Speech and Audio Processing 13(2), 275–285 (2005)CrossRefGoogle Scholar
- 100.Sheh, A., Ellis, D.P.W.: Chord Segmentation and Recognition using EM-Trained Hidden Markov Models. In: Proc. 4th International Symposium of Music Information Retrieval (ISMIR), Baltimore, Maryland, USA, October 26-30 (2003)Google Scholar
- 101.Shenoy, A., Mohapatra, R., Wang, Y.: Key Detection of Acoustic Musical Signals. In: Proc. of IEEE International Conference on Multimedia and Expo. (ICME), Taipei, Taiwan, June 27-30 (2004)Google Scholar
- 102.Shepard, R.N.: Circularity in Judgments of Relative Pitch. Journal of the Acoustical Society of America (JASA) 36, 2346–2353 (1964)CrossRefGoogle Scholar
- 103.Shifrin, J., Pardo, B., Meek, C., Birmingham, W.P.: HMM-Based Musical Query Retrieval. In: Proc. of the 2nd Joint International Conference (ACM & IEEE-CS) on Digital Libraries (JCDL), Portland, Origone, USA, July 14-18, pp. 295–300 (2002)Google Scholar
- 104.Soltau, H., Schultz, T., Westphal, M., Waibel, A.: Recognition of Music Types. In: Proc. of International conference on Acoustics, Speech, and Signal processing (ICASSP), Seattle, Washington, USA, May 12-15 (1998)Google Scholar
- 105.Stevens, S.S., Volkmann, J., Newman, E.B.: A Scale for the Measurement of the Psychological Magnitude of Pitch. Journal of the Acoustical Society of America (JASA) 8(3), 185–190 (1937)CrossRefGoogle Scholar
- 106.Stevens, S.S., Volkmann, J.: The Relation of Pitch Frequency; a Relative Scale. Journal of the Acoustical Society of America (JASA) 53, 329–353 (1940)Google Scholar
- 107.Su, B., Jeng, S.: Multi-Timbre Chord Classification using Wavelet Transform and Self-organized Map Neural Networks. In: Proc. of International conference on Acoustics, Speech, and Signal processing (ICASSP), Sault lake city, Utah, vol. V, pp. 3377–3380 (2001)Google Scholar
- 108.Sundberg, J., Lindqvist, J.: Musical Octaves and Pitch. Journal of the Acoustical Society of America (JASA) 54, 922–929 (1973)CrossRefGoogle Scholar
- 109.Sundberg, J.: The Science of the Singing Voice. Northern Illinois University Press, Dekalb (1987)Google Scholar
- 110.Szczerba, M., Czyżewski, A.: Pitch estimation Enhancement Employing Neural Network-Based Music Prediction. In: Proc. 6th IASTED International Conference on Artificial Intelligence and Soft Computing (ASC), Banff, Canada, July 17-19 (2002)Google Scholar
- 111.MUSIC TECH, Ten Minute Master No 18: Song Structure, MUSIC TECH magazine, pp. 62–63 (October 2003), http://www.musictechmag.co.uk
- 112.Takeda, H., NIshimoto, T., Sagayama, S.: Rhythm and Tempo Recognition of Music Performance from a Probabilistic Approach. In: Proc. 5th International Symposium of Music Information Retrieval (ISMIR), Barcelona, Spain, October 2004, pp. 357–364 (2004)Google Scholar
- 113.Terhardt, E.: Pitch, Consonance and Harmony. Journal of the Acoustical Society of America (JASA) 55(5), 1061–1069 (1974)CrossRefGoogle Scholar
- 114.Terhardt, E.: Pitch of Complex Signals According to Virtual-Pitch Theory: Tests, Examples, and Predictions. Journal of the Acoustical Society of America (JASA) 71(3), 671–678 (1982)CrossRefGoogle Scholar
- 115.Tsai, W.H., Wang, H.M., Rodgers, D., Cheng, S.S., Yu, H.M.: Blind Clustering of Popular Music Recordings Based on Singer Voice Characteristics. In: Proc. 4th International Symposium of Music Information Retrieval (ISMIR), Baltimore, Maryland, USA, October 26-30 (2003)Google Scholar
- 116.Typke, R., Veltkamp, R.C., Wiering, F.: Searching Notated Polyphonic Music Using Transportation Distances. In: Proc. International ACM Conference on Multimedia (ACM MM), New York, USA, October 10-16 (2004)Google Scholar
- 117.Tzanetakis, G., Cook, P.: Music Genre Classification of Audio Signals. IEEE Transactions on Speech and Audio Processing 10(5), 293–302 (2002)CrossRefGoogle Scholar
- 118.Tzanetakis, G.: Song-Specific Bootstrapping of Singing Voice Structure. In: Proc. of IEEE International Conference on Multimedia and Expo. (ICME), Taipei, Taiwan, June 27-30 (2004)Google Scholar
- 119.Uhle, C., Herre, J.: Estimation of Tempo, MicroTime and Time Signature from Percussive Music. In: Proc. of the 6th International Conference on Digital Audio Effects (DAFX 2003), London, UK, September 8-11 (2003)Google Scholar
- 120.Wang, Y., Vilermo, M.: A Compressed Domain Beat Detection Using MP3 Audio Bitstreams. In: Proc. 9th ACM International Conference on Multimedia (ACM MM), Ottawa, Ontario, Canada, September 30 - October 5 (2001)Google Scholar
- 121.Wang, Y., Ahmaniemi, A., Isherwood, D., Huang, W.: Content –Based UEP: A New Scheme for Packet Loss Recovery in Music Streaming. In: Proc. ACM International conference on Multimedia (ACM MM), Berkeley, CA, USA, November 2-8 (2003)Google Scholar
- 122.Wang, Y., Kan, M.Y., Nwe, T.L., Shenoy, A., Yin, J.: LyricAlly: Automatic Synchronization of Acoustic Music Signals and Textual Lyrics. In: Proc. International ACM Conference on Multimedia (ACM MM), New York, USA, October 10-16 (2004)Google Scholar
- 123.Wang, Y., Huang, W., Korhonen, J.: A Framework for Robust and Scalable Audio Streaming. In: Proc. International ACM Conference on Multimedia (ACM MM), New York, USA, October 10-16 (2004)Google Scholar
- 124.Wah, B.W., Su, X., Lin, D.: A Survey of Error-Concealment Schemes for Real-Time Audio and Video Transmission over the Internet. In: IEEE International Symposium on Multimedia Software Engineering, Taipei, Taiwan, December 2000, pp. 17–24 (2000)Google Scholar
- 125.Ward, W.: Subjective Musical Pitch. Journal of the Acoustical Society of America (JASA) 26, 369–380 (1954)CrossRefGoogle Scholar
- 126.Wyse, L., Wang, Y., Zhu, X.: Application of a Content-Based Percussive Sound Synthesizer to Packet Loss Recovery in Music Streaming. In: Proc. ACM International conference on Multimedia (ACM MM), Berkeley, CA, USA, November 2-8 (2003)Google Scholar
- 127.Xi, S., Xu, C.S., Kankanhalli, M.S.: Unsupervised Classification of Music Genre Using Hidden Markov Model. In: Proc. of IEEE International Conference on Multimedia and Expo. (ICME), Taipei, Taiwan, June 27-30 (2004)Google Scholar
- 128.Xi, S., Maddage, N.C., Xu, C.S., Kankanhalli, M.S.: Automatic music summarization based on music structure analysis. In: Proc. Acoustics, Speech, and Signal Processing (2005)Google Scholar
- 129.Xu, C., Zhu, Y., Tian, Q.: Automatic Music Summarization Based on Temporal, Spectral and Cepstral Features. In: Proc. IEEE International Conference on Multimedia and Expo., Lausanne, Switzerland, August 26-29, pp. 117–120 (2002)Google Scholar
- 130.Xu, C.S., Maddage, N.C., Shao, X., Cao, F., Tian, Q.: Musical Genre Classification Using Support Vector Machines. In: Proc. International Conference on Acoustics, Speech, and Signal processing (ICASSP), pp. V429–V432 (2003)Google Scholar
- 131.Xu, C.S., Maddage, N.C., Shao, X.: Automatic Music Classification and Summarization. IEEE Transaction on Speech and Audio Processing 13, 441–450 (2005)CrossRefGoogle Scholar
- 132.Xu, C.S., Maddage, N.C., Shao, X., Qi, T.: Content-Adaptive Digital Music Watermarking based on Music Structure Analysis. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) 3(1) (2007)Google Scholar
- 133.Yoshioka, T., Kitahara, T., Komatani, K., Ogata, T., Okuna, H.G.: Automatic Chord Transcription with Concurrent Recognition of Chord Symbols and Boundaries. In: Proc. of 5th International Symposium/Conference of Music Information Retrieval (ISMIR), Barcelona, Spain, October 10-15 (2004)Google Scholar
- 134.Zhu, Y.: Content-Based Music Retrieval by Acoustic Query. Ph.D. dissertation, Department of Computer Science, National University of Singapore (October 2004)Google Scholar
- 135.Zhu, Y., Kankanhalli, M.S., Gao, S.: Music Key Detection for Musical Audio. In: Proc. 11th International Multimedia Modelling Conference (MMM), Melbourne, Australia, January 12-14 (2005)Google Scholar
- 136.Zhang, T., Kuo, C.C.J.: Audio Content Analysis for Online Audiovisual Data Segmentation and Classification. IEEE Transaction on Speech and Audio Processing 9(4), 441–457 (2001)CrossRefGoogle Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2009