Εισαγωγή στην ανάκτηση και εξόρυξη μουσικής πληροφορίας

Βιβλιογραφία

  • [1] I. Bent. Analysis, chapter Grove’s Dictionary of Music. Macmillan, London, 1980.
  • [2] D. Byrd and T. Crawford. Problems of music information retrieval in the real world. Information Processing and Management, 38(2):249-272, 2002.
  • [3] E. Cambouropoulos. From midi to traditional music notation. In Proceedings Workshop on Articial Intelligence and Music (AAAI), 2000.
  • [4] Wikipedia: The Free Encyclopedia. Sequence motif.
  • [5] M. Kassler. Toward musical information retrieval. Perspectives of New Music, 4(2):59-67, 1966.
  • [6] A. Klapuri. Signal processing methods for the automatic transcription of music. Ph.D. thesis, Tampere University of Technology, 2004.
  • [7] M. Leman. Musical audio mining. Dealing with the Data Flood: Mining Data, Text and Multimedia, 2002.
  • [8] A. Nanopoulos. Data mining techniques for complex data structures. Ph.D. thesis, Aristotle Universtiy of Thessaloniki, 2002.
  • [9] B.C. O’Connor and R.B. Wyatt. Photo Provocations. Scarecrow Press, 2004.
  • [10] J. Pickens. Harmonic modeling for polyphonic music retrieval. Ph.D. thesis, Computer Science Department, University of Massachusetts Amherst, 2004.
  • [11] Harmonet Project. Harmonet project.
  • [12] P.-Y. Rolland and J.-G. Ganascia. Pattern detection and discovery: The case of music data mining. In Proceedings Conference on Pattern Detection and Discovery, pages 190-198, 2002.
  • [13] Feist Publications, Inc. v Rural Tel. Serv. Co., 499 U.S 340, 345, (1991).
  • [14] Directive 96/9/EC on the legal protection of databases, OJ L77 27/3/96 pp.20-28.
  • [15] Copyright and Rights in Databases Regulations 1997, SI 1997/3032.
  • [16] Fixtures Marketing Ltd v Organismos prognostikon agonon podosfairou AE ECJ [C-444/02].
  • [17] Article 5, WIPO Copyright Treaty, 20 December 1996, CRNR/DC/94.
  • [18] Article 10(2), Agreements on Trade-Related Aspects of Intellectual property Rights, Marrakesh 15 April 1994, 33 I.L.M 1197.
  • [19] Chapter III, Directive 96/9/EC on the legal protection of databases, OJ L77 27/3/96.
  • [20] Regulations 12-13, Directive 96/9/EC on the legal protection of databases, OJ L77 27/3/96.
  • [21] Walter v Lane AC 359 (1900).
  • [22] Cramp v Smythson AC 329 (1944).
  • [23] Ladbroke (Football) Ltd v William Hill (Football) Ltd 1 WLR 273 (1964).
  • [24] University of London Press v University Tutorial Press 2 Ch 601 (1916).
  • [25] MAI Sys. Corp. v Peak Computer, Inc., 991 F.2d 511, 518 (9th Cir. 1993).
  • [26] Directive 2001/29/EC on certain aspects of copyright and related rights in the information society, OJ L167.
  • [27] Preamble of Article 33, Directive 2001/29/EC on certain aspects of copyright and related rights in the information society, OJ L167.
  • [28] Berne Convention for the Protection of Literary and Artistic Works, 1886. Paris Act, 24 July 1971.
  • [29] Copyright Act, 1976. Pub. L. No. 94-553, 90 Stat. 254, codified in 17 U.S.C.
  • [30] Copyright, Designs and Patents Act, 1988.
  • [31] T. Aplin. Copyright law in the digital society: the challenges of multimedia. Hart, 2005.
  • [32] T. Aplin and J. Davis. Intellectual Property Law: Text, Cases, and Materials. Oxford University Press, 2009.
  • [33] D. I. Bainbridge. Intellectual Property. Pearson, 2010.
  • [34] H. Ball. The Law of Copyright and Literary Property. Albany, N.Y., Banks and Co.; Albany, N.Y., New York City, M. Bender & Co., 1944.
  • [35] L. Bently and B. Sherman. Intellectual Property Law. Oxford University Press, 2004.
  • [36] M. W. Carroll. A primer on U.S. intellectual property rights applicable to music information retrieval systems. Intellectual Property Quarterly, 2:313-328, 2003.
  • [37] G. P. Cornish. Copyright: Interpreting the Law for Libraries, Archives and Information Services. Facet Publishing, 2004.
  • [38] W. Cornish and D. Llewelyn. Intellectual Property: Patents, Copyright, Trade Marks and Allied Rights. Sweet & Maxwell, 2003.
  • [39] European Council. Council regulation (ec) no 44/2001 of 22 december 2000 on jurisdiction and the recognition and enforcement of judgments in civil and commercial matter, 2000.
  • [40] E. Derclaye. What is a database? a critical analysis of the definition of a database in the european database directive and suggestions for an international definition. Journal of World Intellectual Property, 5(2):981-1011, 2002.
  • [41] G. Dworkin. The moral right of the author: Moral rights and the common law countries. Journal of Law and the Arts, 19:229-251, 1995.
  • [42] D. Ellis, A. Berenzweig, and B. Whitman. The “uspop2002” Pop Music data set. Visited on June 25th, 2010.
  • [43] M. Flint. A User’s Guide to Copyright. Tottel Publishing, 2006.
  • [44] Amazon Inc. Amazon. Visited on June 15th, 2011.
  • [45] Apple Inc. iTunes. Visited on June 15th, 2011.
  • [46] A. Kohn and B. Kohn. Kohn on Music Licensing. Aspen Publishers, 2002.
  • [47] M. LaFrance. Copyright Law in a Nutshell. Thomson West, 2008.
  • [48] Napster LLC. Napster. Visited on June 15th, 2011.
  • [49] D. Loundy. Revising the copyright law for electronic publishing. John Marshall, Journal of Computer and Infromational Law, (14):1-46, 1995.
  • [50] J. M. Mohler. Toward a better understanding of substantial similarity in copyright infringement cases. University of Cincinnati Law Review, 68:971-994, 2000.
  • [51] D. J. Moser. Moser on Music Copyright. Thomson Course Technology PTR, 2007.
  • [52] U.-M. Mutanen. On museums and web 2.0, 2006.
  • [53] P. Pedley. Essential Law for Information Professionals. Facet Publishing, 2003.
  • [54] R. A. Reese. Copyright and internet music transmissions: Existing law, major controversies, possible solutions. University of Miami Law Review, 55:237-274, 2000.
  • [55] C. Simpson. Copyright for Administrators. Linworth Books, 2008.
  • [56] I. A. Stamatoudi. Moral rights of authors in england: the missing emphasis on the role of creators. Intellectual Property Quarterly, pages 478-513, 1997.
  • [57] C. Swack. Safeguarding artistic creation and the cultural heritage: A comparison of droit moral between france and the united states. Journal of Law and the Arts, 22:361-401, 1988.
  • [58] N. Thakur. Database protection in the european union and the united states: the european database directive as an optimum global model? Intellectual Property Quarterly, 100:100-133, 2001.
  • [59] P. K. Yu. Conflict of Laws Issues in International Copyright Cases, 2001. Visited on November 20th, 2010.
  • [60] R. Agrawal and R. Srikant. Mining sequential patterns. In Proceedings IEEE International Conference on Data Engineering (ICDE), pages 3-14, 1995.
  • [61] M. Argamon, S. abd Saric and S. S. Stein. Style mining of electronic messages for multiple authorship discrimination: first results. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 475-480, 2003.
  • [62] J.-J. Aucouturier and M. Sandler. Finding repeating patterns in acoustic musical signals: Applications for audio thumbnailing. In Proceedings International Conference on Virtual, Synthetic and Entertainment Audio (AES), 2002.
  • [63] H. Barlow and S. Morgenstern. A Dictionary of Musical Themes. Crown, 1975.
  • [64] R. Basili, A. Serafini, and A. Stellato. Classification of musical genre: A machine learning approach. In Proceedings International Symposium on Music Information Retrieval (ISMIR), 2004.
  • [65] R. Bayardo. Efficiently mining long patterns from databases. In Proceedings ACM International Conference on Management of Data (SIGMOD), pages 85-93, 1998.
  • [66] D. Byrd and T. Crawford. Problems of music information retrieval in the real world. Information Processing and Management, 38(2):249-272, 2002.
  • [67] Don Byrd. Organization and searching of musical information, course syllabus, 2008. http://www.informatics.indiana.edu/donbyrd/Teach/I545Site--Spring08/SyllabusI545.html.
  • [68] H. Chen and A.L.P. Chen. A music recommendation system based on music data grouping and user interests. In Proceedings Conference in Information and Knowledge Management (CIKM), pages 231-238, 2001.
  • [69] T. Crawford, C.S. Iliopoulos, and R. Raman. String matching techniques for music similarity and melodic recognition. Computing in Musicology, 11:73-100, 1998.
  • [70] K. Hevner. Experimental studies of the elements of expression in music. Proceedings of American Journal of Psychology, 48(2):246-267, 1936.
  • [71] J.L. Hsu, C.C. Liu, and A.L.P. Chen. Discovering non-trivial repeating patterns in music data. IEEE Transactions on Multimedia, 3(3):311-325, 2001.
  • [72] X. Hu, J. Downie, C. Laurier, M. Bay, and A. Ehmann. The 2007 mirex audio mood classification task: Lessons learned. In Proceedings of International Conference on Music Information Retrieval, 2008.
  • [73] Xiao Hu and J. Stephen Downie. Improving mood classification in music digital libraries by combining lyrics and audio. In Proceedings of Joint Conference on Digital Libraries, pages 159-168, 2010.
  • [74] Xiao Hu, J. Stephen Downie, and Andreas F. Ehmann. Lyric text mining in music mood classification. In Proceedings of International Society for Music Information Retrieval, pages 411-416, 2009.
  • [75] C.S. Iliopoulos and M. Kurokawa. Exact & approximate distributed matching for musical melodic recognition. In Proceedings Convention on Artificial Intelligence and the Simulation of Behaviour (AISB), pages 49-56, 2002.
  • [76] C.S. Iliopoulos, M. Niyad, K. Lenstrom, and Y.J. Pinzon. Evolution of musical motifs in polyphonic passages. In Proceedings Convention on Artificial Intelligence and the Simulation of Behaviour (AISB), pages 67-75, 2002.
  • [77] Ioannis Karydis, Alexandros Nanopoulos, and Yannis Manolopoulos. Finding maximum-length repeating patterns in music databases. Multimedia Tools & Applications, 32(1):49-71, 2007.
  • [78] Y.E. Kim, E. Schmidt, R. Migneco, B.G. Morton, Richardson P., J. Scott, Speck J. A., and Turnbull D. Music emotion recognition: A state-of-the-art review. In Proceedings of International Society for Music Information Retrieval Conference, pages 255-266, 2010.
  • [79] J.L. Koh and W.D.C. Yu. Efficient feature mining in music objects. In Proceedings Conference in Database and Expert System Applications (DEXA), pages 221-231, 2001.
  • [80] A. Kornstadt. Themefinder: A web-based melodic search tool. Computing in Musicology, 11:231-236, 1998.
  • [81] Calvin K. M. Lam and Bernard C. Y. Tan. The internet is changing the music industry. Communications ACM, 44(8):6268, 2001.
  • [82] P. Lamere. Social tagging and music information retrieval. Journal of New Music Research, 37(2):101-114, 2008.
  • [83] T. Landauer, P. Foltz, and D. Laham. An introduction to latent semantic analysis. Discourse Processes, 25:259-284, 1998.
  • [84] Olivier Lartillot and Petri Toiviainen. A matlab toolbox for musical feature extraction from audio. In Proceedings of International Conference on Digital Audio Effects, 2007.
  • [85] Cyril Laurier, Jens Grivolla, and Perfecto Herrera. Multimodal music mood classification using audio and lyrics. In Proceedings of International Conference on Machine Learning and Applications, pages 688-693, 2008.
  • [86] C.-R. Lin, N.-H. Liu, Y.-H. Wu, and A.L.P. Chen. Music classification using significant repeating patterns. In Procceedings Database Systems for Advanced Applications, pages 506-518, 2004.
  • [87] D.-I. Lin and Z. Kedem. Pincer-search: An efficient algorithm for discovering the maximum frequent set. IEEE Transactions on Knowledge and Data Engineering, 14(3):553-566, 2002.
  • [88] C.C. Liu, J.L. Hsu, and A.L.P. Chen. Efficient theme and non-trivial repeating pattern discovering in music databases. In Proceedings IEEE International Conference on Data Engineering (ICDE), pages 14-21, 1999.
  • [89] B. Logan, A. Kositsky, and P. Moreno. Semantic analysis of song lyrics. In Proceedings of IEEE International Conference on Multimedia and Expo, volume 2, pages 827-830, 2004.
  • [90] D. McEnnis, C. McKay, and I. Fujinaga. jAudio: A feature extraction library. In Proceedings of International Conference on Music Information Retrieval, 2005.
  • [91] Brian McFee and Gert R. G. Lanckriet. The natural language of playlists. In Proceedings of International Society for Music Information Retrieval, pages 537-542, 2011.
  • [92] C. McKay and I. Fujinaga. Automatic genre classification using large high-level musical feature sets. In Proceedings International Symposium on Music Information Retrieval (ISMIR), pages 31-38, 2004.
  • [93] Matt McVicar, Tim Freeman, and Tijl De Bie. Mining the correlation between lyrical and audio features and the emergence of mood. In Proceedings of International Society for Music Information Retrieval, pages 783-788, 2011.
  • [94] C. Meek and W.P. Birmingham. Thematic extractor. In Proceedings International Symposium on Music Information Retrieval (ISMIR), pages 119-128, 2001.
  • [95] MIREX. Annual Music Information Retrieval eXchange. http://www.music-ir.org/mirex/wiki/MIREX_HOME.
  • [96] Alexandros Nanopoulos, Dimitrios Rafailidis, Panagiotis Symeonidis, and Yannis Manolopoulos. Musicbox: Personalized music recommendation based on cubic analysis of social tags. Transactions on Audio, Speech and Language Processing, 18(2):407-412, 2010.
  • [97] A. C. North and D. J. Hargreaves. Liking for musical styles. Musicae Scientiae, 1:109-128, 1997.
  • [98] F. Pachet and D. Cazaly. A taxonomy of musical genres, 2000.
  • [99] J. Park, M.-S. Chen, and P. Yu. Using a hash-based method with transaction trimming for mining association rules. IEEE Transactions on Knowledge and Data Engineering, 9(5):813-825, 1997.
  • [100] A. Rauber R. Mayer, R. Neumayer. Rhyme and style features for musical genre classification by song lyrics. In Proceedings of International Conference on Machine Learning and Applications, pages 337-342, 2008.
  • [101] P.-Y. Rolland and J.-G. Ganascia. Pattern detection and discovery: The case of music data mining. In Proceedings Conference on Pattern Detection and Discovery, pages 190-198, 2002.
  • [102] J.A. Russell. A circumplex model of affect. Journal of personality and social psychology, 39(6):1161-1178, 1980.
  • [103] Spyridon Saroukos. Enhancing a greek language stemmer - efficiency and accuracy improvements. Master’s thesis, Dept. of Computer Sciences, University of Tampere, Finland, 2008.
  • [104] Erik M. Schmidt and Youngmoo E. Kim. Prediction of time-varying musical mood distributions from audio. In Proceedings of International Society for Music Information Retrieval, pages 465-470, 2010.
  • [105] L. Smith and R. Medina. Discovering themes by exact pattern matching. In Proceedings International Symposium on Music Information Retrieval (ISMIR), pages 31-32, 2001.
  • [106] C. Ta-Chun, A.L.P. Chen, and L. Chih-Chin. Music databases: Indexing techniques and implementation. In Proceedings International Workshop on Multimedia Databases Management Systems, pages 46-53, 1996.
  • [107] R.E. Thayer. The biopsychology of mood & arousal. Oxford University Press, 1989.
  • [108] G. Tzanetakis, A. Ermolinskyi, and P. Cook. Pitch histograms in audio and symbolic music information retrieval. In Proceedings International Symposium on Music Information Retrieval (ISMIR), pages 31-38, 2002.
  • [109] P. van der Merwe. Origins of the Popular Style: The Antecedents of Twentieth-Century Popular Music. Oxford University Press, 1992.
  • [110] Menno van Zaanen and Pieter Kanters. Automatic mood classification using tf*idf based on lyrics. In Proceedings of International Society for Music Information Retrieval, pages 75-80, 2010.
  • [111] Dan Yang and Won-Sook Lee. Disambiguating music emotion using software agents. In Proceedings of International Conference on Music Information Retrieval, 2004.
  • [112] Dan Yang and Won-Sook Lee. Music emotion identification from lyrics. In Proceedings of IEEE International Symposium on Multimedia, pages 624-629, 2009.
  • [113] Yi-Hsuan Yang, Yu-Ching Lin, Heng-Tze Cheng, I-Bin Liao, Yeh-Chin Ho, and Homer H. Chen. Toward multi-modal music emotion classification. In Proceedings of Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing, pages 70-79, 2008.
  • [114] M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. New algorithms for fast discovery of association rules. In Proceedings International Conference on Knowledge Discovery and Data Mining (KDD), pages 283-286, 1997.
  • [115] M. Zentner, D. Grandjean, and K. R. Scherer. Emotions evoked by the sound of music: Characterization, classification, and measurement. Emotion, 8(4):494-521, 2008.
  • [116] D. P. Agrawal and Q.-A. Zeng. Introduction to Wireless and Mobile Systems. Thomson, Brooks/Cole, 2003.
  • [117] R. Agrawal, C. Faloutsos, and A. Swami. Efficient similarity search in sequence databases. In Proceedings Conference on Foundations of Data Organization and Algorithms (FODO), pages 69-84, 1993.
  • [118] R. Agrawal, K.-I. Lin, H.S. Sawhney, and K. Swim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proceedings Conference on Very Large Data Bases (VLDB), 1995.
  • [119] Jitendra Ajmera, Iain A. McCowan, and Hervé Bourlard. Speech/music discrimination using entropy and dynamism features in a hmm classification framework. Speech Communication, 40:351-363, 2003.
  • [120] Stephanos Androutsellis-Theotokis and Diomidis Spinellis. A survey of peer-to-peer content distribution technologies. ACM Computing Surveys (CSUR), 36(4):335--371, 2004.
  • [121] Apple. Podcast Resources. Let your voice be heard., 2014.
  • [122] N. Beckmann, H.P. Kriegel, and B. Seeger. The R*-tree: An efficient and robust method for points and rectangles. In Proceedings ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), pages 322-331, 1990.
  • [123] Adam Berenzweig, Beth Logan, Daniel P. W. Ellis, and Brian P. W. Whitman. A large-scale evaluation of acoustic and subjective music-similarity measures. Computer Music Journal, 28(2):63--76, 2004.
  • [124] A. Piccialli C. Roads, S. Pope and G. De Poli, editors. Musical Signal Processing. Royal Swets & Zeitlinger, 1997.
  • [125] R. Castan~eda, S. R. Das, and M. K. Marina. Query localization techniques for on-demand routing protocols in ad-hoc networks. Wireless Networks, 8(2/3):137-151, 2002.
  • [126] K. Chan and A.W.-C. Fu. Efficient time series matching by wavelets. In Proceedings International Conference on Data Engineering, pages 126-133, 1999.
  • [127] I. Daubechies, S. Mallat, and A.S. Willsky. Introduction to the special issue on wavelet transforms and multiresolution signal analysis. IEEE Transactions on Information Theory, 38(2):529-532, 1992.
  • [128] J. DeVriendt, P. Laine, C. Lerouge, and X. Xu. Mobile network evolution: A revolution on the move. IEEE Communications Magazine, pages 104-111, April 2002.
  • [129] A. Dutta, J. Chennikara, W. Chen, O. Altintas, and H. Schulzrinne. Multicasting streaming media to mobile users. IEEE Communications Magazine, pages 81-89, 2003.
  • [130] C. Faloutsos, M. Ranganathan, and Y. Manolopoulos. Fast subsequence matching in time-series databases. In Proceedings ACM International Conference on Management of Data (SIGMOD), pages 419-429, 1994.
  • [131] Y. Fang, Z.J. Haas, B. Liang, and Y.-B. Lin. TTL prediction schemes and the effects of inter-update time distribution on wireless data access. Wireless Networks, 10(5):607-619, 2004.
  • [132] Like Gao and X. Sean Wang. Continually evaluating similarity-based pattern queries on a streaming time series. In Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, SIGMOD ’02, pages 370--381, 2002.
  • [133] Like Gao, Zhengrong Yao, and X. Sean Wang. Evaluating continuous nearest neighbor queries for streaming time series via pre-fetching. In Proceedings of the Eleventh International Conference on Information and Knowledge Management, CIKM ’02, pages 485--492, 2002.
  • [134] M. Grimaldi, P. Cunningham, and A. Kokaram. An evaluation of alternative feature selection strategies and ensemble techniques for classifying music. In Proceedings Workshop on Multimedia Discovery and Mining, 2003.
  • [135] D.B. Johnson and D.A. Maltz. Dynamic source routing in ad-hoc wireless networks. In Mobile Computing, volume 353. Kluwer Academic Publishers, 1996.
  • [136] S. Kadambe and G. Faye Boudreaux-Bartels. Application of the wavelet transform for pitch detection of speech signals. IEEE Transactions on Information Theory, 38(2):917-924, 1992.
  • [137] V. Kalogeraki, D. Gunopulos, and D. Zeinalipour-Yazti. A local search mechanism for peer-to-peer networks. In Proceedings Conference on Information and Knowledge Management (CIKM), pages 300-307, 2002.
  • [138] I. Karydis, A. Nanopoulos, A. Papadopoulos, D. Katsaros, and Y. Manolopoulos. Content-based music information retrieval in wireless ad-hoc networks. In Proceedings International Symposium on Music Information Retrieval (ISMIR), pages 137-144, 2005.
  • [139] I. Karydis, A. Nanopoulos, A. Papadopoulos, and Y. Manolopoulos. Audio indexing for efficient music information retrieval. In Proceedings Multimedia Modeling Conference (MMM), pages 22-29, 2005.
  • [140] I. Karydis, A. Nanopoulos, A. N. Papadopoulos, and Y. Manolopoulos. Music retrieval in p2p networks under the warping distance. In Proceedings International Conference on Enterprise Information Systems (ICEIS), pages 100-107, 2005.
  • [141] Ioannis Karydis, Alexandros Nanopoulos, Apostolos Papadopoulos, Dimitrios Katsaros, and Yannis Manolopoulos. Music retrieval over wireless ad-hoc networks. IEEE Transactions on Audio, Speech and Language Processing, 16(6):1152-1162, 2008.
  • [142] E. Keogh and S. Kasetty. On the need for time series data mining benchmarks: A survey and empirical demonstration. In Proceedings of ACM SIGKDD Conference, pages 102-111, 2002.
  • [143] E. Keogh and A.N. Ratanamahatana. Exact indexing of dynamic time warping. Knowledge and Information Systems, 7(3):358-386, 2005.
  • [144] A. W.-C. Fu Kin-Pong Chan and C. Yu. Haar wavelets for efficient similarity search of time-series: With and without time warping. IEEE Transactions on Knowledge and Data Engineering, 15(3), 2003.
  • [145] A. Klapuri. Signal processing methods for the automatic transcription of music. Ph.D. thesis, Tampere University of Technology, 2004.
  • [146] M. Kontaki and A.N. Papadopoulos. Similarity search in streaming time sequences. In Proceedings Statistical and Scientific Database Management (SSDBM), 2004.
  • [147] Maria Kontaki, Ioannis Karydis, and Yannis Manolopoulos. Content-based information retrieval in streaming music. In Pan-Hellenic Conference in Informatics, pages 249--259, 2007.
  • [148] Maria Kontaki and Apostolos Papadopoulos. Efficient similarity search in streaming time sequences. In Proceedings of international Conference on Scientific and Statistical Database Management, 2004.
  • [149] B. Kostek and A. Wieczorkowska. Parametric representation of musical sound. In Archive of Acoustics, pages 3-26, 1997.
  • [150] T. Li, Q. Li, S. Zhu, and M. Ogihara. A survey on wavelet applications in data mining. Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) Explorations, 4(2):49-68, 2002.
  • [151] T. Li, M. Ogihara, and Q. Li. A comparative study on content-based music genre classification. In Proceedings ACM Conference on Research and Development in Information Retrieval (SIGIR), pages 282-289, 2003.
  • [152] X. Li and J. Wu. Searching techniques in peer-to-peer networks. In Handbook of Theoretical and Algorithmic Aspects of Ad Hoc, Sensor, and Peer-to-Peer Networks, 2004.
  • [153] W. Lou and J. Wu. Broadcasting in ad hoc networks using neighbor designating. In I. Maghoub and M. Ilyas, editors, Handbook of Mobile Computing. CRC Press, 2004.
  • [154] M.K. Mandal, T. Aboulnasr, and S. Panchanathan. Fast wavelet histogram techniques for image indexing. Computer Vision and Image Understanding, 75(1-2):99-110, 1999.
  • [155] MIREX. Annual Music Information Retrieval eXchange.
  • [156] PR Newswire. ‘Podcast’ Is the Word of the Year, 2006.
  • [157] S.-Y. Ni, Y.-C. Tseng, Y.-S. Chen, and J.P. Sheu. The broadcast storm problem in a mobile ad-hoc networks. In Proceedings ACM/IEEE International Conference on Mobile Computing and Networking MOBICOM, pages 151-162, 1999.
  • [158] Elias Pampalk. Audio-based music similarity and retrieval: Combining a spectral similarity model with information extracted from fluctuation patterns, 2006. Implementation submitted to the 3rd Annual Music Information Retrieval eXchange 2006.
  • [159] C. Papaodysseus, G. Roussopoulos, D. Fragoulis, Th. Panagopoulos, and C. Alexiou. A new approach to the automatic recognition of musical recordings. Jounal of Acoustical Engineering Society, 49(1/2):23-35, 2001.
  • [160] M. Paraskevas and J. Mourjopoulos. A statistical study of the variability and features of audio signals. In Audio Engineering Society, 1996.
  • [161] G. P. Premkumar. Alternative distribution strategies for digital music. Communications of the ACM, 46(9):89-95, 2003.
  • [162] Internet Radio. Free Music from Thousands of Stations.
  • [163] M. Roccetti, P. Salomoni, V. Ghini, and S. Ferretti. Bringing the wireless Internet to UMTS devices: A case study with music distribution. Multimedia Tools and Applications, 25(2):217-251, 2005.
  • [164] P. Shrestha and T. Kalker. Audio fingerprinting in peer-to-peer networks. In Proceedings International Symposium on Music Information Retrieval (ISMIR), pages 341-344, 2004.
  • [165] TuneIn. Listen to Online Radio, Music and Talk Stations.
  • [166] G. Tzanetakis, J. Gao, and P. Steenkiste. A scalable peer-to-peer system for music information retrieval. Computer Music Journal, 28(2):24-33, 2004.
  • [167] C. Wang, J. Li, and S. Shi. A kind of content-based music information retrieval method in a peer-to-peer environment. In Proceedings International Symposium on Music Information Retrieval (ISMIR), pages 178-186, 2002.
  • [168] A. Wieczorkowska. Musical sound classification based on wavelet analysis. Fundamenta Informaticae, 47(1/2):175-188, 2001.
  • [169] A. Wieczorkowska and Z. Ras. Audio content description in sound databases. In Web Intelligence: Research and Development, pages 175-183, 2001.
  • [170] B. Yang and H. Garcial-Molina. Improving search in peer-to-peer networks. In Procceedings Conference of Distributed Computer Systems, pages 5-15, 2002.
  • [171] C. Yang. Efficient acoustic index for music retrieval with various degrees of similarity. In Proceedings ACM Multimedia Conference, pages 584-591, 2002.
  • [172] C. Yang. Peer-to-peer architecture for content-based music retrieval on acoustic data. In Proceedings International World Wide Web Conference (WWW), pages 376-383, 2003.
  • [173] B.-K. Yi and C. Faloutsos. Fast time sequence indexing for arbitrary lp norms. In Proceedings Conference on Very Large Data Bases (VLDB), pages 385-394, 2000.
  • [174] B.-K. Yi, H.V. Jagadish, and C. Faloutsos. Efficient retrieval of similar time sequences under time wraping. In Proceedings IEEE International Conference on Data Engineering, pages 201-208, 1998.
  • [175] Y. Zhu and D. Shasha. Warping indexes with envelope transforms for query by humming. In Procceedings ACM Conference on Management of Data (SIGMOD), pages 181-192, 2003.
  • [176] N. Beckmann, H. P. Kriegel, and B. Seeger. The R*-tree: An efficient and robust method for points and rectangles. In Proc. ACM SIGMOD Conf., pages 322-331, 1990.
  • [177] D. Dervos, P. Linardis, and Y. Manolopoulos. S-index: a hybrid structure for text retrieval. In Proceedings of ADBIS, pages 204-209, 1997.
  • [178] C. Faloutsos. Searching Multimedia Databases by Content. Kluwer Academic Publishers, 1996.
  • [179] C. Faloutsos, M. Ranganathan, and Y. Manolopoulos. Fast subsequence matching in time-series databases. In Proceedings of the ACM SIGMOD international conference on Management of data, pages 419-429, 1994.
  • [180] I. Karydis, A. Nanopoulos, A. Papadopoulos, and Y. Manolopoulos. Audio indexing for efficient music information retrieval. In Proc. MMM Conf., pages 22-29, 2005.
  • [181] B. Kostek and A. Wieczorkowska. Parametric representation of musical sound. In Archive of Acoustics, pages 3-26, 1997.
  • [182] C. Papaodysseus, G. Roussopoulos, D. Fragoulis, Th. Panagopoulos, and C. Alexiou. A new approach to the automatic recognition of musical recordings. Jounal of Acoustical Engineering Society, 49(1/2):23-35, 2001.
  • [183] M. Paraskevas and J. Mourjopoulos. A statistical study of the variability and features of audio signals. In Audio Engineering Society, 1996.
  • [184] J. Pickens. Harmonic modeling for polyphonic music retrieval. Ph.D. thesis, University of Massachusetts at Amherst, 2004.
  • [185] J. Reiss, J.-J. Aucouturier, and M. Sandler. Efficient multidimensional searching routines for music information retrieval. In Proceedings of ISMIR, pages 163-171, 2001.
  • [186] V. S. Subrahmanian. Multimedia Database Systems. Kaufmann Publishers, 1998.
  • [187] A. Wieczorkowska. Musical sound classification based on wavelet analysis. Fundamenta Informaticae, 47(1/2):175-188, 2001.
  • [188] A. Wieczorkowska and Z. Ras. Audio content description in sound databases. In Web Intelligence: Research and Development, pages 175-183, 2001.
  • [189] J.-Y. Won, J.-H. Lee, K. Ku, J. Park, and Y.-S. Kim. A content-based music retrieval system using representative melody index from music databases. In Computer Music Modeling and Retrieval, pages 280-294, 2004.
  • [190] C. Yang. Efficient acoustic index for music retrieval with various degrees of similarity. In Proc. ACM MM Conf., pages 584-591, 2002.