References
- 1Allamanche, E. (2001). Content-based identification of audio material using MPEG-7 low level description. In Proceedings of the 2nd International Symposium on Music Information Retrieval.
- 2Bellettini, C., & Mazzini, G. (2008). Reliable automatic recognition for pitch-shifted audio. In Proceedings of 17th International Conference on Computer Communications and Networks (ICCCN 2008), 838–843.
IEEE . DOI: 10.1109/ICCCN.2008.ECP.157 - 3Bertin-Mahieux, T., Ellis, D. P., Whitman, B., & Lamere, P. (2011). The Million Song Dataset. In Proceedings of the 12th International Conference on Music Information Retrieval.
- 4Bogdanov, D., Wack, N., Gómez, E., Gulati, S., Herrera, P., Mayor, O., Roma, G., Salamon, J., Zapata, J., & Serra, X. (2013). ESSENTIA: An audio analysis library for music information retrieval. In Proceedings of the 14th International Conference on Music Information Retrieval, 493–498. DOI: 10.1145/2502081.2502229
- 5Bressan, F., Six, J., & Leman, M. (2017). Applications of duplicate detection: linking meta-data and merging music archives. The experience of the IPEM historical archive of electronic music. In Proceedings of 4th International Digital Libraries for Musicology workshop (DLfM 2017), 45–48. Shanghai (China).
ACM Press . DOI: 10.1145/3144749.3144759 - 6Cano, P., Batlle, E., Kalker, T., & Haitsma, J. (2005). A review of audio fingerprinting. The Journal of VLSI Signal Processing, 41, 271–284. DOI: 10.1007/s11265-005-4151-3
- 7Coover, B., & Han, J. (2014). A power mask based audio fingerprint. In IEEE International Conference on Acoustics, Speech and Signal Processing, 1394–1398. DOI: 10.1109/ICASSP.2014.6853826
- 8Cotton, C. V., & Ellis, D. P. W. (2010). Audio fingerprinting to identify multiple videos of an event. In IEEE International Conference on Acoustics Speech and Signal Processing, 2386–2389. DOI: 10.1109/ICASSP.2010.5496185
- 9Ellis, D. P., Whitman, B., & Porter, A. (2011). Echoprint: An open music identification service. In Proceedings of the 12th International Society for Music Information Retrieval Conference.
- 10Fenet, S., Richard, G., & Grenier, Y. (2011). A Scalable Audio Fingerprint Method with Robustness to Pitch-Shifting. In Proceedings of the 12th International Society for Music Information Retrieval Conference, 121–126.
- 11Fischinger, T. (2013). Preface by the guest editor of the special issue.
- 12Haitsma, J., & Kalker, T. (2002). A highly robust audio fingerprinting system. In Proceedings of the 3rd International Conference on Music Information Retrieval.
- 13Haitsma, J., & Kalker, T. (2003). A highly robust audio fingerprinting system with an efficient search strategy. Journal of New Music Research, 32(2), 211–221. DOI: 10.1076/jnmr.32.2.211.16746
- 14Herre, J., Hellmuth, O., & Cremer, M. (2002). Scalable robust audio fingerprinting using MPEG-7 content description. In IEEE Workshop on Multimedia Signal Processing, 165–168. DOI: 10.1109/MMSP.2002.1203273
- 15Jackson, M., Crouch, S., & Baxter, R. (2011). Software evaluation: criteria-based assessment.
- 16Knorr-Cetina, K. (1999). Epistemic Cultures: How the Sciences Make Knowledge. Harvard University Press.
- 17Leonelli, S. (2016). Locating ethics in data science: responsibility and accountability in global and distributed knowledge production systems. Philosophical Transactions of the Royal Society A, 374. DOI: 10.1098/rsta.2016.0122
- 18Leonelli, S., & Ankeny, R. A. (2015). Repertoires: How to transform a project into a research community. BioScience, 65(7), 701–708. DOI: 10.1093/biosci/biv061
- 19Levin, N., Leonelli, S., Weckowska, D., Castle, D., & Dupré, J. (2016). How do scientists define openness? Exploring the relationship between open science policies and research practice. Bulletin of Science, Technology and Society, 36(2):128–141. DOI: 10.1177/0270467616668760
- 20Malekesmaeili, M., & Ward, R. K. (2013). A local fingerprinting approach for audio copy detection. Computing Research Repository (CoRR), abs/1304.0793.
- 21Mauch, M., & Ewert, S. (2013). The audio degradation toolbox and its application to robustness evaluation. In Proceedings of the 14th International Society for Music Information Retrieval Conference, 83–88.
- 22Mesirov, J. P. (2010). Accessible reproducible research. Science, 327(5964), 415–416. DOI: 10.1126/science.1179653
- 23Nosek, B. A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler, S. J., Buck, S., Chambers, C. D., Chin, G., Christensen, G., Contestabile, M., Dafoe, A., Eich, E., Freese, J., Glennerster, R., Goroff, D., Green, D. P., Hesse, B., Humphreys, M., Ishiyama, J., Karlan, D., Kraut, A., Lupia, A., Mabry, P., Madon, T., Malhotra, N., Mayo-Wilson, E., McNutt, M., Miguel, E., Paluck, E. L., Simonsohn, U., Soderberg, C., Spellman, B. A., Turitto, J., VandenBos, G., Vazire, S., Wagenmakers, E. J., Wilson, R., & Yarkoni, T. (2015). Promoting an open research culture. Science, 348(6242), 1422–1425. DOI: 10.1126/science.aab2374
- 24Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251),
aac4716 . DOI: 10.1126/science.aac4716 - 25Ouali, C., Dumouchel, P., & Gupta, V. (2014). A robust audio fingerprinting method for content-based copy detection. In 12th International Workshop on Content-Based Multimedia Indexing, 1–6.
IEEE . DOI: 10.1109/CBMI.2014.6849814 - 26Pashler, H., & Wagenmakers, E.-J. (2012). Editors’ introduction to the special section on replicability in psychological science: A crisis of confidence? Perspectives on Psychological Science, 7(6), 528–530. DOI: 10.1177/1745691612465253
- 27Peeters, G., & Fort, K. (2012). Towards a (better) definition of the description of annotated MIR corpora. In Proceedings of the 13th International Society for Music Information Retrieval Conference, 25–30.
- 28Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060), 1226–1227. DOI: 10.1126/science.1213847
- 29Plapous, C., Berrani, S.-A., Besset, B., & Rault, J.-B. (2017). A low-complexity audio fingerprinting technique for embedded applications. Multimedia Tools and Applications, 1–20.
- 30Porter, A., Bogdanov, D., Kaye, R., Tsukanov, R., & Serra, X. (2015). AcousticBrainz: A community platform for gathering music information obtained from audio. In Proceedings of the International Society for Music Information Retrieval Conference, 786–792.
- 31Ramona, M., Fenet, S., Blouet, R., Bredin, H., Fillon, T., & Peeters, G. (2012). A public audio identification evaluation framework for broadcast monitoring. Applied Artificial Intelligence, 26(1–2), 119–136. DOI: 10.1080/08839514.2012.629840
- 32Ramona, M., & Peeters, G. (2013). AudioPrint: An effcient audio fingerprint system based on a novel cost-less synchronization scheme. In Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing, 818–822. DOI: 10.1109/ICASSP.2013.6637762
- 33Six, J., Bressan, F., & Leman, M. (2018). Applications of duplicate detection in music archives: From metadata comparison to storage optimisation – the case of the Belgian Royal Museum for Central Africa. In Proceedings of the 13th Italian Research Conference on Digital Libraries (IRCDL 2018). Available at:
https://link.springer.com/chapter/10.1007/978-3-319-73165-0_10 . - 34Six, J., & Leman, M. (2014). Panako: A scalable acoustic fingerprinting system handling time-scale and pitch modification. In Proceedings of the International Society for Music Information Retrieval Conference.
- 35Six, J., & Leman, M. (2015). Synchronizing multimodal recordings using audio-to-audio alignment. Journal of Multimodal User Interfaces, 9(3), 223–229. DOI: 10.1007/s12193-015-0196-1
- 36Sonnleitner, R., Arzt, A., & Widmer, G. (2016). Landmark-based audio fingerprinting for DJ mix monitoring. In Proceedings of the International Society for Music Information Retrieval Conference, 185–191.
- 37Sonnleitner, R., & Widmer, G. (2016). Robust quad-based audio fingerprinting. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(3), 409–421. DOI: 10.1109/TASLP.2015.2509248
- 38Sturm, B. L. (2012). Two systems for automatic music genre recognition: What are they really recognizing? In Proceedings of the Second International ACM Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies, 69–74. DOI: 10.1145/2390848.2390866
- 39Sturm, B. L., & Noorzad, P. (2012). On automatic music genre recognition by sparse representation classification using auditory temporal modulations. In Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval, 379–394.
- 40Van Balen, J., Serrà, J., & Haro, M. (2012). Sample identification in hip hop music. In Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval, 301–312.
- 41Wang, A., & Culbert, D. (2009). Robust and invariant audio pattern matching. US Patent 7(627), 477.
- 42Wang, A. L.-C. (2003). An industrial-strength audio search algorithm. In Proceedings of the 4th International Conference on Music Information Retrieval, 7–13.
- 43y Arcas, B. A., Gfeller, B., Guo, R., Kilgour, K., Kumar, S., Lyon, J., Odell, J., Ritter, M., Roblek, D., Sharifi, M., & Velimirović, M. (2017). Now playing: Continuous low-power music recognition. Computing Research Repository (CoRR), abs/1711.10958.
- 44Zhu, B., Li, W., Wang, Z., & Xue, X. (2010). A novel audio fingerprinting method robust to time scale modification and pitch shifting. In Proceedings of the International Conference on Multimedia, 987–990.
ACM . DOI: 10.1145/1873951.1874130