References
-
1
Befus,
C. (2010).
Design and evaluation of dynamic feature-based segmentation on music . Master’s thesis, University of Lethbridge, Lethbridge, Alberta, Canada. - 2 Bimbot, F., Blouch, O. L., Sargent, G., & Vincent, E. (2010). Decomposition into autonomous and comparable blocks: A structural description of music pieces. In Proc. of the 11th International Society for Music Information Retrieval Conference, pages 189–194. Utrecht, The Netherlands.
- 3 Bimbot, F., Sargent, G., Deruty, E., Guichaoua, C., & Vincent, E. (2014). Semiotic description of music structure: An introduction to the Quaero/Metiss structural annotations. In Proc. of the AES 53rd Conference on Semantic Audio.
- 4 Bittner, R., Fuentes, M., Rubinstein, D., Jansson, A., Choi, K., & Kell, T. (2019). mirdata: Software for reproducible usage of datasets. In Proc. of the 20th International Society for Music Information Retrieval Conference, pages 99–106. Delft, The Netherlands.
- 5 Bruderer, M. J. (2008). Perception and Modeling of Segment Boundaries in Popular Music. PhD thesis, Technische Universiteit Eindhoven.
- 6 Bruderer, M. J., McKinney, M. F., & Kohlrausch, A. (2009). The perception of structural boundaries in melody lines of Western popular music. Musicæ Scientiæ, 13(2), 273–313. DOI: 10.1177/102986490901300204
- 7 Cambouropoulos, E. (2001). The local boundary detection model (LBDM) and its application in the study of expressive timing. In Proc. of the International Computer Music Conference, pages 17–22. La Havana, Cuba.
-
8
Cannam,
C.,
Landone,
C., &
Sandler,
M. (2010).
Sonic Visualiser: An open source application for viewing,
analysing, and annotating music audio files. In
Proc. of the 18th ACM International Conference on
Multimedia, pages 1467–1468.
ACM . DOI: 10.1145/1873951.1874248 - 9 Chen, T.-P., & Su, L. (2019). Harmony Transformer: Incorporating chord segmentation into harmony recognition. In Proc. of the 20th International Society for Music Information Retrieval Conference, pages 259–267. Delft, The Netherlands.
- 10 Cheng, T., Smith, J. B. L., & Goto, M. (2018). Music structure boundary detection and labelling by a deconvolution of path-enhanced self-similarity matrix. In IEEE International Conference on Acoustics, Speech and Signal Processing, pages 106–110. Calgary, Alberta, Canada. DOI: 10.1109/ICASSP.2018.8461319
- 11 Collins, T., Arzt, A., Flossman, S., & Widmer, G. (2013). SIARCT-CFP: Improving precision and the discovery of inexact musical patterns in point-set representations. In Proc. of the 14th International Society for Music Information Retrieval Conference, pages 549–554. Curitiba, Brazil.
-
12
Dannenberg,
R. B., &
Goto,
M. (2008).
Music structure analysis from acoustic signals . In Havelock, D., Kuwano, S., & Vorländer, M., editors, Handbook of Signal Processing in Acoustics, pages 305–331. Springer, New York, NY. DOI: 10.1007/978-0-387-30441-0_21 - 13 Dhariwal, P., Jun, H., Payne, C., Kim, J. W., Radford, A., & Sutskever, I. (2020). Jukebox: A generative model for music. arXiv preprint 2005.00341.
-
14
Dieleman,
S., van
den Oord, A., &
Simonyan,
K. (2018).
The challenge of realistic music generation: Modelling raw audio at scale . In Advances in Neural Information Processing Systems 31, pages 7989–7999. Curran Associates, Inc. - 15 Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2017). Density estimation using real NVP. In 5th International Conference on Learning Representations (ICLR). Toulon, France.
- 16 Dong, H.-W., Hsiao, W.-Y., Yang, L.-C., & Yang, Y.-H. (2018). MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In Proc. of the 32nd AAAI Conference on Artificial Intelligence. New Orleans, LA, USA.
- 17 Engel, J., Agrawal, K. K., Chen, S., Gulrajani, I., Donahue, C., & Roberts, A. (2019). GANSynth: Adversarial neural audio synthesis. In 7th International Conference on Learning Representations (ICLR). New Orleans, LA, USA.
- 18 Flexer, A., & Grill, T. (2016). The problem of limited inter-rater agreement in modelling music similarity. Journal of New Music Research, 45(3), 239–251. DOI: 10.1080/09298215.2016.1200631
- 19 Foote, J. (2000). Automatic audio segmentation using a measure of audio novelty. In Proc. of the IEEE International Conference of Multimedia and Expo (ICME), pages 452–455. New York City, NY, USA. DOI: 10.1109/ICME.2000.869637
- 20 Fuentes, M., Maia, L. S., & Biscainho, L. W. P. (2019a). Tracking beats and microtiming in Afro-Latin American music using conditional random fields and deep learning. In Proc. of the 20th International Society for Music Information Retrieval Conference, pages 251–258. Delft, The Netherlands.
- 21 Fuentes, M., McFee, B., Crayencour, H., Essid, S., & Bello, J. (2019b). A music structure informed downbeat tracking system using skip-chain conditional random fields and deep learning. In Proc. of the 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 481–485. DOI: 10.1109/ICASSP.2019.8682870
- 22 Gemmeke, J. F., Ellis, D. P., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., & Ritter, M. (2017). Audio Set: An ontology and humanlabeled dataset for audio events. Proc. of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing, pages 776–780. DOI: 10.1109/ICASSP.2017.7952261
- 23 Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems 27, pages 2672–2680.
- 24 Goto, M. (2003). A chorus-section detecting method for musical audio signals. In Proc. of the 28th IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 437–440. Hong Kong, China.
- 25 Goto, M. (2006a). AIST annotation for the RWC Music Database. In Proc. of the 7th International Conference on Music Information Retrieval, pages 359–360. Victoria, BC, Canada.
- 26 Goto, M. (2006b). A chorus section detection method for musical audio signals and its application to a music listening station. IEEE Transactions on Audio, Speech, and Language Processing, 14(5), 1783–1794. DOI: 10.1109/TSA.2005.863204
- 27 Goto, M., Yoshii, K., Fujihara, H., Mauch, M., & Nakano, T. (2011). Songle: A web service for active music listening improved by user contributions. In Proc. of the 12th International Society for Music Information Retrieval Conference, pages 311–316. Miami, FL, USA.
- 28 Grill, T., & Schlüter, J. (2015a). Music boundary detection using neural networks on combined features and two-level annotations. In Proc. of the 16th International Society for Music Information Retrieval Conference. Málaga, Spain. DOI: 10.1109/EUSIPCO.2015.7362593
- 29 Grill, T., & Schlüter, J. (2015b). Music boundary detection using neural networks on spectrograms and self-similarity lag matrices. In Proc. of the 23rd European Signal Processing Conference (EUSIPCO). Nice, France. DOI: 10.1109/EUSIPCO.2015.7362593
- 30 Groves, R. (2016). Automatic melodic reduction using a supervised probabilistic context-free grammar. In Proc. of the 17th International Society for Music Information Retrieval Conference, pages 775–781. New York, NY, USA.
- 31 Guérin, É., Digne, J., Galin, É., Peytavie, A., Wolf, C., Benes, B., & Martinez, B. (2017). Interactive example-based terrain authoring with conditional generative adversarial networks. ACM Transactions on Graphics, 36(6), 228:1–228:13. DOI: 10.1145/3130800.3130804
- 32 Hamanaka, M., Hirata, K., & Tojo, S. (2006). Implementing “A Generative Theory of Tonal Music”. Journal of New Music Research, 35(4), 249–277. DOI: 10.1080/09298210701563238
- 33 Hargreaves, S., Klapuri, A., & Sandler, M. (2012). Structural segmentation of multitrack audio. IEEE Transactions on Audio, Speech, and Language Processing, 20(10), 2637–2647. DOI: 10.1109/TASL.2012.2209419
- 34 Humphrey, E. J., Bello, J. P., & LeCun, Y. (2012). Moving beyond feature design: Deep architecture and automatic feature learning in music informatics. In Proc. of the 13th International Society for Music Information Retrieval Conference, pages 403–408. Porto, Portugal.
- 35 Humphrey, E. J., Salamon, J., Nieto, O., Forsyth, J., Bittner, R. M., & Bello, J. P. (2014). JAMS: A JSON annotated music specification for reproducible MIR research. In Proc. of the 15th International Society for Music Information Retrieval Conference, pages 591–596. Taipei, Taiwan.
- 36 Janssen, B., de Haas, W., Volk, A., & Van Kranenburg, P. (2013). Discovering repeated patterns in music: State of knowledge, challenges, perspectives. In Proc. of the 10th International Symposium on Computer Music Multidisciplinary Research (CMMR), pages 225–240. Marseille, France.
- 37 Jhamtani, H., & Berg-Kirkpatrick, T. (2019). Modeling self-repetition in music generation using generative adversarial networks. In Machine Learning for Music Discovery Workshop, ICML. Long Beach, USA.
- 38 Kaiser, F., & Peeters, G. (2013). A simple fusion method of state and sequence segmentation for music structure discovery. In Proc. of the 14th International Society for Music Information Retrieval Conference. Curitiba, Brazil.
- 39 Kaiser, F., & Sikora, T. (2010). Music structure discovery in popular music using non-negative matrix factorization. In Proc. of the 11th International Society for Music Information Retrieval Conference, pages 429–434. Utrecht, Netherlands.
- 40 Kim, J. W., & Bello, J. P. (2019). Adversarial learning for improved onsets and frames music transcription. In Proc. of the 20th International Society for Music Information Retrieval Conference, pages 670–677. Delft, The Netherlands.
-
41
Kingma,
D. P., &
Dhariwal,
P. (2018).
Glow: Generative flow with invertible 1 × 1 convolutions . In Advances in Neural Information Processing Systems 31, pages 10215–10224, Montreal, Canada. - 42 Kinnaird, K. M. (2016). Aligned hierarchies: A multiscale structure-based representation for musicbased data streams. In Proc. of the 17th International Society for Music Information Retrieval Conference, pages 337–343. New York City, NY, USA.
- 43 Kinnaird, K. M. (2018). Aligned sub-hierarchies: A structure-based approach to the cover song task. In Proc. of the 19th International Society for Music Information Retrieval Conference, pages 585–591. Paris, France.
- 44 Klien, V., Grill, T., & Flexer, A. (2012). On automated annotation of acousmatic music. Journal of New Music Research, 41(2), 153–173. DOI: 10.1080/09298215.2011.618226
- 45 Lerdahl, F., & Jackendoff, R. (1983). A Generative Theory of Tonal Music. MIT Press.
- 46 Levy, M., & Sandler, M. (2008). Structural segmentation of musical audio by constrained clustering. IEEE Transactions on Audio, Speech, and Language Processing, 16(2), 318–326. DOI: 10.1109/TASL.2007.910781
- 47 Levy, M., Sandler, M., & Casey, M. (2006). Extraction of high-level musical structure from audio data and its application to thumbnail generation. In Proc. of the 31st IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 5. DOI: 10.1109/ICASSP.2006.1661200
- 48 Liem, C. C., Gomez, E., & Schedl, M. (2015). PHENICX: Innovating the classical music experience. In Proc. of the 2015 IEEE International Conference on Multimedia and Expo Workshops, pages 3–6. Torino, Italy. DOI: 10.1109/ICMEW.2015.7169835
- 49 Logan, B., & Chu, S. (2000). Music summarization using key phrases. In Proc. of the 25th IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 2, pages 749–752. Istanbul, Turkey. DOI: 10.1109/ICASSP.2000.859068
- 50 Lukashevich, H. (2008). Towards quantitative measures of evaluating song segmentation. In Proc. of the 9th International Conference on Music Information Retrieval, pages 375–380. Philadelphia, PA, USA.
- 51 Maezawa, A. (2019). Music boundary detection based on a hybrid deep model of novelty, homogeneity, repetition and duration. In Proc. of the 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 206–210. Brighton, United Kingdom. DOI: 10.1109/ICASSP.2019.8683249
- 52 Manzelli, R., Thakkar, V., Siahkamari, A., & Kulis, B. (2018). Conditioning deep generative raw audio models for structured automatic music. In Proc. of the 19th International Society for Music Information Retrieval Conference, pages 182–189. Paris, France.
- 53 Martin, D. R., Fowlkes, C. C., & Malik, J. (2004). Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5), 530–549. DOI: 10.1109/TPAMI.2004.1273918
- 54 Mauch, M., Cannam, C., Davies, M., Dixon, S., Harte, C., Kolozali, S., Tidhar, D., & Sandler, M. (2009a). OMRAS2 Metadata Project 2009. In Late Breaking/Demo at the 10th International Society for Music Information Retrieval Conference. Kobe, Japan.
- 55 Mauch, M., Noland, K., & Dixon, S. (2009b). Using musical structure to enhance automatic chord transcription. In Proc. of the 10th International Society for Music Information Retrieval Conference, pages 231–236. Kobe, Japan.
- 56 McCallum, M. (2019). Unsupervised learning of deep features for music segmentation. In Proc. of the 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 346–350. Brighton, United Kingdom. DOI: 10.1109/ICASSP.2019.8683407
- 57 McFee, B., & Ellis, D. P. W. (2014a). Analyzing song structure with spectral clustering. In Proc. of the 15th International Society for Music Information Retrieval Conference, pages 405–410. Taipei, Taiwan.
- 58 McFee, B., & Ellis, D. P. W. (2014b). Learning to segment songs with ordinal linear discriminant analysis. In Proc. of the 39th IEEE International Conference on Acoustics, Speech and Signal Processing, pages 5197–5201. Florence, Italy. DOI: 10.1109/ICASSP.2014.6854594
- 59 McFee, B., & Kinnaird, K. (2019). Improving structure evaluation through automatic hierarchy expansion. In Proc. of the 20th International Society for Music Information Retrieval Conference, pages 152–158. Delft, The Netherlands.
-
60
McFee,
B.,
Nieto,
O.,
Farbood, M.
M., &
Bello, J.
P. (2017).
Evaluating hierarchical structure in music
annotations. Frontiers in Psychology,
8:
1337 . DOI: 10.3389/fpsyg.2017.01337 - 61 McFee, B., Raffel, C., Liang, D., Ellis, D. P. W., McVicar, M., Battenberg, E., & Nieto, O. (2015). librosa: Audio and music signal analysis in python. In Proc. of the 14th Python in Science Conference (SciPy), pages 18–25. Austin, TX, USA. DOI: 10.25080/Majora-7b98e3ed-003
- 62 Müller, M., & Jiang, N. (2012). A scape plot representation for visualizing repetitive structures of music recordings. In Proc. of the 13th International Society for Music Information Retrieval Conference, pages 97–102. Porto, Portugal.
- 63 Murthy, Y. V. S., & Koolagudi, S. G. (2018). Contentbased music information retrieval (CB-MIR) and its applications toward the music industry: A review. ACM Computing Surveys, 51(3). DOI: 10.1145/3177849
- 64 Nieto, O. (2015). Discovering Structure in Music: Automatic Approaches and Perceptual Evaluations. PhD thesis, New York University.
- 65 Nieto, O., & Bello, J. P. (2014). Music segment similarity using 2D-Fourier magnitude coefficients. In Proc. of the 39th IEEE International Conference on Acoustics Speech and Signal Processing, pages 664–668. Florence, Italy. DOI: 10.1109/ICASSP.2014.6853679
- 66 Nieto, O., & Bello, J. P. (2016). Systematic exploration of computational music structure research. In Proc. of the 17th International Society for Music Information Retrieval Conference, pages 547–553. New York City, NY, USA.
- 67 Nieto, O., Farbood, M. M., Jehan, T., & Bello, J. P. (2014). Perceptual analysis of the F-measure for evaluating section boundaries in music. In Proc. of the 15th International Society for Music Information Retrieval Conference, pages 265–270. Taipei, Taiwan.
- 68 Nieto, O., Humphrey, E. J., & Bello, J. P. (2012). Compressing music recordings into audio summaries. In Proc. of the 13th International Society for Music Information Retrieval Conference, pages 313–318. Porto, Portugal.
- 69 Nieto, O., & Jehan, T. (2013). Convex non-negative matrix factorization for automatic music structure identification. In Proc. of the 38th IEEE International Conference on Acoustics, Speech, and Signal Processing. DOI: 10.1109/ICASSP.2013.6637644
- 70 Nieto, O., McCallum, M., Davies, M., Robertson, A., Stark, A., & Egozy, E. (2019). The Harmonix Set: Beats, downbeats, and functional segment annotations of Western popular music. In Proc. of the 20th International Society for Music Information Retrieval Conference, pages 565–572. Delft, The Netherlands.
- 71 Panagakis, Y., & Kotropoulos, C. (2012). Music structure analysis by ridge regression of beatsynchronous audio features. In Proc. of the 13th International Society for Music Information Retrieval Conference, pages 271–276. Porto, Portugal.
- 72 Paulus, J., & Klapuri, A. (2009). Music structure analysis using a probabilistic fitness measure and a greedy search algorithm. IEEE Transactions on Audio, Speech, and Language Processing, 17(6), 1159–1170. DOI: 10.1109/TASL.2009.2020533
- 73 Paulus, J., Müller, M., & Klapuri, A. (2010). Audiobased music structure analysis. In Proc. of the 11th International Society for Music Information Retrieval Conference, pages 625–636. Utrecht, Netherlands.
- 74 Pauwels, J., Kaiser, F., & Peeters, G. (2013). Combining harmony-based and novelty-based approaches for structural segmentation. In Proc. of the 14th International Society for Music Information Retrieval Conference. Curitiba, Brazil.
- 75 Peeters, G., & Bisot, V. (2014). Improving music structure segmentation using lag-priors. In Proc. of the 15th International Society for Music Information Retrieval Conference, pages 337–342. Taipei, Taiwan.
-
76
Peeters,
G.,
Burthe, A.
L., & Rodet,
X. (2002).
Toward automatic music audio summary generation from signal
analysis. In Proc. of the 3rd International
Conference on Music Information Retrieval. Paris,
France.
ISMIR . - 77 Peeters, G., & Deruty, E. (2009). Is music structure annotation multi-dimensional? A proposal for robust local music annotation. In Proc. of the 3rd International Worskhop on Learning the Semantics of Audio Signals (LSAS), pages 75–90. Graz, Austria.
- 78 Pons, J., Nieto, O., Prockup, M., Schmidt, E. M., Ehmann, A. F., & Serra, X. (2018). End-to-end learning for music audio tagging at scale. In Proc. of the 19th International Society for Music Information Retrieval Conference, pages 637–644. Paris, France.
- 79 Raffel, C., Mcfee, B., Humphrey, E. J., Salamon, J., Nieto, O., Liang, D., & Ellis, D. P. W. (2014). mir_eval: A transparent implementation of common MIR metrics. In Proc. of the 15th International Society for Music Information Retrieval Conference, pages 367–372. Taipei, Taiwan.
- 80 Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.
-
81
Rafii,
Z.,
Liutkus,
A., &
Pardo,
B. (2014).
REPET for background/foreground separation in audio . In Naik, G. R., & Wang, W., editors, Blind Source Separation, pages 395–411. Springer. DOI: 10.1007/978-3-642-55016-4_14 - 82 Roberts, A., Engel, J., Raffel, C., Hawthorne, C., & Eck, D. (2018). A hierarchical latent vector model for learning long-term structure in music. In Proc. of the 35th International Conference on Machine Learning, volume 80 of Proc. of Machine Learning Research, pages 4364–4373. Stockholm, Sweden.
- 83 Rosenberg, A., & Hirschberg, J. (2007). V-measure: A conditional entropy-based external cluster evaluation measure. In Proc. of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language (EMNLPCoNLL), pages 410–420.
- 84 Sargent, G., Bimbot, F., & Vincent, E. (2011). A regularity-constrained Viterbi algorithm and its application to the structural segmentation of songs. In Proceedings of the International Conference on Music Information Retrieval, pages 483–488. Miami, United States.
- 85 Sargent, G., Bimbot, F., & Vincent, E. (2017). Estimating the structural segmentation of popular music pieces under regularity constraints. IEEE/ACM Transactions on Audio Speech and Language Processing, 25(2), 344–358. DOI: 10.1109/TASLP.2016.2635031
- 86 Schedl, M., Gómez, E., & Urbano, J. (2014). Music information retrieval: Recent developments and applications. Foundations and Trends in Information Retrieval, 8(2–3), 127–261. DOI: 10.1561/1500000042
- 87 Schnitzer, D., Flexer, A., Schedl, M., & Widmer, G. (2011). Using mutual proximity to improve content-based audio similarity. In Proc. of the 12th International Society for Music Information Retrieval Conference, pages 79–84. Miami, FL, USA.
- 88 Seetharaman, P., & Pardo, B. (2016). Simultaneous separation and segmentation in layered music. In Proc. of the 17th International Society for Music Information Retrieval Conference. New York City, NY, USA.
- 89 Serrà, J., Müller, M., Grosche, P., & Arcos, J. L. (2014). Unsupervised music structure annotation by time series structure features and segment similarity. IEEE Transactions on Multimedia, Special Issue on Music Data Mining, 16(5), 1229–1240. DOI: 10.1109/TMM.2014.2310701
- 90 Serrà, J., Serra, X., & Andrzejak, R. G. (2009). Cross recurrence quantification for cover song identification. New Journal of Physics, 11(9), 1138–1151. DOI: 10.1088/1367-2630/11/9/093017
- 91 Shibata, G., Nishikimi, R., Nakamura, E., & Yoshii, K. (2019). Statistical music structure analysis based on a homogeneity-, repetitiveness-, and regularityaware hierarchical hidden semi-Markov model. In Proc. of the 20th International Society for Music Information Retrieval Conference, pages 268–275. Delft, The Netherlands.
- 92 Smith, J. B. L. (2014). Explaining listener differences in the perception of musical structure. PhD thesis, Queen Mary University of London.
- 93 Smith, J. B. L., Burgoyne, J. A., Fujinaga, I., De Roure, D., & Downie, J. S. (2011). Design and creation of a large-scale database of structural annotations. In Proc. of the 12th International Society for Music Information Retrieval Conference, pages 555–560. Miami, FL, USA.
- 94 Smith, J. B. L., & Chew, E. (2013). A meta-analysis of the MIREX structure segmentation task. In Proc. of the 14th International Society for Music Information Retrieval Conference, pages 251–256. Curitiba, Brazil.
- 95 Smith, J. B. L., & Goto, M. (2016). Using priors to improve estimates of music structure. In Proc. of the 17th International Society for Music Information Retrieval Conference, pages 554–560. New York City, NY, USA.
- 96 Smith, J. B. L., & Goto, M. (2017). Multi-part pattern analysis: Combining structure analysis and source separation to discover intra-part repeated sequences. In Proc. of the 18th International Society for Music Information Retrieval Conference, pages 716–723. Suzhou, China.
- 97 Thickstun, J., Harchaoui, Z., Foster, D., & Kakade, S. (2019). Coupled recurrent models for polyphonic music composition. In Proc. of the 20th International Society for Music Information Retrieval Conference, pages 311–318. Delft, The Netherlands.
- 98 Tian, M., & Sandler, M. B. (2016). Towards music structural segmentation across genres. ACM Transactions on Intelligent Systems and Technology, 8(2), 1–19. DOI: 10.1145/2950066
- 99 Tralie, C. J., & McFee, B. (2019). Enhanced hierarchical music structure annotations via feature level similarity fusion. In Proc. of the 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 201–205. Brighton, United Kingdom. DOI: 10.1109/ICASSP.2019.8683492
- 100 Turnbull, D., Lanckriet, G., Pampalk, E., & Goto, M. (2007). A supervised approach for detecting boundaries in music using difference features and boosting. In Proc. of the 8th International Conference on Music Information Retrieval, pages 42–49. Vienna, Austria.
- 101 Ullrich, K., Schlüter, J., & Grill, T. (2014). Boundary detection in music structure analysis using convolutional neural networks. In Proc. of the 15th International Society for Music Information Retrieval Conference, pages 417–422. Taipei, Taiwan.
- 102 van den Oord, A., Dieleman, S., & Schrauwen, B. (2013). Deep content-based music recommendation. Advances in Neural Information Processing Systems 26, pages 2643–2651.
- 103 Wang, C.-I., Mysore, G. J., & Dubnov, S. (2017). Re-visiting the music segmentation problem with crowdsourcing. In Proc. of the 18th International Society for Music Information Retrieval Conference, pages 738–744. Suzhou, China.
- 104 Wang, J.-C., Lee, H.-S., Wang, H.-M., & Jeng, S.-K. (2011). Learning the similarity of audio music in bag-of-frames representation from tagged music data. In Proc. of the 12th International Society for Music Information Retrieval Conference, pages 85–90. Miami, FL, USA.
- 105 Weiss, R., & Bello, J. P. (2011). Unsupervised discovery of temporal structure in music. IEEE Journal of Selected Topics in Signal Processing, 5(6), 1240–1251. DOI: 10.1109/JSTSP.2011.2145356
