References
- Albrecht, H., and Frieler, K. (2014). The perception and recognition of Wagnerian leitmotifs in multimodal conditions. In Proceedings of the International Conference of Students of Systematic Musicology (SysMus), London, UK.
- Baker, D. J., and Müllensiefen, D. (2017). Perception of leitmotives in Richard Wagner’s Der Ring des Nibelungen. Frontiers in Psychology, 8: 662. DOI: 10.3389/fpsyg.2017.00662
- Böck, S., Krebs, F., and Widmer, G. (2016). Joint beat and downbeat tracking with recurrent neural networks. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 255–261, New York City, USA.
- Bribitzer-Stull, M. (2015). Understanding the Leitmotif. Cambridge University Press. DOI: 10.1017/CBO9781316161678
- Çakir, E., Parascandolo, G., Heittola, T., Huttunen, H., and Virtanen, T. (2017). Convolutional recurrent neural networks for polyphonic sound event detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(6): 1291–1303. DOI: 10.1109/TASLP.2017.2690575
- Choi, K., Fazekas, G., Sandler, M. B., and Cho, K. (2017). Transfer learning for music classification and regression tasks. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR), pages 141–149, Suzhou, China.
- Di Giorgi, B., Mauch, M., and Levy, M. (2020). Downbeat tracking with tempo-invariant convolutional neural networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR), pages 216–222, Montréal, Canada.
- Dreyfus, L., and Rindfleisch, C. (2014). Using digital libraries in the research of the reception and interpretation of Richard Wagner’s leitmotifs. In Proceedings of the International Workshop on Digital Libraries for Musicology, pages 1–3, London, UK. DOI: 10.1145/2660168.2660181
- Elowsson, A., and Friberg, A. (2019). Modeling music modality with a key-class invariant pitch chroma CNN. In Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR), pages 541–548, Delft, The Netherlands.
- Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press, Cambridge and London.
http://www.deeplearningbook.org . - Kornstädt, A. (2001). The JRing system for computerassisted musicological analysis. In Proceedings of the International Symposium on Music Information Retrieval (ISMIR), pages 93–98, Bloomington, Indiana, USA.
- Krause, M., Zalkow, F., Zalkow, J., Weiß, C., and Müller, M. (2020). Classifying leitmotifs in recordings of operas by Richard Wagner. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 473–480, Montréal, Canada.
- Lattner, S., Dörfler, M., and Arzt, A. (2019). Learning complex basis functions for invariant representations of audio. In Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR), pages 700–707, Delft, The Netherlands.
- Li, Y., Liu, M., Drossos, K., and Virtanen, T. (2020). Sound event detection via dilated convolutional recurrent neural networks. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 286–290. DOI: 10.1109/ICASSP40776.2020.9054433
- Mesaros, A., Heittola, T., and Virtanen, T. (2016). Metrics for polyphonic sound event detection. Applied Sciences, 6(6): 162. DOI: 10.3390/app6060162
- Morimoto, Y., Kamekawa, T., and Marui, A. (2009). Verbal effect on memorisation and recognition of Wagner’s leitmotifs. In Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM).
- Page, K. R., Nurmikko-Fuller, T., Rindfleisch, C., Weigl, D. M., Lewis, R., Dreyfus, L., and De Roure, D. (2015). A toolkit for live annotation of opera performance: Experiences capturing Wagner’s Ring cycle. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 211–217, Málaga, Spain.
- Schlüter, J., and Lehner, B. (2018). Zero-mean convolutions for level-invariant singing voice detection. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), pages 321–326, Paris, France.
- Schreiber, H., Weiß, C., and Müller, M. (2020). Local key estimation in classical music recordings: A cross-version study on Schubert’s Winterreise. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 501–505, Barcelona, Spain. DOI: 10.1109/ICASSP40776.2020.9054642
- Stowell, D., Giannoulis, D., Benetos, E., Lagrange, M., and Plumbley, M. D. (2015). Detection and classification of acoustic scenes and events. IEEE Transactions on Multimedia, 17(10): 1733–1746. DOI: 10.1109/TMM.2015.2428998
- Virtanen, T., Plumbley, M. D., and Ellis, D. (2018). Computational Analysis of Sound Scenes and Events. Springer. DOI: 10.1007/978-3-319-63450-0
- Wagner, R. (1995). Opera and Drama. University of Nebraska Press. Translation of the original edition from 1851.
- Wagner, R. (2013). Der Ring des Nibelungen. Vollständiger Text mit Notentafeln der Leitmotive. Schott Music, Mainz. Reprint of the original edition from 1913 (Ed. Julius Burghold).
- Weiß, C., Arifi-Müller, V., Prätzlich, T., Kleinertz, R., and Müller, M. (2016). Analyzing measure annotations for Western classical music recordings. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 517–523, New York, USA.
- Wu, C.-W., Dittmar, C., Southall, C., Vogl, R., Widmer, G., Hockman, J., Müller, M., and Lerch, A. (2018). A review of automatic drum transcription. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(9): 1457–1483. DOI: 10.1109/TASLP.2018.2830113
- Xia, X., Togneri, R., Sohel, F., Zhao, Y., and Huang, D. (2019). A survey: Neural network-based deep learning for acoustic event detection. Circuits, Systems, and Signal Processing, 38(8): 3433–3453. DOI: 10.1007/s00034-019-01094-1
- Zalkow, F., Weiß, C., and Müller, M. (2017a). Exploring tonal-dramatic relationships in Richard Wagner’s Ring cycle. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 642–648, Suzhou, China.
- Zalkow, F., Weiß, C., Prätzlich, T., Arifi-Müller, V., and Müller, M. (2017b). A multi-version approach for transferring measure annotations between music recordings. In Proceedings of the AES International Conference on Semantic Audio, pages 148–155, Erlangen, Germany.
