References
- Bittner, R. M., Fuentes, M., Rubinstein, D., Jansson, A., Choi, K., and Kell, T. (2019). Mirdata: Software for reproducible usage of datasets. A. Flexer, G. Peeters, J. Urbano, and A. Volk (Eds.), In Proceedings of the 20th International Society for Music Information Retrieval Conference, ISMIR 2019 (pp. 99–106). International Society for Music Information Retrieval.
- Bittner, R., Salamon, J., Tierney, M., Mauch, M., Cannam, C., and Bello, J. P. (2014). Medleydb: A multitrack dataset for annotation‑intensive MIR research. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (Vol. 14, pp. 155–160).
- Black, D., Li, M., and Tian, M. (2014). Automatic identification of emotional cues in Chinese opera singing. In Proceedings of the International Conference on Music Perception and Cognition (ICMPC) (pp. 250–255).
- Bogdanov, D., Won, M., Tovstogan, P., Porter, A., and Serra, X. (2019). The MTG‑Jamendo dataset for automatic music tagging. In Proceedings of the International Conference on Machine Learning (ICML).
- Burgoyne, J. A., Wild, J., and Fujinaga, I. (2011). An expert ground truth set for audio chord recognition and music analysis. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), (Vol. 11, pp. 633–638).
- Caro Repetto, R., and Serra, X. (2014). Creating a corpus of jingju (Beijing opera) music and possibilities for melodic analysis. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 313–318).
- de Valk, R., Volk, A., Holzapfel, A., Pikrakis, A., Kroher, N., and Six, J. (2017). Mirchiving: Challenges and opportunities of connecting MIR research and digital music archives. In Proceedings of the International Conference on Digital Libraries for Musicology (DLfM) (pp. 25–28).
- Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Li, F. (2009). ImageNet: A large‑scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 248–255).
- Foster, D., and Dixon, S. (2021). Filosax: A dataset of annotated jazz saxophone recordings. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 205–212).
- Gong, X., Zhu, Y., Zhu, H., and Wei, H. (2021). CHMusic: A traditional Chinese music dataset for evaluation of instrument recognition. In Proceedings of the International Conference on Big Data Technology (ICBDT) (pp. 184–189).
- Goto, M. (2006). AIST annotation for the RWC music database. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 359–360).
- Hainsworth, S. W., and Macleod, M. D. (2004). Particle filtering applied to musical tempo tracking. EURASIP Journal on Advances in Signal Processing, 2004(15), 1–11.
- Hockman, J., Davies, M. E., and Fujinaga, I. (2012). One in the jungle: Downbeat detection in hardcore, jungle, and drum and bass. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 169–174).
- Hsu, C., and Jang, J. R. (2010). On the improvement of singing voice separation for monaural recordings using the MIR‑1K dataset. IEEE Transactions on Speech and Audio Processing, 18(2), 310–319.
- Hu, X., and Yang, Y.‑H. (2017). Cross‑dataset and cross‑cultural music mood prediction: A case on Western and Chinese pop songs. IEEE Transactions on Affective Computing, 8(2), 228–240.
- Huang, C.‑Z. A., Hawthorne, C., Roberts, A., Dinculescu, M., Wexler, J., Hong, L., and Howcroft, J. (2019). The Bach Doodle: Approachable music composition with machine learning at scale. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR).
- Huang, Y.‑F., Liang, J.‑I., Wei, I.‑C., and Su, L. (2020). Joint analysis of mode and playing technique in guqin performance with machine learning. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 85–92).
- Hung, H., Ching, J., Doh, S., Kim, N., Nam, J., and Yang, Y. (2021). EMOPIA: A multi‑modal pop piano dataset for emotion recognition and emotion‑based music generation. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 318–325).
- Kong, Q., Li, B., Chen, J., and Wang, Y. (2020). GiantMIDI‑Piano: A large‑scale MIDI dataset for classical piano music. Transactions of the International Society for Music Information Retrieval, 5, 87–98.
- Levy, M. (2011). Improving perceptual tempo estimation with crowd‑sourced annotations. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 317–322).
- Li, D., Che, M., Meng, W., Wu, Y., Yu, Y., Xia, F., and Li, W. (2023). Frame‑level multi‑label playing technique detection using multi‑scale network and self‑attention mechanism. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 1–5).
- Li, D., Wu, Y., Li, Q., Zhao, J., Yu, Y., Xia, F., and Li, W. (2022). Playing technique detection by fusing note onset information in guzheng performance. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 314–320).
- Li, R., and Zhang, Q. (2022). Audio recognition of Chinese traditional instruments based on machine learning. Cognitive Computation and Systems, 4(2), 108–115.
- Liang, X., Li, Z., Liu, J., Li, W., Zhu, J., and Han, B. (2019). Constructing a multimedia Chinese musical instrument database. In Proceedings of the Conference on Sound and Music Technology (CSMT) (pp. 53–60).
- McFee, B., Raffel, C., Liang, D., Ellis, D. P., McVicar, M., Battenberg, E., and Nieto, O. (2015). librosa: Audio and music signal analysis in Python. In Proceedings of the Python in Science Conference (SciPy), 8 (Vol. 8, pp. 18–25).
- Müller, M. (2007). Information retrieval for music and motion. Springer.
- Nahar, F., Agres, K. B., and Herremans, D. (2020). A dataset and classification model for Malay, Hindi, Tamil, and Chinese music. ArXiv preprint, abs/2009.04459
- Nieto, O., McCallum, M. C., Davies, M. E. P., Robertson, A., Stark, A. M., and Egozy, E. (2019). The harmonix set: Beats, downbeats, and functional segment annotations of Western popular music. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 565–572).
- Palanisamy, K., Singhania, D., and Yao, A. (2020). Rethinking CNN models for audio classification. ArXiv preprint, abs/2007.11154
- Pegoraro Santana, I. A., Pinhelli, F., Donini, J., Catharin, L., Mangolin, R. B., da Costa, Y. M. E., Feltrim, V. D., and Domingues, M. A. (2020). Music4all: A new music database and its applications. In Proceedings of the International Conference on Systems, Signals and Image Processing (IWSSIP) (pp. 399–404).
- Ren, W., Che, M., Wang, Z., Meng, W., Li, Q., Hu, J., Xia, F., and Li, W. (2022). CNPM database: A Chinese national pentatonic modulation database for computational musicology. Journal of Fudan University (Natural Science), 61(5), 9.
- Shen, J., Wang, R., and Shen, H.‑W. (2020). Visual exploration of latent space for traditional Chinese music. Visual Informatics, 4(2), 99–108.
- Thickstun, J., Harchaoui, Z., and Kakade, S. M. (2017). Learning features of music from scratch. In Proceedings of the International Conference on Learning Representations (ICLR).
- Tsalera, E., Papadakis, A. E., and Samarakou, M. (2021). Comparison of pre‑trained CNNs for audio classification using transfer learning. Journal of Sensor and Actuator Networks, 10, 72.
- Wang, C., Benetos, E., Lostanlen, V., and Chew, E. (2022a). Adaptive scattering transforms for playing technique recognition. IEEE Transactions on Speech and Audio Processing, 30, 1407–1421.
- Wang, J., and Jang, J. R. (2021). On the preparation and validation of a large‑scale dataset of singing transcription. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 276–280).
- Wang, Z., Che, M., Yang, Y., Meng, W., Li, Q., Xia, F., and Li, W. (2022b). Automatic Chinese national pentatonic modes recognition using convolutional neural networks. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 345–352).
- Wang, Z., Chen, K., Jiang, J., Zhang, Y., Xu, M., Dai, S., and Xia, G. (2020). POP909: A pop‑song dataset for music arrangement generation. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 38–45).
- Wang, Z., Li, J., Chen, X., Li, Z., Zhang, S., Han, B., and Yang, D. (2019). Musical instrument playing technique detection based on FCN: Using Chinese bowed‑stringed instruments as an example. ArXiv preprint, abs/1910.09021
- Zhang, K., Wu, X., Tang, R., Huang, Q., Yang, C., and Zhang, H. (2021). The Jinyue database for huqin music emotion, scene, and imagery recognition. In Proceedings of the IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 314–319).
- Zhang, Y., Zhou, Z., Li, X., Yu, F., and Sun, M. (2023). CCom‑Huqin: An annotated multimodal Chinese fiddle performance dataset. Transactions of the International Society for Music Information Retrieval, 6(1), 60–74.