CBFdataset: A Dataset of Chinese Bamboo Flute Performances

CBFdataset is a dataset of Chinese bamboo flute (CBF) performances, created for ecologically valid analysis of music playing techniques in context.

The dataset comprises monophonic recordings of classic CBF pieces and isolated playing techniques, recorded by 10 professional CBF performers; and expert annotations of seven playing techniques: vibrato, tremolo, trill, flutter-tongue (FT), acciaccatura, portamento, and glissando. The recorded pieces include Busy Delivering Harvest (BH) 扬鞭催马运粮忙, Jolly Meeting (JM) 喜相逢, Morning (Mo) 早晨, and Flying Partridge (FP) 鹧鸪飞. All data was recorded in a professional recording studio using a Zoom H6 recorder at 44.1kHz/24-bits. The difference between different Versions 1.2, 1.1, and 1.0:

• V1.2 is the complete CBFdataset [1, 2] with a total duration of 2.6 hours.
• V1.1 splits the CBFdataset into two subsets according to playing technique types: CBF-periDB [3] and CBF-petsDB [4]. The former contains all the full-length pieces, isolated playing techniques, and annotations of four periodic modulations: vibrato, tremolo, trill, and flutter-tongue. The latter comprises the same full-length recordings, isolated playing techniques, and annotations of three pitch evolution-based techniques: acciaccatura, portamento, and glissando.
• V1.0 includes only the CBF-periDB [3].

Related updates, demos, and code for reproducibility are available at http://c4dm.eecs.qmul.ac.uk/CBFdataset.html. Any queries, please feel free to contact Changhong at changhong.wang@ls2n.fr. Please cite the corresponding papers:

[1] C. Wang, E. Benetos, V. Lostanlen, and E. Chew, "Adaptive Scattering Transforms for Playing Technique Recognition," submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP), 2021.

[2] C. Wang, Scattering Transform for Playing Technique Recognition, PhD thesis, Queen Mary University of London, UK, 2021.

[3] C. Wang, E. Benetos, V. Lostanlen, and E. Chew, "Adaptive Time–Frequency Scattering for Periodic Modulation Recognition in Music Signals," In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 809–815, 2019.

[4] C. Wang, V. Lostanlen, E. Benetos, and E. Chew, "Playing Technique Recognition by Joint Time–Frequency Scattering". In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 881–885, 2020.