Fetching the paper…
Reading the bibliography…
Data-driven approaches to automatic drum transcription (ADT) are often limited to a predefined, small vocabulary of percussion instrument classes.
D. Fitzgerald, E. Coyle, and B. Lawlor, “Sub-band independent subspace analysis for drum transcription,” in Proc. of the Digital Audio Effects Conference (DAFX) , 2002
2002
Earlier work this paper cites.
O. Gillet and G. Richard, “Automatic transcription of drum loops,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , 2004
2004
Earlier work this paper cites.
G. Tzanetakis, A. Kapur, and R. I. McWalter, “Subband-based drum transcription for audio signals,” in IEEE 7th Workshop on Multimedia Signal Processing , 2005, pp. 1–4
2005
Earlier work this paper cites.
O. Gillet and G. Richard, “Drum track transcription of polyphonic music using noise subspace projection.” in Proc. of the 6th International Society for Music Information Retrieval Conference (ISMIR) , 2005, pp. 92–99
2005
Earlier work this paper cites.
O. Gillet and G. Richard, “Extraction and remixing of drum tracks from polyphonic music signals,” in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) , 2005, pp. 315–318
2005
Earlier work this paper cites.
O. Gillet and G. Richard, “ENST-Drums: an extensive audio-visual database for drum signals processing.” in Proc. of the 7th International Society for Music Information Retrieval Conference (ISMIR) , 2006, pp. 156–159
2006
Earlier work this paper cites.
S. Böck, F. Krebs, and M. Schedl, “Evaluating the online capabilities of onset detection methods.” in Proc. of the 13th International Society for Music Information Retrieval Conference (ISMIR) , 2012, pp. 49–54
2012
Earlier work this paper cites.
H. Lindsay-Smith, S. McDonald, and M. Sandler, “Drumkit transcription via convolutive nmf,” in Proc. of the 15th International Conference on Digital Audio Effects (DAFx) , 2012
2012
Earlier work this paper cites.
C. Dittmar and D. Gärtner, “Real-time transcription and separation of drum recordings based on NMF decomposition,” in Proc. of the International Conference on Digital Audio Effects (DAFx) , 2014, pp. 187–194
2014
Earlier work this paper cites.
R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam, and J. Bello, “MedleyDB: A multitrack dataset for annotation-intensive MIR research,” in Proc. of the 15th International Society for Music Information Retrieval Conference (ISMIR) , 2014
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
C. Wu and A. Lerch, “Drum transcription using partially fixed non-negative matrix factorization,” 23rd European Signal Processing Conference (EUSIPCO) , pp. 1281–1285, 2015
2015
Cited alongside, same era.
G. R. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in ICML Workshop , 2015
2015
Cited alongside, same era.
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra, “Matching networks for one shot learning,” in Advances in Neural Information Processing Systems 29 , 2016, pp. 3630–3638
2016
Cited alongside, same era.
C. Raffel, “Learning-based methods for comparing sequences, with applications to audio-to-midi alignment and matching,” Ph.D. dissertation, 2016
2016
Cited alongside, same era.
S. Böck, F. Korzeniowski, J. Schlüter, F. Krebs, and G. Widmer, “Madmom: A New Python Audio and Music Signal Processing Library,” in Proc. of the 24th ACM International Conference on Multimedia , 2016, p. 1174–1178
M. Cartwright and J. P. Bello, “Increasing drum transcription vocabulary using data synthesis,” in Proc. of the 21st International Conference on Digital Audio Effects (DAFx) , 2018, pp. 57–64
2018
Later among the works it cites.
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. S. Torr, and T. M. Hospedales, “Learning to compare: Relation network for few-shot learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , June 2018, pp. 1199–1208
2018
Later among the works it cites.
S. Chou, K. Cheng, J. R. Jang, and Y. Yang, “Learning to match transient sound events using attentional similarity for few-shot sound recognition,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 26–30
2019
Later among the works it cites.
K. Cheng, S. Chou, and Y. Yang, “Multi-label few-shot learning for sound event recognition,” in IEEE 21st International Workshop on Multimedia Signal Processing (MMSP) , 2019, pp. 1–5
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
——, “Automatic drum transcription using the student-teacher learning paradigm with unlabeled music data,” in Proc. of the 18th International Society for Music Information Retrieval Conference (ISMIR) , 2017
2017
Cited alongside, same era.
R. Vogl, M. Dorfer, and P. Knees, “Drum transcription from polyphonic music with recurrent neural networks,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2017, pp. 201–205
2017
Cited alongside, same era.
C. Southall, R. Stables, and J. Hockman, “Automatic drum transcription for polyphonic recordings using soft attention mechanisms and convolutional neural networks,” in Proc. of the 18th International Society for Music Information Retrieval Conference (ISMIR) , 2017
2017
Cited alongside, same era.
C. Southall, C. Wu, A. Lerch, and J. A. Hockman, “MDB Drums — An Annotated Subset of MedleyDB for Automatic Drum Transcription,” in Late Breaking Demo (Extended Abstract), Proc. of the 18th International Society for Music Information Retrieval Conference (ISMIR) , 2017
2017
Cited alongside, same era.
J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” in Advances in Neural Information Processing Systems 30 , 2017, pp. 4077–4087
2017
Cited alongside, same era.
R. Vogl, G. Widmer, and P. Knees, “Towards multi-instrument drum transcription,” in Proc. of the 21st International Conference on Digital Audio Effects (DAFx) , 2018, pp. 57–64
2018
Cited alongside, same era.
J. Pons, J. Serrà, and X. Serra, “Training Neural Audio Classifiers with Few Data,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 16–20
2019
Later among the works it cites.
E. Manilow, G. Wichern, P. Seetharaman, and J. Le Roux, “Cutting music source separation some slakh: A dataset to study the impact of training data quality and quantity,” in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) , 2019, pp. 45–49
2019
Later among the works it cites.
B. McFee, V. Lostanlen, M. McVicar, A. Metsai, S. Balke, C. Thomé, C. Raffel et al. , “librosa/librosa: 0.7.0,” 2019
2019
Later among the works it cites.
W. Chen, Y. Liu, Z. Kira, Y. F. Wang, and J. Huang, “A closer look at few-shot classification,” in International Conference on Learning Representations , 2019
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen et al. , “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32 , 2019, pp. 8024–8035
2019
Later among the works it cites.
Y. Wang, J. Salamon, N. J. Bryan, and J. P. Bello, “Few-shot sound event detection,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2020, pp. 81–85
2020
Closest in time.
B. Shi, M. Sun, K. C. Puvvada, C. Kao, S. Matsoukas, and C. Wang, “Few-shot acoustic event detection via meta learning,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2020, pp. 76–80
2020
Closest in time.