The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Convolutional recurrent neural networks for music classification
Open accessAbstract
We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurrent neural networks for temporal summarisation of the extracted features. We compare CRNN with three CNN structures that have been used for music tagging while controlling the number of parameters with respect to their performance and training time per sample. Overall, we found that CRNNs show a strong performance with respect to the number of parameter and training time, indicating the effectiveness of its hybrid structure in music feature extraction and feature summarisation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
(2014) · Cited by 33,419
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Cho, K., van Merrienboer, B., Bahdanau, D., & Bengio, Y. (2014) · Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation · doi:10.3115/v1/w14-4012 · Cited by 7,099
Self-citationFast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513
librosa: Audio and Music Signal Analysis in Python
McFee, B., Raffel, C., Liang, D., Ellis, D., McVicar, M., Battenberg, E., & Nieto, O. (2015) · Proceedings of the Python in Science Conference · doi:10.25080/majora-7b98e3ed-003 · Cited by 3,033
Convergence Results for Neural Networks via Electrodynamics
Panigrahy, R., Rahimi, A., Sachdeva, S., & Zhang, Q. (2018) · Leibniz international proceedings in informatics · doi:10.4230/lipics.itcs.2018.22 · Cited by 2,761
Static Analysis of Shape in TensorFlow Programs
The Theano Development Team, Al‐Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.ecoop.2020.15 · Cited by 1,589
Self-citationDocument Modeling with Gated Recurrent Neural Network for Sentiment Classification
Tang, D., Qin, B., & Liu, T. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1167 · Cited by 1,519
Deep content-based music recommendation
van den Oord, A., Dieleman, S., & Schrauwen, B. (2013) · Ghent University Academic Bibliography (Ghent University) · Cited by 962
Cited by
Spatial–Temporal Recurrent Neural Network for Emotion Recognition
Zhang, T., Zheng, W., Cui, Z., Zong, Y., & Li, Y. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2788081 · Cited by 595
Flood susceptibility mapping using convolutional neural network frameworks
Wang, Y., Fang, Z., Hong, H., & Peng, L. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.124482 · Cited by 339
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
Yang, L.-C., Chou, S.-Y., & Yang, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10847 · Cited by 283
Muffin: testing deep learning libraries via neural architecture fuzzing
Gu, J., Luo, X., Zhou, Y., & Wang, X. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510092 · Cited by 71
A systematic deep learning model selection for p300-based brain-computer interfaces
Abibullaev, B., & Zollanvari, A. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3051136 · Cited by 53
Metadata
- Authors
- Choi, K., Fazekas, G., Sandler, M., & Cho, K.
- Venue
- 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 2392-2396
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2963451564
- Cited by
- 517
- References
- 21
- FWCI
- 39.56
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Convolutional recurrent neural networks for music classification. https://app.cochat.ai/papers/10.1109/icassp.2017.7952585
- The paper
- Choi, K., Fazekas, G., Sandler, M., & Cho, K. (2017). Convolutional recurrent neural networks for music classification. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2017.7952585
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
