The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
CNN architectures for large-scale audio classification
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“RECAP [323] and [43] leverages dense retrievers, CLAP [26] and VGGish [69] respectively, to retrieve related captions given audio data.”
From Retrieval-Augmented Generation for AI-Generated Content: A Survey (2024) · cites this paper as [69]
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Cites
Deep Residual Learning for Image Recognition
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ImageNet classification with deep convolutional neural networks
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Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Audio Set: An ontology and human-labeled dataset for audio events
Gemmeke, J. F., Ellis, D. P. W., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., & Ritter, M. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952261 · Cited by 3,134
Self-citationActivityNet: A large-scale video benchmark for human activity understanding
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Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks
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Cited by
Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D., Zoph, B., Shlens, J., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00359 · Cited by 3,223
Audio Set: An ontology and human-labeled dataset for audio events
Gemmeke, J. F., Ellis, D. P. W., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., & Ritter, M. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952261 · Cited by 3,134
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VoxCeleb: A Large-Scale Speaker Identification Dataset
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PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
Kong, Q., Cao, Y., Iqbal, T., Wang, Y., Wang, W., & Plumbley, M. D. (2020) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2020.3030497 · Cited by 1,235
Metadata
- Authors
- Hershey, S., Chaudhuri, S., Ellis, D. P. W., Gemmeke, J. F., Jansen, A., Moore, R. C., Plakal, M., Platt, D., Saurous, R. A., Seybold, B., Slaney, M., Weiss, R. J., & Wilson, K.
- Venue
- 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 131-135
- Cited by
- 2,023
- References
- 10
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- CoChat. (2026). Citation check: CNN architectures for large-scale audio classification. https://app.cochat.ai/papers/10.1109/icassp.2017.7952132
- The paper
- Hershey, S., Chaudhuri, S., Ellis, D. P. W., Gemmeke, J. F., Jansen, A., Moore, R. C., Plakal, M., Platt, D., Saurous, R. A., Seybold, B., Slaney, M., Weiss, R. J., & Wilson, K. (2017). CNN architectures for large-scale audio classification. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2017.7952132
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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.
