Fetching the paper…
Reading the bibliography…
This article describes the Data-Efficient Low-Complexity Acoustic Scene Classification Task in the DCASE 2024 Challenge and the corresponding baseline system.
J. F. Gemmeke, D. P. W. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “Audio set: An ontology and human-labeled dataset for audio events,” in
2017
Earlier work this paper cites.
E. Benetos, D. Stowell, and M. D. Plumbley, “Approaches to complex sound scene analysis,” in
2018
Earlier work this paper cites.
A. Mesaros, T. Heittola, and T. Virtanen, “A multi-device dataset for urban acoustic scene classification,” in
2018
Earlier work this paper cites.
A. Mesaros, T. Heittola, and T. Virtanen, “Acoustic scene classification in DCASE 2019 challenge: Closed and open set classification and data mismatch setups,” in
2019
Earlier work this paper cites.
T. Heittola, A. Mesaros, and T. Virtanen, “Acoustic scene classification in DCASE 2020 challenge: Generalization across devices and low complexity solutions,” in
2020
Earlier work this paper cites.
K. Koutini, F. Henkel, H. Eghbal-zadeh, and G. Widmer, “CP-JKU submissions to DCASE’20: Low-complexity cross-device acoustic scene classification with RF-regularized CNNs,” DCASE Challenge, Tech. Rep., 2020
2020
Earlier work this paper cites.
I. Martín-Morató, T. Heittola, A. Mesaros, and T. Virtanen, “Low-complexity acoustic scene classification for multi-device audio: Analysis of DCASE 2021 challenge systems,” in
2021
Earlier work this paper cites.
C.-H. H. Yang, H. Hu, S. M. Siniscalchi, Q. Wang, W. Yuyang, X. Xia, Y. Zhao, Y. Wu, Y. Wang, J. Du, and C.-H. Lee, “A lottery ticket hypothesis framework for low-complexity device-robust neural acoustic scene classification,” DCASE Challenge, Tech. Rep., 2021
2021
Earlier work this paper cites.
K. Koutini, J. Schlüter, and G. Widmer, “CPJKU submission to DCASE21: Cross-device audio scene classification with wide sparse frequency-damped CNNs,” DCASE Challenge, Tech. Rep., 2021
2021
Earlier work this paper cites.
B. Kim, S. Yang, J. Kim, and S. Chang, “QTI submission to DCASE 2021: Residual normalization for device-imbalanced acoustic scene classification with efficient design,” DCASE Challenge, Tech. Rep., 2021
2021
Earlier work this paper cites.
K. Koutini, H. Eghbal-zadeh, and G. Widmer, “Receptive field regularization techniques for audio classification and tagging with deep convolutional neural networks,”
2021
Earlier work this paper cites.
I. Martín-Morató, F. Paissan, A. Ancilotto, T. Heittola, A. Mesaros, E. Farella, A. Brutti, and T. Virtanen, “Low-complexity acoustic scene classification in DCASE 2022 challenge,” in
2022
Earlier work this paper cites.
F. Schmid, S. Masoudian, K. Koutini, and G. Widmer, “CP-JKU submission to DCASE22: Distilling knowledge for low-complexity convolutional neural networks from a patchout audio transformer,” DCASE Challenge, Tech. Rep., 2022
2022
Earlier work this paper cites.
J.-H. Lee, J.-H. Choi, P. M. Byun, and J.-H. Chang, “Hyu submission for the DCASE 2022: Efficient fine-tuning method using device-aware data-random-drop for device-imbalanced acoustic scene classification,” DCASE Challenge, Tech. Rep., 2022
2022
Earlier work this paper cites.
B. Kim, S. Yang, J. Kim, H. Park, J. Lee, and S. Chang, “Domain generalization with relaxed instance frequency-wise normalization for multi-device acoustic scene classification,” in
2022
Cited alongside, same era.
E. Fonseca, X. Favory, J. Pons, F. Font, and X. Serra, “FSD50K: an open dataset of human-labeled sound events,”
2022
Cited alongside, same era.
K. Koutini, J. Schlüter, H. Eghbal-zadeh, and G. Widmer, “Efficient training of audio transformers with patchout,” in
2022
Cited alongside, same era.
H. Nam, S. Kim, and Y. Park, “Filteraugment: An acoustic environmental data augmentation method,” in
2022
Cited alongside, same era.
J. Tan and Y. Li, “Low-complexity acoustic scene classification using blueprint separable convolution and knowledge distillation,” DCASE Challenge, Tech. Rep., 2023
2023
Cited alongside, same era.
J. Park, T. Kim, D. Rho, J. Kim, and G. Lee, “Kt submission: Periodic activation and knowledge distillation for data-efficient low-complexity acoustic scene classification,” DCASE Challenge, Tech. Rep., 2024
2024
Closest in time.
Y. Cai, P. Zhang, and S. Li, “TF-SepNet: An efficient 1d kernel design in CNNs for low-complexity acoustic scene classification,” in
2024
Closest in time.
Y. Oo and N. Srikanth, “Low complexity acoustic scene classification with moflenet,” DCASE Challenge, Tech. Rep., 2024
2024
Closest in time.
H. Truchan, T. H. Ngo, and Z. Ahmadi, “Ascdomain: Domain invariant device-adversarial isotropic convolutional neural architecture,” DCASE Challenge, Tech. Rep., 2024
2024
Closest in time.
C. Yan, Y. Yu, and X. Xiong, “Submission for DCASE 2024 task1: An asymmetric residual deep neural network for low-complexity acoustic scene classification,” DCASE Challenge, Tech. Rep., 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Cai, M. Lin, C. Zhu, S. Li, and X. Shao, “DCASE2023 task1 submission: Device simulation and time-frequency separable convolution for acoustic scene classification,” DCASE Challenge, Tech. Rep., 2023
2023
Cited alongside, same era.
F. Schmid, T. Morocutti, S. Masoudian, K. Koutini, and G. Widmer, “CP-JKU submission to DCASE23: Efficient acoustic scene classification with cp-mobile,” DCASE Challenge, Tech. Rep., 2023
2023
Cited alongside, same era.
T. Morocutti, F. Schmid, K. Koutini, and G. Widmer, “Device-robust acoustic scene classification via impulse response augmentation,” in
2023
Cited alongside, same era.
F. Schmid, T. Morocutti, S. Masoudian, K. Koutini, and G. Widmer, “Distilling the knowledge of transformers and CNNs with CP-mobile,” in
2023
Cited alongside, same era.
A. Gu and T. Dao, “Mamba: Linear-time sequence modeling with selective state spaces,”
2023
Cited alongside, same era.
S. Chen, Y. Wu, C. Wang, S. Liu, D. Tompkins, Z. Chen, W. Che, X. Yu, and F. Wei, “BEATs: Audio pre-training with acoustic tokenizers,” in
2023
Cited alongside, same era.
H. Bing, H. Wen, C. Zhengyang, J. Anbai, C. Xie, F. Pingyi, L. Cheng, L. Zhiqiang, L. Jia, Z. Wei-Qiang, and Q. Yanmin, “Data-efficient acoustic scene classification via ensemble teachers distillation and pruning,” DCASE Challenge, Tech. Rep., 2024
2024
Cited alongside, same era.
2024
Closest in time.
N. David, R. Aida, and S. Patrick, “Data-efficient acoustic scene classification with pre-trained CP-Mobile,” DCASE Challenge, Tech. Rep., 2024
2024
Closest in time.
W. Chen, Y. Liang, Z. Ma, Z. Zheng, and X. Chen, “EAT: self-supervised pre-training with efficient audio transformer,”
2024
Closest in time.
F. Schmid, K. Koutini, and G. Widmer, “Dynamic convolutional neural networks as efficient pre-trained audio models,”
2024
Closest in time.
J. Bai, M. Wang, E.-L. Tan, J. J. S. Yeo, J. W. Yeow, S. Peksi, D. Shi, W.-S. Gan, and J. Chen, “Hierarchical acoustic scene classification with knowledge distillation and pre-trained dynamic networks,” DCASE Challenge, Tech. Rep., 2024
2024
Closest in time.
A. Werning and R. Haeb-Umbach, “Upb-nt submission to DCASE24: Dataset pruning for targeted knowledge distillation,” DCASE Challenge, Tech. Rep., 2024
2024
Closest in time.
M. Surkov, “Efficient acoustic scene classification using mean-teacher and knowledge distillation,” DCASE Challenge, Tech. Rep., 2024
2024
Closest in time.
G. Chen and Y. Li, “Data-efficient low-complexity acoustic scene classification using parallel attention broad-cast-residual network,” DCASE Challenge, Tech. Rep., 2024
2024
Closest in time.
S. Yeo, E.-L. Tan, J. Bai, S. Peksi, and W.-S. Gan, “Data efficient acoustic scene classification using sing teacher-informed confusing class instruction,” DCASE Challenge, Tech. Rep., 2024
2024
Closest in time.