Spectral images based environmental sound classification using CNN with meaningful data augmentation

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works

A survey on Image Data Augmentation for Deep Learning

Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203 · see citing works

Untitled work

(2010) · Cited by 8,261 · see citing works

librosa: Audio and Music Signal Analysis in Python

McFee, B., Raffel, C., Liang, D., Ellis, D. P. W., McVicar, M., Battenberg, E., & Nieto, O. (2015) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-7b98e3ed-003 · Cited by 3,099 · see citing works

Cyclical Learning Rates for Training Neural Networks

Smith, L. N. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.58 · Cited by 2,931 · see citing works

CNN architectures for large-scale audio classification

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., et al. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952132 · Cited by 2,605 · see citing works

An Overview of Overfitting and its Solutions

Ying, X. (2019) · Journal of Physics: Conference Series · doi:10.1088/1742-6596/1168/2/022022 · Cited by 2,352 · see citing works

ESC: Dataset for Environmental Sound Classification

Piczak, K. J. (2015) · ACM Multimedia · doi:10.1145/2733373.2806390 · Cited by 2,195 · see citing works

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

(2011) · doi:10.1109/embc13666.2011 · Cited by 1,699 · see citing works

Proceedings of the International Joint Conference on Neural Networks, 2003.

(2003) · doi:10.1109/ijcnn.2003 · Cited by 1,689 · see citing works

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

Salamon, J., & Bello, J. P. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2657381 · Cited by 1,602 · see citing works

A Dataset and Taxonomy for Urban Sound Research

Salamon, J., Jacoby, C., & Bello, J. P. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2655045 · Cited by 1,355 · see citing works

Environmental sound classification with convolutional neural networks

Piczak, K. J. (2015) · Machine learning for signal processing ... · doi:10.1109/mlsp.2015.7324337 · Cited by 971 · see citing works

Transfusion: Understanding Transfer Learning for Medical ImagingPreprint

Raghu, M., Zhang, C., Kleinberg, J. M., & Samy Bengio, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07208 · Cited by 845 · see citing works

SoundNet: Learning Sound Representations from Unlabeled VideoPreprint

Aytar, Y., Vondrick, C., & Torralba, A. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09001 · Cited by 658 · see citing works

Objects that Sound

Arandjelović, R., & Zisserman, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_27 · Cited by 448 · see citing works

Collaborative Security

Meng, G., Liu, Y., Zhang, J., Pokluda, A., & Boutaba, R. (2015) · ACM Computing Surveys · doi:10.1145/2785733 · Cited by 427 · see citing works

End-to-end environmental sound classification using a 1D convolutional neural network

Abdoli, S., Cardinal, P., & Koerich, A. L. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.06.040 · Cited by 359 · see citing works

Classifying environmental sounds using image recognition networks

Boddapati, V., Petef, A., Rasmusson, J., & Lundberg, L. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.250 · Cited by 292 · see citing works

Gammatone Cepstral Coefficients: Biologically Inspired Features for Non-Speech Audio Classification

Valero, X., & Álías, F. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2199972 · Cited by 283 · see citing works

2016 24th European Signal Processing Conference (EUSIPCO)

(2016) · doi:10.1109/eusipco37884.2016 · Cited by 251 · see citing works

Audio Surveillance

Crocco, M., Cristani, M., Trucco, A., & Murino, V. (2016) · ACM Computing Surveys · doi:10.1145/2871183 · Cited by 247 · see citing works

Sound Classification Using Convolutional Neural Network and Tensor Deep Stacking Network

Khamparia, A., Gupta, D. K., Nguyen, G. N., Khanna, A. K., Pandey, B., & Tiwari, P. (2019) · IEEE Access · doi:10.1109/access.2018.2888882 · Cited by 238 · see citing works

Learning from Between-class Examples for Deep Sound RecognitionPreprint

Tokozume, Y., Ushiku, Y., & Harada, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10282 · Cited by 206 · see citing works

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Huang, Y., Palomar, D. P., & Zhang, S. (2012) · Cited by 205 · see citing works

Environmental sound recognition: A survey

Chachada, S., & Kuo, C.-C. J. (2013) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference · doi:10.1017/atsip.2014.12 · Cited by 200 · see citing works

Environment Sound Classification Using a Two-Stream CNN Based on Decision-Level Fusion

Su, Y., Zhang, K., Wang, J., et al. (2019) · Sensors · doi:10.3390/s19071733 · Cited by 197 · see citing works

Environmental sound classification using a regularized deep convolutional neural network with data augmentation

Mushtaq, Z., & Su, S. (2020) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107389 · Cited by 180 · see citing works

Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming

George, A. P., & Powell, W. B. (2006) · Machine Learning · doi:10.1007/s10994-006-8365-9 · Cited by 174 · see citing works

A comparison of Deep Learning methods for environmental sound detection

Li, J., Dai, W., Metze, F., et al. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952131 · Cited by 156 · see citing works

Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural NetworksPreprint

Huzaifah, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.07156 · Cited by 148 · see citing works

International Conference on Control, Automation and Systems

Xuan, D.-J., Kim, J.-W., Zang, J.-I., et al. (2007) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2007.4406494 · Cited by 144 · see citing works

Spectral vs. spectro-temporal features for acoustic event detection

Cotton, C. V., & Ellis, D. P. W. (2011) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/aspaa.2011.6082331 · Cited by 138 · see citing works

Transfer learning for music classification and regression tasksPreprint

Choi, K., Fazekas, G., Sandler, M. B., & Cho, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.09179 · Cited by 137 · see citing works

A Study on Feature Analysis for Musical Instrument Classification

Deng, J. D., Simmermacher, C., & Cranefield, S. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2007.913394 · Cited by 133 · see citing works

A new pyramidal concatenated CNN approach for environmental sound classification

Demir, F., Türkoğlu, M., Aslan, M., et al. (2020) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107520 · Cited by 130 · see citing works

An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition

Li, S., Yao, Y. F., Hu, J., Liu, G., Yao, X., & Hu, J. (2018) · Applied Sciences · doi:10.3390/app8071152 · Cited by 118 · see citing works

Speaker recognition with hybrid features from a deep belief network

Ali, H., Tran, S. N., Benetos, E., et al. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2501-7 · Cited by 92 · see citing works

Novel TEO-based Gammatone features for environmental sound classification

Agrawal, D. M., Sailor, H. B., Soni, M. H., & Patil, H. A. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081521 · Cited by 63 · see citing works

Deep neural networks for automatic detection of screams and shouted speech in subway trains

Laffitte, P., Sodoyer, D., Tatkeu, C., et al. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472921 · Cited by 62 · see citing works

Audio recognition in the wild: Static and dynamic classification on a real-world database of animal vocalizations

Weninger, F. J., & Schuller, B. W. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946409 · Cited by 61 · see citing works

Improving event detection for audio surveillance using Gabor filterbank features

Geiger, J. T., & Helwani, K. (2015) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2015.7362476 · Cited by 61 · see citing works

Comparing time and frequency domain for audio event recognition using deep learning

Hertel, L., Phan, H., & Mertins, A. (2016) · doi:10.1109/ijcnn.2016.7727635 · Cited by 59 · see citing works

Sound Detection and Classification for Medical Telesurvey

Vacher, M., Istrate, D., Besacier, L., Sérignat, J.-F., & Castelli, E. (2004) · HAL · Cited by 49 · see citing works

2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)

(2017) · doi:10.1109/mmsp39884.2017 · Cited by 43 · see citing works

IoT-based Urban Noise Identification Using Machine Learning

Alsouda, Y., Pllana, S., & Kurti, A. (2019) · doi:10.1145/3312614.3312631 · Cited by 41 · see citing works

2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

(2007) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2007.897018 · Cited by 34 · see citing works

Generative Model Driven Representation Learning in a Hybrid Framework for Environmental Audio Scene and Sound Event Recognition

Chandrakala, S., & Jayalakshmi, S. L. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2925956 · Cited by 31 · see citing works

Learning Representations for Nonspeech Audio Events Through Their Similarities to Speech Patterns

Phan, H., Hertel, L., Maaß, M., Mazur, R., & Mertins, A. (2016) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2016.2530401 · Cited by 30 · see citing works

A study on transfer learning for acoustic event detection in a real life scenario

Arora, P., & Haeb‐Umbach, R. (2017) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2017.8122258 · Cited by 24 · see citing works

The bag-of-frames approach: A not so sufficient model for urban soundscapes

Lagrange, M., Lafay, G., Défréville, B., et al. (2015) · The Journal of the Acoustical Society of America · doi:10.1121/1.4935350 · Cited by 22 · see citing works

Crime warning system using image and sound processing

Intani, P., & Orachon, T. (2013) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2013.6704220 · Cited by 17 · see citing works

Environment Sound Classification using Multiple Feature Channels and Deep Convolutional Neural NetworksPreprint

Sharma, J., Granmo, O., & Goodwin, M. (2019) · arXiv (Cornell University) · Cited by 10 · see citing works

Environmental sound classification based on feature fusion

Zhao, H., Huang, X., Liu, W., et al. (2018) · MATEC Web of Conferences · doi:10.1051/matecconf/201817303059 · Cited by 7 · see citing works

The effect of room acoustics on audio event classification

Emmanouilidou, D., & Gamper, H. (2019) · RWTH Publications (RWTH Aachen) · doi:10.18154/rwth-conv-239986 · Cited by 7 · see citing works

Learning Environmental Sounds with Multi-scale Convolutional Neural NetworkPreprint

Zhu, B., Wang, C., Liu, F., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10219 · Cited by 6 · see citing works

Deep Convolutional Neural Network with Mixup for Environmental Sound ClassificationPreprint

Zhang, Z., Xu, S., Cao, S. C., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.08405 · Cited by 5 · see citing works

Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation

Zsebők, S. (2026) · Biologia Futura · doi:10.1007/s42977-026-00322-5

CNN-based Transfer Learning for Covid-19 Diagnosis

Ahmed, Z. S., Surameery, N. M. S., Rashid, R. D., Salih, S. Q., & Abdulla, H. K. (2021) · doi:10.1109/icit52682.2021.9491126 · Cited by 3 · see citing works

Hand-Gun Detection in Images with Transfer Learning-Based Convolutional Neural Networks

Veranyurt, O., & Sakar, C. O. (2020) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu49456.2020.9302394 · Cited by 7 · see citing works

Drone Image Based Illegal Crowd Detection For Covid-19 Disease Prevention Via Convolutional Neural Networks (CNNs) Transfer Learning

Ong, J. E., & As’ari, M. A. (2022) · Journal of Human Centered Technology · doi:10.11113/humentech.v1n2.20 · Cited by 1 · see citing works

Small aircraft detection using deep learning

Kıyak, E., & Unal, G. (2021) · Aircraft Engineering and Aerospace Technology · doi:10.1108/aeat-11-2020-0259 · Cited by 19 · see citing works

Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs

Lee, K.-S., Jung, S., Ryu, J., Shin, S., & Choi, J. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9020392 · Cited by 191 · see citing works