The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning
Open accessAbstract
Microphones are remarkably powerful sensors of human behavior and context. However, audio sensing is highly susceptible to wild fluctuations in accuracy when used in diverse acoustic environments (such as, bedrooms, vehicles, or cafes), that users encounter on a daily basis. Towards addressing this challenge, we turn to the field of deep learning; an area of machine learning that has radically changed related audio modeling domains like speech recognition. In this paper, we present DeepEar -- the first mobile audio sensing framework built from coupled Deep Neural Networks (DNNs) that simultaneously perform common audio sensing tasks. We train DeepEar with a large-scale dataset including unlabeled data from 168 place visits. The resulting learned model, involving 2.3M parameters, enables DeepEar to significantly increase inference robustness to background noise beyond conventional approaches present in mobile devices. Finally, we show DeepEar is feasible for smartphones by building a cloud-free DSP-based prototype that runs continuously, using only 6% of the smartphone's battery daily.
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Cites
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A fast learning algorithm for deep belief nets
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Cited by
Deep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.010 · Cited by 2,014
“Although some existing work adopted deep HAR on smartphone (Lane et al., 2015) and watch (Bhattacharya and Lane, 2016), they are still far from online and mobile deployment.”
BackgroundMethodInfluentialLearning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing
Li, E., Zeng, L., Zhou, Z., & Chen, X. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2946140 · Cited by 929
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
Yao, S., Hu, S., Zhao, Y., Zhang, A., & Abdelzaher, T. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052577 · Cited by 667
Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables
Hammerla, N. Y., Halloran, S., & Ploetz, T. (2016) · arXiv · doi:10.48550/arxiv.1604.08880 · Cited by 606
Metadata
- Authors
- Lane, N. D., Georgiev, P., & Qendro, L.
- Venue
- Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
- Publisher
- ACM
- Published
- 2015-09-07
- Pages
- 283-294
- OpenAlex
- W1997430507
- Cited by
- 310
- References
- 65
- FWCI
- 33.41
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Lane, N. D., Georgiev, P., & Qendro, L. (2015). DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. https://doi.org/10.1145/2750858.2804262
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