Low-resource multi-task audio sensing for mobile and embedded devices via shared deep neural network representations
Open accessAbstract
Continuous audio analysis from embedded and mobile devices is an increasingly important application domain. More and more, appliances like the Amazon Echo, along with smartphones and watches, and even research prototypes seek to perform multiple discriminative tasks simultaneously from ambient audio; for example, monitoring background sound classes (e.g., music or conversation), recognizing certain keywords (‘Hey Siri' or ‘Alexa'), or identifying the user and her emotion from speech. The use of deep learning algorithms typically provides state-of-the-art model performances for such general audio tasks. However, the large computational demands of deep learning models are at odds with the limited processing, energy and memory resources of mobile, embedded and IoT devices. In this paper, we propose and evaluate a novel deep learning modeling and optimization framework that specifically targets this category of embedded audio sensing tasks. Although the supported tasks are simpler than the task of speech recognition, this framework aims at maintaining accuracies in predictions while minimizing the overall processor resource footprint. The proposed model is grounded in multi-task learning principles to train shared deep layers and exploits, as input layer, only statistical summaries of audio filter banks to further lower computations. We find that for embedded audio sensing tasks our framework is able to maintain similar accuracies, which are observed in comparable deep architectures that use single-task learning and typically more complex input layers. Most importantly, on an average, this approach provides almost a 2.1× reduction in runtime, energy, and memory for four separate audio sensing tasks, assuming a variety of task combinations.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
There is an exploding number of proposals for XAI, including post-hoc explanation methods and interpretable-by-design ML approaches.
“In the last years, we are observing an exploding number of proposals for eXplainable Artificial Intelligence (XAI), either post-hoc explanation methods but also interpretable-by-design Machine Learning (ML) approaches [8, 3, 1, 4, 2].”
From Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence (2023) · cites this paper as [1]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,865 · see citing works
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636 · see citing works
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542 · see citing works
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · see citing works
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058 · see citing works
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works
Perceptual linear predictive (PLP) analysis of speech
Heřmanský, H. (1990) · The Journal of the Acoustical Society of America · doi:10.1121/1.399423 · Cited by 2,550 · see citing works
Theano: A Python framework for fast computation of mathematical expressionsPreprint
Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., Belopolsky, A., et al. (2016) · arXiv.org · doi:10.48550/arxiv.1605.02688 · Cited by 2,438 · see citing works
Static Analysis of Shape in TensorFlow ProgramsPreprint
The Theano Development Team, Al‐Rfou, R., Alain, G., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.ecoop.2020.15 · Cited by 1,629 · see citing works
Learning both Weights and Connections for Efficient Neural Networks
Han, S., Pool, J., Tran, J., & Dally, W. J. (2015) · arXiv · doi:10.48550/arxiv.1506.02626 · Cited by 1,481 · see citing works
Deng, L., Hinton, G. E., & Kingsbury, B. E. D. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639344 · Cited by 1,222 · see citing works
Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2017)
Amendola, G., Ricca, F., & Truszczyński, M. (2017) · International Joint Conference on Artificial Intelligence · Cited by 1,162 · see citing works
Deep neural networks for small footprint text-dependent speaker verification
Variani, E., Lei, X., McDermott, E., Moreno, I. L., & Gónzalez-Domínguez, J. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854363 · Cited by 1,073 · see citing works
Compressing Deep Convolutional Networks using Vector QuantizationPreprint
Gong, Y., Liu, L., Yang, M., & Bourdev, L. D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6115 · Cited by 1,047 · see citing works
Compressing Neural Networks with the Hashing Trick
Chen, W., Wilson, J. T., Tyree, S., Weinberger, K. Q., & Chen, Y. (2015) · arXiv · doi:10.48550/arxiv.1504.04788 · Cited by 974 · see citing works
(2009) · neural information processing systems · Cited by 962 · see citing works
Speech emotion recognition using deep neural network and extreme learning machine
Han, K., Yu, D., & Tashev, I. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-57 · Cited by 824 · see citing works
Recent advances in deep learning for speech research at Microsoft
Deng, L., Li, J., Huang, J.-T., Yao, K., Yu, D., Seide, F., Seltzer, M., Zweig, G., He, X., Williams, J., et al. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639345 · Cited by 801 · see citing works
Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers
Huang, J.-T., Li, J., Yu, D., Deng, L., & Gong, Y. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639081 · Cited by 612 · see citing works
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 · see citing works
Comparison of different implementations of MFCC
Zheng, F., Zhang, G., & Song, Z. (2001) · Journal of Computer Science and Technology · doi:10.1007/bf02943243 · Cited by 594 · see citing works
Lu, H., Pan, W., Lane, N. D., Choudhury, T., & Campbell, A. T. (2009) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/1555816.1555834 · Cited by 581 · see citing works
The Jigsaw continuous sensing engine for mobile phone applications
Lu, H., Yang, J., Liu, Z., Lane, N. D., Choudhury, T., & Campbell, A. T. (2010) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/1869983.1869992 · Cited by 561 · see citing works
Small-footprint keyword spotting using deep neural networks
Chen, G., Parada, C., & Heigold, G. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854370 · Cited by 555 · see citing works
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
Li, X., Zhao, L.-M., Wei, L., Yang, M., Wu, F. Q., Zhuang, Y., Ling, H., & Wang, J. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2579306 · Cited by 535 · see citing works
Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., Gática-Pérez, D., & Choudhury, T. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370270 · Cited by 511 · see citing works
Rachuri, K. K., Musolesi, M., Cecilia Mascolo, C., Rentfrow, P. J., Longworth, C., & Auçinas, A. (2010) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/1864349.1864393 · Cited by 509 · see citing works
ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge
Wu, Z., Kinnunen, T., Evans, N., Yamagishi, J., Hanilçi, C., Sahidullah, M., & Sizov, A. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-462 · Cited by 484 · see citing works
Restructuring of deep neural network acoustic models with singular value decomposition
Xue, J., Li, J., & Gong, Y. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-552 · Cited by 433 · see citing works
Exponential Family Harmoniums with an Application to Information Retrieval
Welling, M., Rosen‐Zvi, M., & Hinton, G. E. (2004) · Cited by 423 · see citing works
Liu, X., Gao, J., He, X., Deng, L., Duh, K., & Wang, Y.-Y. (2015) · Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.3115/v1/n15-1092 · Cited by 395 · see citing works
Feature Learning for Activity Recognition in Ubiquitous Computing
Plötz, T., Hammerla, N., & Olivier, P. (2011) · Newcastle University · doi:10.25405/ncl.34175319 · Cited by 337 · see citing works
Lane, N. D., Georgiev, P., & Qendro, L. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2804262 · Cited by 310 · see citing works
Can Deep Learning Revolutionize Mobile Sensing?
Lane, N. D., & Georgiev, P. (2015) · Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications · doi:10.1145/2699343.2699349 · Cited by 282 · see citing works
Bhattacharya, S., & Lane, N. D. (2016) · Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems CD-ROM · doi:10.1145/2994551.2994564 · Cited by 236 · see citing works
PD Disease State Assessment in Naturalistic Environments Using Deep Learning
Hammerla, N., Fisher, J., Andras, P., Rochester, L., Walker, R., & Ploetz, T. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9484 · Cited by 179 · see citing works
From smart to deep: Robust activity recognition on smartwatches using deep learning
Bhattacharya, S., & Lane, N. D. (2016) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2016.7457169 · Cited by 175 · see citing works
SpeakerSense: Energy Efficient Unobtrusive Speaker Identification on Mobile Phones
Lu, H., Brush, A. J. B., Priyantha, N. B., Karlson, A. K., & Liu, J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21726-5_12 · Cited by 155 · see citing works
Mathur, A., Lane, N. D., Bhattacharya, S., Boran, A., Forlivesi, C., & Kawsar, F. (2017) · Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3081333.3081359 · Cited by 128 · see citing works
Xu, C., Li, S., Liu, G., Zhang, Y., Miluzzo, E., Chen, Y.-F. R., Li, J., & Firner, B. (2013) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2493432.2493435 · Cited by 122 · see citing works
Reshaping deep neural network for fast decoding by node-pruning
He, T., Fan, Y., Qian, Y., Tan, T., & Yu, K. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853595 · Cited by 119 · see citing works
Lee, Y., Min, C., Hwang, C., Lee, J., Hwang, I., Ju, Y., Yoo, C., Moon, M., Lee, U., & Song, J. (2013) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2462456.2465426 · Cited by 114 · see citing works
A new filter bank theory for time-frequency representation
Smith, M., & Barnwell, T. P., III (1987) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1987.1165139 · Cited by 106 · see citing works
Luo, C., & Chan, M. C. (2013) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/2517351.2517353 · Cited by 41 · see citing works
Lane, N. D., Bhattacharya, S., Mathur, A., Forlivesi, C., & Kawsar, F. (2016) · Mobile Computing, Applications, and Services · doi:10.4108/eai.30-11-2016.2267463 · Cited by 37 · see citing works
Histogram of gradients of Time-Frequency Representations for Audio scene detectionPreprint
Rakotomamonjy, A., & Gasso, G. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.04909 · Cited by 7 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,847 · see citing works
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762 · see citing works
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Toward 6G
Vaezi, M., Azari, A., Khosravirad, S. R., Shirvanimoghaddam, M., Azari, M. M., Chasaki, D., & Popovski, P. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3151028 · Cited by 654 · see citing works
Explainable Deep Learning: A Field Guide for the Uninitiated
Ras, G., Xie, N., van Gerven, M. A. J., & Doran, D. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13200 · Cited by 486 · see citing works
Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review
Shuvo, M. M. H., Islam, S. K., Cheng, J., & Morshed, B. I. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3226481 · Cited by 436 · see citing works
“The accuracy can be further enhanced by training shared layers and multitask learning [398].”
BackgroundMulti-task Self-Supervised Learning for Human Activity Detection
Saeed, A., Ozcelebi, T., & Lukkien, J. (2019) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3328932 · Cited by 346 · see citing works
Multimodal Deep Learning for Activity and Context Recognition
Radu, V., Tong, C., Bhattacharya, S., Lane, N. D., Mascolo, C., Marina, M. K., & Kawsar, F. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3161174 · Cited by 281 · see citing works
Edge Intelligence: Empowering Intelligence to the Edge of Network
Xu, D., Li, T., Li, Y., Su, X. J., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3119950 · Cited by 281 · see citing works
Yao, S., Zhao, Y., Shao, H., Liu, S., Liu, D., Su, L., & Abdelzaher, T. (2018) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3274783.3274840 · Cited by 146 · see citing works
Edge Intelligence: Architectures, Challenges, and ApplicationsPreprint
Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12172 · Cited by 184 · see citing works
Deep Learning in Mobile and Wireless Networking: A SurveyPreprint
Zhang, C., Patras, P., & Haddadi, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.04311 · Cited by 121 · see citing works
Adaptive deep learning model selection on embedded systems
Taylor, B., Marco, V. S., Wolff, W., Elkhatib, Y., & Wang, Z. (2018) · ACM SIGPLAN Notices · doi:10.1145/3299710.3211336 · Cited by 114 · see citing works
Adaptive deep learning model selection on embedded systems
Taylor, B., Marco, V. S., Wolff, W., Elkhatib, Y., & Wang, Z. (2018) · Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems · doi:10.1145/3211332.3211336 · Cited by 90 · see citing works
Artificial Intelligence of Things: A Survey
Siam, M. S. I., Ahn, H., Liu, L., Alam, S., Shen, H., Cao, Z., Shroff, N. B., Krishnamachari, B., Srivastava, M. B., & Zhang, M. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3690639 · Cited by 90 · see citing works
Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection
Marco, V. S., Taylor, B., Wang, Z., & Elkhatib, Y. (2020) · Lancaster EPrints (Lancaster University) · Cited by 78 · see citing works
A Survey on Deep Neural Network Compression: Challenges, Overview, and SolutionsPreprint
Mishra, R., Gupta, H. P., & Dutta, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.03954 · Cited by 92 · see citing works
Watching and Safeguarding Your 3D Printer: Online Process Monitoring Against Cyber-Physical Attacks
Gao, Y., Li, B., Wang, W., Xu, W., Zhou, C., & Jin, Z. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3264918 · Cited by 61 · see citing works
Collaborative Intelligence: Challenges and Opportunities
Bajić, I. V., Lin, W., & Tian, Y. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413943 · Cited by 65 · see citing works
Multi-Task Zipping via Layer-wise Neuron SharingPreprint
He, X., Zhou, Z., & Thiele, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09791 · Cited by 43 · see citing works
Transforming Large-Size to Lightweight Deep Neural Networks for IoT Applications
Mishra, R., & Gupta, H. P. (2022) · ACM Computing Surveys · doi:10.1145/3570955 · Cited by 43 · see citing works
Transfer Learning in Sensor-Based Human Activity Recognition: A Survey
Dhekane, S. G., & Ploetz, T. (2025) · ACM Computing Surveys · doi:10.1145/3717608 · Cited by 42 · see citing works
Samanta, R. K., Sadhukhan, B., Samaddar, H., Sarkar, S., Koner, C., & Ghosh, M. (2022) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12114 · Cited by 45 · see citing works
A Smart Classroom Based on Deep Learning and Osmotic IoT Computing
Pacheco, A., Cano, P., Flores, E., Trujillo, E., & Márquez, P. (2018) · Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI) · doi:10.1109/coniiti.2018.8587095 · Cited by 34 · see citing works
Survey on Emotion Sensing Using Mobile Devices
Yang, K., Tag, B., Wang, C., Gu, Y., Sarsenbayeva, Z., Dingler, T., Wadley, G., & Gonçalves, J. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3220484 · Cited by 31 · see citing works
Qin, Q., Ren, J., Yu, J., Wang, H., Gao, L. C., Zheng, J., Feng, Y., Fang, J., & Wang, Z. (2018) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/bdcloud.2018.00110 · Cited by 29 · see citing works
Differentiable Neural Network Pruning to Enable Smart Applications on Microcontrollers
Liberis, E., & Lane, N. D. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3569468 · Cited by 29 · see citing works
Toma, C., Popa, M., Iancu, B., Doinea, M., Pascu, A., & Ioan-Dutescu, F. (2022) · Electronics · doi:10.3390/electronics11213507 · Cited by 22 · see citing works
Smart Classrooms Aided by Deep Neural Networks Inference on Mobile Devices
Pacheco, A., Flores, E., Sanchez, R., & Almanza-Garcia, S. (2018) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2018.8500260 · Cited by 18 · see citing works
Kwon, Y. D., Chauhan, J., & Cecilia Mascolo, C. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1091 · Cited by 18 · see citing works
Collaborative Intelligence: Challenges and OpportunitiesPreprint
Bajić, I. V., Lin, W., & Tian, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06841 · Cited by 18 · see citing works
MyoKey: Inertial Motion Sensing and Gesture-Based QWERTY Keyboard for Extended Realities
Shatilov, K., Kwon, Y. D., Lee, L., Chatzopoulos, D., & Hui, P. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3156939 · Cited by 15 · see citing works
A Discreet Wearable Long-Range Emergency System Based on Embedded Machine Learning
Orfanidis, C., Hassen, R. B. H., Kwiek, A., Fafoutis, X., & Jacobsson, M. (2021) · 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) · doi:10.1109/percomworkshops51409.2021.9430981 · Cited by 14 · see citing works
Information flow perception modeling and optimization of Internet of Things for cloud services
Liu, J., Duan, Y., Wu, Y., Chen, R., Chen, L., & Chen, G. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.012 · Cited by 13 · see citing works
Yapay Zeka Sohbet Robotları ve ChatGPT’nin Hemşirelik Eğitiminde Kullanılması
İlaslan, E. (2023) · Akdeniz Hemşirelik Dergisi · doi:10.59398/ahd.1330341 · Cited by 14 · see citing works
Variational Information Bottleneck for Effective Low-Resource Audio Classification
Si, S., Wang, J., Sun, H., Wu, J., Zhang, C., Qu, X., Cheng, N., Chen, L., & Xiao, J. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-2028 · Cited by 14 · see citing works
Augmenting Conversational Agents with Ambient Acoustic Contexts
Park, C., Min, C., Bhattacharya, S., & Kawsar, F. (2020) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3379503.3403535 · Cited by 10 · see citing works
FedMPT: Federated Learning for Multiple Personalized Tasks Over Mobile Computing
Zhang, X., Ou, Z., & Yang, Z. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3246463 · Cited by 9 · see citing works
Runtime Deep Model Multiplexing for Reduced Latency and Energy Consumption Inference
Eshratifar, A. E., & Pedram, M. (2020) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd50377.2020.00053 · Cited by 8 · see citing works
On-Device Deep Multi-Task Inference via Multi-Task Zipping
He, X., Wang, X., Zhou, Z., Wu, J., Yang, Z., & Thiele, L. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3124306 · Cited by 7 · see citing works
Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection
Marco, V. S., Taylor, B., Wang, Z., & Elkhatib, Y. (2020) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3371154 · Cited by 80 · see citing works
Capturing Interaction Quality in Long Duration (Simulated) Space Missions With Wearables
Gedik, E., Olenick, J. S., Chang, C.-H. D., Kozlowski, S. W. J., & Hung, H. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3176967 · Cited by 6 · see citing works
Deep Learning Models for Audio Processing Applications Under Resource-Constrained Devices: A Survey
Debelee, T. G., & Ayano, Y. M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-31327-1_12 · Cited by 6 · see citing works
Adaptive Selection of Deep Learning Models on Embedded SystemsPreprint
Taylor, B., Marco, V. S., Wolff, W., Elkhatib, Y., & Wang, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.04252 · Cited by 6 · see citing works
Deep Reinforcement Learning for Wireless Network
Sharma, B., Saini, R. K., Singh, A., & Singh, K. K. (2020) · Wiley eBooks · doi:10.1002/9781119640554.ch3 · Cited by 6 · see citing works
The benefit of the doubt: Uncertainty aware sensing for edge computing platforms
Qendro, L., Chauhan, J., Ramos, A. G. C. P., & Mascolo, C. (2021) · arXiv · doi:10.48550/arxiv.2102.05956 · Cited by 6 · see citing works
What-If XAI Framework (WiXAI): From Counterfactuals towards Causal Understanding
Kshetry, N., & Kantardzic, M. M. (2024) · Journal of Computer and Communications · doi:10.4236/jcc.2024.126011 · Cited by 5 · see citing works
Wei, Y., Xiong, J., Liu, H., Yu, Y., Pan, J. J., & Du, J. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3631460 · Cited by 5 · see citing works
Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications
Kwon, Y. D., Chauhan, J., Kumar, A., Hui, P., & Cecilia Mascolo, C. (2021) · ePrints Soton (University of Southampton) · doi:10.17863/cam.81436 · Cited by 23 · see citing works
Dong, B., Wang, X., & Cao, Q. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/8091383 · Cited by 5 · see citing works
SCOPE: Stochastic Cartographic Occupancy Prediction Engine for Uncertainty-Aware Dynamic Navigation
Xie, Z., & Dames, P. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3578234 · Cited by 4 · see citing works
Assessment of Innovative Architectures, Challenges and Solutions of Edge Intelligence
Siltanen, H., & Vlrtanen, L. (2022) · Journal of Machine and Computing · doi:10.53759/7669/jmc202202020 · Cited by 4 · see citing works
ScaleFlow: Efficient Deep Vision Pipeline with Closed-Loop Scale-Adaptive Inference
Leng, Y., Liu, R., Guo, H., Chen, S., & Yao, S. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612412 · Cited by 4 · see citing works
TinyMM: Multimodal-Multitask Machine Learning on Low-Power MCUs for Smart Glasses
Demagh, L., Garda, P. F., Gilbert, C., & Hachicha, K. (2023) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors56945.2023.10325296 · Cited by 3 · see citing works
Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach
Bua, C., Fiorini, F., Pagano, M., Adami, D., & Giordano, S. (2025) · Future Internet · doi:10.3390/fi17050214 · Cited by 3 · see citing works
Deep Learning Applications in Mobile Networks
Dašić, D., Vučetić, M., Kee, G. H. A., & Stanković, M. S. (2019) · International Scientific Conference Sinteza · doi:10.15308/sinteza-2019-553-560 · Cited by 3 · see citing works
Run-time Deep Model Multiplexing
Eshratifar, A. E., & Pedram, M. (2020) · Cited by 2 · see citing works
Application of deep learning algorithms and architectures in the new generation of mobile networks
Dašić, D., Vučetić, M., Ilić, N., Stanković, M. S., & Beko, M. (2021) · Serbian Journal of Electrical Engineering · doi:10.2298/sjee2103397d · Cited by 2 · see citing works
Variational Information Bottleneck for Effective Low-resource Audio ClassificationPreprint
Si, S., Wang, J., Sun, H., Wu, J., Zhang, C., Qu, X., Cheng, N., Chen, L., & Xiao, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.04803 · Cited by 2 · see citing works
Multi-Task Adapters for On-Device Audio Inference
Tagliasacchi, M., Quitry, F. D. C., & Roblek, D. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.2988158 · Cited by 1 · see citing works
Lobachev, I. M., Antoshchuk, S. G., & Hodovychenko, M. A. (2021) · Herald of Advanced Information Technology · doi:10.15276/hait.03.2021.3 · Cited by 1 · see citing works
Can You Do Real-Time Gesture Recognition with 5 Watts?
Rahman, A., Kovalenko, M., Przewozny, D., Tomotaki-Dawoud, K., Chojecki, P., Eisert, P., & Bosse, S. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9659029 · Cited by 1 · see citing works
Deep Neural Mobile NetworkingPreprint
Zhang, C. (2020) · arXiv (Cornell University) · doi:10.7488/era/356 · Cited by 1 · see citing works
Multimodal Knowledge Extraction via Model Distillation for Affective ComputingPreprint
Liu, S., Liu, Z., Ye, X., Wang, L., Wang, X., & Weng, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6250092
SPIDER: Lightweight Speaker Identification on Resource-Constrained Embedded Devices
Gallacher, M., Boano, C. A., Sankar, M. S. A., Roedig, U., Lunardi, W. T., & Baddeley, M. (2026) · doi:10.1145/3774906.3800492
Short Paper: Towards Algorithmically Grounded Embedded AI Models
Wang, Z., Uddin, M. Y. S., & Nirjon, S. (2026) · Proceedings of the 2026 ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems · doi:10.1145/3774906.3800495
Tiny Large Language Models for IoT networks: Potentials and challenges
Golec, M., Melhem, S. B., Khamayseh, Y., Alwarafy, A., & Al‐Dhahir, N. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.111406
MKE: A Lightweight Framework for Multimodal Knowledge Extraction via Knowledge Distillation
Liu, S., Ye, X., Wang, X., Wang, L., Dong, W., & An, M. (2026) · Electronics · doi:10.3390/electronics15194474
- Authors
- Georgiev, P., Bhattacharya, S., Lane, N. D., & Mascolo, C.
- Affiliations
- University of Cambridge (GB)
- Nokia Bell Labs
- University College London (GB)
- University of Cambridge
- Venue
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
- Published
- 2017-09-11
- Volume
- 1
- Issue
- 3
- Pages
- 1-19
- ISSN
- 2474-9567 (ISSN-L)
- OpenAlex
- W2754395205
- Cited by
- 67
- References
- 48
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 2 · 2023: 7 · 2022: 12 · 2021: 13 · 2020: 11 · 2019: 3 · 2018: 11
- FWCI
- 5.69
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Microsoft Research
- Topics
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- multi-task learning, mobile devices, low-resource computing, keyword spotting, energy efficiency, execution time reduction, memory usage, IoT devices
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
