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UbiEar: Bringing Location-independent Sound Awareness to the Hard-of-hearing People with Smartphones

Published:30 June 2017Publication History
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Abstract

Non-speech sound-awareness is important to improve the quality of life for the deaf and hard-of-hearing (DHH) people. DHH people, especially the young, are not always satisfied with their hearing aids. According to the interviews with 60 young hard-of-hearing students, a ubiquitous sound-awareness tool for emergency and social events that works in diverse environments is desired. In this paper, we design UbiEar, a smartphone-based acoustic event sensing and notification system. Core techniques in UbiEar are a light-weight deep convolution neural network to enable location-independent acoustic event recognition on commodity smartphons, and a set of mechanisms for prompt and energy-efficient acoustic sensing. We conducted both controlled experiments and user studies with 86 DHH students and showed that UbiEar can assist the young DHH students in awareness of important acoustic events in their daily life.

References

  1. Audio Analytic. 2017. https://www.audioanalytic.com/. (2017).Google ScholarGoogle Scholar
  2. James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, and Balázs Kégl. 2006. Aggregate features and AdaBoost for music classification. Machine Learning 65, 2-3 (2006), 473--484. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Sourav Bhattacharya and Nicholas D Lane. 2016. Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables. In Proc. SenSys. ACM, 176--189. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Danielle Bragg, Nicholas Huynh, and Richard E Ladner. 2016. A Personalizable Mobile Sound Detector App Design for Deaf and Hard-of-Hearing Users. In Proc. ASSETS. ACM, 3--13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J-F Cardoso. 1998. Multidimensional independent component analysis. In Proc. ICASSP, Vol. 4. IEEE, 1941--1944.Google ScholarGoogle ScholarCross RefCross Ref
  6. Andrew Collette. 2015. HDF5 for Python. http://www.h5py.org/. (2015).Google ScholarGoogle Scholar
  7. Simon Dixon. 2006. Onset detection revisited. In Proc. DAFx.Google ScholarGoogle Scholar
  8. Google. 2016. android.util.LruCache. https://developer.android.com/reference/android/util/LruCache.html. (2016).Google ScholarGoogle Scholar
  9. Benjamin M Gorman. 2014. VisAural:: a wearable sound-localisation device for people with impaired hearing. In Proc. ASSETS. ACM, 337--338. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Song Han, Huizi Mao, and William J Dally. 2016. Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. In Proc. ICLR.Google ScholarGoogle Scholar
  11. Toni Heittola, Annamaria Mesaros, Tuomas Virtanen, and Antti Eronen. 2011. Sound event detection in multisource environments using source separation. In Proc. CHiME. 36--40.Google ScholarGoogle Scholar
  12. F Ho-Ching, Jennifer Mankoff, and James A Landay. 2003. Can you see what i hear?: the design and evaluation of a peripheral sound display for the deaf. In Proc. CHI. ACM, 161--168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Forrest N Iandola, Matthew W Moskewicz, Khalid Ashraf, Song Han, William J Dally, and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size. arXiv preprint arXiv:1602.07360 (2016).Google ScholarGoogle Scholar
  14. Keisuke Imoto and Nobutaka Ono. 2015. Acoustic scene analysis from acoustic event sequence with intermittent missing event. In Proc. ICASSP. IEEE, 156--160.Google ScholarGoogle ScholarCross RefCross Ref
  15. Leeo Inc. 2017. Meet the Leeo Smart Alert Nightlight. https://www.leeo.com/meet-the-leeo-smart-alert-nightlight/. (2017).Google ScholarGoogle Scholar
  16. Dhruv Jain, Leah Findlater, Jamie Gilkeson, Benjamin Holland, Ramani Duraiswami, Dmitry Zotkin, Christian Vogler, and Jon E Froehlich. 2015. Head-Mounted Display Visualizations to Support Sound Awareness for the Deaf and Hard of Hearing. In Proc. CHI. ACM, 241--250. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional architecture for fast feature embedding. In Proc. MM. ACM, 675--678. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei. 2014. Large-scale video classification with convolutional neural networks. In Proc. CVPR. IEEE, 1725--1732. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Keras. 2016. Keras: Deep Learning library for Theano and TensorFlow. https://keras.io/. (2016).Google ScholarGoogle Scholar
  20. Hamed Ketabdar and Tim Polzehl. 2009. Tactile and Visual Alerts for Deaf People by Mobile Phones. In Proc. ASSETS. ACM, 253--254. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Adel Khalil, James Sun, Yu Zhang, and Gordon Poole. 2014. RTM noise attenuation and image enhancement using time-shift gathers. In Proc. EAGE Conference and Exhibition.Google ScholarGoogle Scholar
  22. George Kimura. 2017. Ambient Noise Database. http://www.ntt-at.com/product/noise-DB/. (2017).Google ScholarGoogle Scholar
  23. Sergei Kochkin. 2000. MarkeTrak V: Why my hearing aids are in the drawer: The consumers’ perspective. The Hearing Journal 53, 2 (2000), 34--36.Google ScholarGoogle ScholarCross RefCross Ref
  24. Gregoire Lafay. 2017. IEEE DCASE 2016 Challenge-Task 2-Train/Development Datasets. https://archive.org/details/dcase2016_task2_train_dev. (2017). Published February 10, 2016.Google ScholarGoogle Scholar
  25. Nicholas D Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, Lei Jiao, Lorena Qendro, and Fahim Kawsar. 2016. DeepX: A software accelerator for low-power deep learning inference on mobile devices. In Proc. IPSN. ACM, 1--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Nicholas D Lane, Petko Georgiev, and Lorena Qendro. 2015. DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning. In Proc. UbiComp. ACM, 283--294. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Joseph Lee Rodgers and W Alan Nicewander. 1988. Thirteen ways to look at the correlation coefficient. The American Statistician (1988), 59--66.Google ScholarGoogle Scholar
  28. Andy Liaw and Matthew Wiener. 2002. Classification and regression by randomForest. R news 2, 3 (2002), 18--22.Google ScholarGoogle Scholar
  29. Min Lin, Qiang Chen, and Shuicheng Yan. 2013. Network in network. arXiv preprint arXiv:1312.4400 (2013).Google ScholarGoogle Scholar
  30. Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, and Marianna Pensky. 2015. Sparse convolutional neural networks. In Proc. CVPR. IEEE, 806--814.Google ScholarGoogle Scholar
  31. Sicong Liu and Junzhao Du. 2016. Poster: MobiEar-Building an Environment-independent Acoustic Sensing Platform for the Deaf using Deep Learning. In Proc. MobiSys. ACM, 50--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Nitin N Lokhande, Navnath S Nehe, and Pratap S Vikhe. 2012. Voice activity detection algorithm for speech recognition applications. In Proc. ICCIA. IJCA.Google ScholarGoogle Scholar
  33. Hong Lu, Wei Pan, Nicholas D Lane, Tanzeem Choudhury, and Andrew T Campbell. 2009. SoundSense: scalable sound sensing for people-centric applications on mobile phones. In Proc. MobiSys. ACM, 165--178. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Hong Lu, Jun Yang, Zhigang Liu, Nicholas D Lane, Tanzeem Choudhury, and Andrew T Campbell. 2010. The Jigsaw continuous sensing engine for mobile phone applications. In Proc. SenSys. ACM, 71--84. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research Nov (2008), 2579--2605.Google ScholarGoogle Scholar
  36. Tara Matthews, Janette Fong, and Jennifer Mankoff. 2005. Visualizing non-speech sounds for the deaf. In Proc. ASSETS. ACM, 52--59. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Abby McCormack and Heather Fortnum. 2013. Why do people fitted with hearing aids not wear them? International Journal of Audiology 52, 5 (2013), 360--368.Google ScholarGoogle ScholarCross RefCross Ref
  38. Annamaria Mesaros, Toni Heittola, Antti Eronen, and Tuomas Virtanen. 2010. Acoustic event detection in real life recordings. In Proc. EUSIPCO. IEEE, 1267--1271.Google ScholarGoogle Scholar
  39. Matthias Mielke and Rainer Brueck. 2015. Design and evaluation of a smartphone application for non-speech sound awareness for people with hearing loss. In Proc. EMBC. IEEE, 5008--5011.Google ScholarGoogle ScholarCross RefCross Ref
  40. OtoSense. 2017. https://www.otosense.com/. (2017).Google ScholarGoogle Scholar
  41. Jouni Paulus and Tuomas Virtanen. 2005. Drum transcription with non-negative spectrogram factorisation. In Proc. EUSIPCO. IEEE, 1--4.Google ScholarGoogle Scholar
  42. Karol J Piczak. 2015. Environmental sound classification with convolutional neural networks. In Proc. MLSP. IEEE, 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  43. Ilyas Potamitis, Stavros Ntalampiras, Olaf Jahn, and Klaus Riede. 2014. Automatic bird sound detection in long real-field recordings: Applications and tools. Applied Acoustics 80 (2014), 1--9.Google ScholarGoogle ScholarCross RefCross Ref
  44. Tauhidur Rahman, Alexander Travis Adams, Mi Zhang, Erin Cherry, Bobby Zhou, Huaishu Peng, and Tanzeem Choudhury. 2014. BodyBeat: a mobile system for sensing non-speech body sounds.. In Proc. MobiSys. ACM, 2--13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Ann Mette Rekkedal. 2012. Assistive hearing technologies among students with hearing impairment: Factors that promote satisfaction. Journal of Deaf Studies and Deaf Education 17, 4 (2012), 499--517.Google ScholarGoogle ScholarCross RefCross Ref
  46. Android Studio. 2017. Battery Historian Charts. https://developer.android.com/studio/profile/battery-historian-charts.html. (2017).Google ScholarGoogle Scholar
  47. Denis Tomé, Luca Bondi, Luca Baroffio, Stefano Tubaro, Emanuele Plebani, and Danilo Pau. 2016. Reduced memory region based deep Convolutional Neural Network detection. In Proc. ICCE-Berlin. IEEE, 15--19.Google ScholarGoogle ScholarCross RefCross Ref
  48. Emmanuel Vincent, Nancy Bertin, Rémi Gribonval, and Frédéric Bimbot. 2014. From blind to guided audio source separation: How models and side information can improve the separation of sound. Signal Processing Magazine 31, 3 (2014), 107--115.Google ScholarGoogle ScholarCross RefCross Ref
  49. In-Chul Yoo and Dongsuk Yook. 2008. Automatic sound recognition for the hearing impaired. Transactions on Consumer Electronics (2008), 2029--2036. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
      Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 1, Issue 2
      June 2017
      665 pages
      EISSN:2474-9567
      DOI:10.1145/3120957
      Issue’s Table of Contents

      Copyright © 2017 ACM

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      Publication History

      • Published: 30 June 2017
      • Accepted: 1 May 2017
      • Revised: 1 April 2017
      • Received: 1 February 2017
      Published in imwut Volume 1, Issue 2

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