eprintid: 1503675
rev_number: 30
eprint_status: archive
userid: 608
dir: disk0/01/50/36/75
datestamp: 2017-05-30 12:22:06
lastmod: 2021-10-10 22:52:31
status_changed: 2017-05-30 12:22:06
type: proceedings_section
metadata_visibility: show
creators_name: Lane, ND
creators_name: Georgiev, P
creators_name: Qendro, L
title: DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments using Deep Learning
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Mobile sensing, deep learning, audio sensing.
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
© 2015 ACM
abstract: 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.
date: 2015-09-11
date_type: published
publisher: Association for Computing Machinery
official_url: http://dx.doi.org/10.1145/2750858.2804262
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1122803
doi: 10.1145/2750858.2804262
isbn_13: 9781450335744
lyricists_name: Lane, Nicholas
lyricists_id: NLANE01
actors_name: Lane, Nicholas
actors_id: NLANE01
actors_role: owner
full_text_status: public
series: ACM International Joint Conference on Pervasive and Ubiquitous Computing
publication: UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
volume: 2015
place_of_pub: Osaka, Japan
pagerange: 283-294
event_title: UbiComp '15 ACM International Joint Conference on Pervasive and Ubiquitous Computing - September 07 - 11, 2015
book_title: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
editors_name: Miyahara, H
editors_name: Tokuda, H
editors_name: Mase, K
editors_name: Langheinrich, M
citation:        Lane, ND;    Georgiev, P;    Qendro, L;      (2015)    DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments using Deep Learning.                     In: Miyahara, H and Tokuda, H and Mase, K and Langheinrich, M, (eds.) Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing.  (pp. pp. 283-294).  Association for Computing Machinery: Osaka, Japan.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1503675/1/Lane%2C%20Georgiev%2C%20%26%20Qendro%202015%20UbiComp.pdf