{"id":4761,"date":"2015-09-07T09:00:22","date_gmt":"2015-09-07T16:00:22","guid":{"rendered":"https:\/\/blogs.msdn.microsoft.com\/msr_er\/?p=4761"},"modified":"2016-07-20T07:29:00","modified_gmt":"2016-07-20T14:29:00","slug":"ubicomp-2015-best-paper-awards","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/ubicomp-2015-best-paper-awards\/","title":{"rendered":"UBICOMP 2015: Best paper awards"},"content":{"rendered":"

The winners of the best paper award for UBICOMP 2015 (opens in new tab)<\/span><\/a> go to:<\/p>\n

 <\/p>\n

DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning by\u00a0Nicholas Lane, Petko Georgiev, Lorena Qendro (opens in new tab)<\/span><\/a><\/p>\n

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 as radically changed related audio modeling domains like speech recognition. In this paper, we present DeepEar\u2014the 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 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\u2019s battery daily.<\/p>\n

Enhancing Mobile Apps to Use Sensor Hubs without Programmer Effort by\u00a0Haichen Shen, Aruna Balasubramanian, Anthony Lamarca, David Wetherall (opens in new tab)<\/span><\/a><\/p>\n

Always-on continuous sensing apps drain the battery quickly because they prevent the main processor from sleeping. Instead, sensor hub hardware, available in many smartphones today, can run continuous sensing at lower power while keeping the main processor idle. However, developers have to divide functionality between the main processor and the sensor hub. We implement MobileHub, a system that automatically rewrites applications to leverage the sensor hub without additional programming effort. MobileHub uses a combination of dynamic taint tracking and machine learning to learn when it is safe to leverage the sensor hub without affecting application semantics. We implement MobileHub in Android and prototype a sensor hub on a 8-bit AVR micro-controller.<\/p>\n

We experiment with 20 applications from Google Play. Our evaluation shows that MobileHub significantly reduces power consumption for continuous sensing apps.<\/p>\n

From Computational Thinking to Computational Making by\u00a0Jennifer Rode, Anne Weibert, Andrea Marshall, Konstantin Aal, Thomas von Rekowski, Houda El mimouni, Jennifer Booker (opens in new tab)<\/span><\/a><\/p>\n

No abstract available.<\/p>\n

For more computer science research news, visit ResearchNews.com (opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

The winners of the best paper award for UBICOMP 2015 go to:   DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning by\u00a0Nicholas Lane, Petko Georgiev, Lorena Qendro Microphones are remarkably powerful sensors of human behavior and context. However, audio sensing is highly susceptible to wild fluctuations in accuracy when used in […]<\/p>\n","protected":false},"author":32627,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[],"msr_hide_image_in_river":0,"footnotes":""},"categories":[194468,194476,194459],"tags":[195307,196536,197537],"research-area":[],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-4761","post","type-post","status-publish","format-standard","hentry","category-audio","category-devices-and-hardware","category-researchnews","tag-deepear","tag-mobilehub","tag-ubicomp-2015","msr-locale-en_us"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-events":[],"related-researchers":[],"msr_type":"Post","byline":"","formattedDate":"September 7, 2015","formattedExcerpt":"The winners of the best paper award for UBICOMP 2015 go to:   DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning by\u00a0Nicholas Lane, Petko Georgiev, Lorena Qendro Microphones are remarkably powerful sensors of human behavior and context. However, audio sensing is…","locale":{"slug":"en_us","name":"English","native":"","english":"English"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/4761","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/users\/32627"}],"replies":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/comments?post=4761"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/4761\/revisions"}],"predecessor-version":[{"id":260694,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/4761\/revisions\/260694"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=4761"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/categories?post=4761"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/tags?post=4761"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=4761"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=4761"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=4761"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=4761"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=4761"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=4761"},{"taxonomy":"msr-promo-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-promo-type?post=4761"},{"taxonomy":"msr-podcast-series","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-podcast-series?post=4761"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}