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dc.contributor.authorGeorgiev, Petkoen
dc.contributor.authorLane, Nicholas Den
dc.contributor.authorRachuri, Kiran Ken
dc.contributor.authorMascolo, Ceciliaen
dc.date.accessioned2014-09-05T08:44:30Z
dc.date.available2014-09-05T08:44:30Z
dc.date.issued2014en
dc.identifier.citationSenSys '14, Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, 295-309. DOI: 10.1145/2668332.2668349
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/245877
dc.description.abstractThe rapidly growing adoption of sensor-enabled smartphones has greatly fueled the proliferation of applications that use phone sensors to monitor user behavior. A central sensor among these is the microphone which enables, for instance, the detection of valence in speech, or the identification of speakers. Deploying multiple of these applications on a mobile device to continuously monitor the audio environment allows for the acquisition of a diverse range of sound-related contextual inferences. However, the cumulative processing burden critically impacts the phone battery. To address this problem, we propose DSP.Ear – an integrated sensing system that takes advantage of the latest low-power DSP co-processor technology in commodity mobile devices to enable the continuous and simultaneous operation of multiple established algorithms that perform complex audio inferences. The system extracts emotions from voice, estimates the number of people in a room, identifies the speakers, and detects commonly found ambient sounds, while critically incurring little overhead to the device battery. This is achieved through a series of pipeline optimizations that allow the computation to remain largely on the DSP. Through detailed evaluation of our prototype implementation we show that, by exploiting a smartphone’s co-processor, DSP.Ear achieves a 3 to 7 times increase in the battery lifetime compared to a solution that uses only the phone’s main processor. In addition, DSP.Ear is 2 to 3 times more power efficient than a naïve DSP solution without optimizations. We further analyse a large-scale dataset from 1320 Android users to show that in about 80-90% of the daily usage instances DSP.Ear is able to sustain a full day of operation (even in the presence of other smartphone workloads) with a single battery charge.
dc.description.sponsorshipThis work was supported by Microsoft Research through its PhD Scholarship Program.
dc.languageen_USen
dc.language.isoen_USen
dc.titleDSP.Ear: Leveraging Co-Processor Support for Continuous Audio Sensing on Smartphonesen
dc.typeArticle
dc.description.versionThis is the author's accepted manuscript. The final version is available from ACM in the proceedings of the ACM Conference on Embedded Networked Sensor Systems: http://dl.acm.org/citation.cfm?id=2668349.en
prism.endingPage309
prism.publicationDate2014en
prism.startingPage295
rioxxterms.versionofrecord10.1145/2668332.2668349en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2014en
dc.contributor.orcidMascolo, Cecilia [0000-0001-9614-4380]
rioxxterms.typeJournal Article/Reviewen


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