T14-SoundSense - SoundSense by Andrius Andrijauskas...

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SoundSense by Andrius Andrijauskas
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Introduction Today’s mobile phones come with various embedded sensors such as GPS, WiFi, compass, etc. Arguably, one of the most overlooked sensors, available on every device, is the microphone. Sound captured by a mobile phone’s microphone can be a rich source of information.
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Introduction From the captured sound various inferences can be made about the person carrying the phone. o Conversation detection o Activity recognition o Location classification
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Challenges Scaling Robustness Device integration
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Scaling People live in different environments, containing a wide variety of everyday sounds. Typically, sounds encountered by a student will be different from those encountered by a truck driver. It is no feasible to collect every possible sound and classify it.
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Robustness People carry phones in a number of different ways; for example, in the pocket, on a belt, in a bag. Location of the phone with respect to the body presents various challenges because in the same environment sound levels will vary based on the phone’s location.
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Robustness Phone context alters the volume: o A – in hand of a user facing source o B – in pocket of a user facing source o C – in hand of a user facing away from the source
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Device Integration Algorithms that perform sound sensing must be integrated in a way not to hinder phone’s primary function. Algorithms must be simple enough to run on a mobile device. Captured audio data may be privacy sensitive, so user privacy must be protected.
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SoundSense SoundSense – scalable sound sensing framework for mobile phones. It is the first general purpose sound event classification system designed specifically to address scaling, robustness, and device integration.
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Architecture
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Sensing and Preprocessing
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