SoundSense: scalable sound sensing for people-centric applications on mobile phones

  • Authors:

  • Hong Lu;Wei Pan;Nicholas D. Lane;Tanzeem Choudhury;Andrew T. Campbell

  • Affiliations:

  • Dartmouth College, Hanover, NH, USA;Dartmouth College, Hanover, NH, USA;Dartmouth College, Hanover, NH, USA;Dartmouth College, Hanover, NH, USA;Dartmouth College, Hanover, NH, USA

  • Venue:
  • Proceedings of the 7th international conference on Mobile systems, applications, and services
  • Year:
  • 2009

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Abstract

Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone - a powerful sensor that is capable of making sophisticated inferences about human activity, location, and social events from sound. In this paper, we exploit this untapped sensor not in the context of human communications but as an enabler of new sensing applications. We propose SoundSense, a scalable framework for modeling sound events on mobile phones. SoundSense is implemented on the Apple iPhone and represents the first general purpose sound sensing system specifically designed to work on resource limited phones. The architecture and algorithms are designed for scalability and Soundsense uses a combination of supervised and unsupervised learning techniques to classify both general sound types (e.g., music, voice) and discover novel sound events specific to individual users. The system runs solely on the mobile phone with no back-end interactions. Through implementation and evaluation of two proof of concept people-centric sensing applications, we demostrate that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.