Proceedings ArticleDOI
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Hong Lu,Wei Pan,Nicholas D. Lane,Tanzeem Choudhury,Andrew T. Campbell +4 more
- pp 165-178
TLDR
This paper proposes SoundSense, a scalable framework for modeling sound events on mobile phones that represents the first general purpose sound sensing system specifically designed to work on resource limited phones and demonstrates that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.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.read more
Citations
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Proceedings ArticleDOI
Using approximated auditory roughness as a pre-filtering feature for human screaming and affective speech AED
TL;DR: This work evaluates the potential of detecting screaming and affective speech using Auditory Roughness and proposes a very light-weight approximation method and shows that the approximated roughness returns higher accuracy.
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Nonverbal acoustic communication in human-computer interaction
Yong Lin,Fillia Makedon +1 more
TL;DR: This paper introduces how nonverbal acoustic communication can be utilized in human-computer interaction and provides a design framework of nonverbal communication based intelligent agents.
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An Empirical Analysis of Perforated Audio Classification
Mahathir Monjur,Shahriar Nirjon +1 more
TL;DR: This paper model perforation, demonstrates how it affects the classification accuracy, and proposes two approaches to deal with the problem, and quantifies the loss of accuracy of a standard classifier when the input audio is perforated.
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PION: Human mobility-based service provisioning framework for smartphone users
TL;DR: This paper introduces PION, a framework for personalized service provisioning to manage diverse user contexts and provide appropriate mobile services in daily life and believes that the PION framework is a viable context-aware system for smartphone users.
Proceedings ArticleDOI
AudioIMU: Enhancing Inertial Sensing-Based Activity Recognition with Acoustic Models
TL;DR: In this article , a teacher-student framework is proposed to derive an IMU-based human activity recognition (HAR) model, where an advanced audio-based teacher model is incorporated to guide the student HAR model.
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