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
Code in the air: simplifying sensing on smartphones
Tim Kaler,John Patrick Lynch,Timothy Peng,Lenin Ravindranath,Arvind Thiagarajan,Hari Balakrishnan,Samuel Madden +6 more
TL;DR: Modern smartphones are equipped with a wide variety of sensors including GPS, WiFi and cellular radios capable of positioning, accelerometers, magnetic compasses and gyroscopes, light and proximity sensors, and cameras that have made smartphones an attractive platform for collaborative sensing applications where phones cooperatively collect sensor data to perform various tasks.
Journal ArticleDOI
Participants Ranking Algorithm for Crowdsensing in Mobile Communication
TL;DR: This article provides the efficient raking process of participants to assign the priorities for performing tasks in smooth manner and presents the concept of Crowd sensing along with the raking procedure for a large user pool.
Journal ArticleDOI
A Two-Level Approach to Characterizing Human Activities from Wearable Sensor Data
TL;DR: This paper proposes an approach that splits the concept of physical activity into two sub-categories that are supposed to have functional relationship with each other and should help to better understand activities on a larger scale, and shows different methods of collecting, interpreting and evaluating data from different sensor sources.
Effect of noise-in-speech on MFCC parameters
TL;DR: The effect of noise in the speech signal on the extracted speech features that are used in speech recognition is studied and additive Gaussian noise-in-speech results in an error in MFCC parameter estimation which is also Gaussian.
Posted Content
Fine-Grained User Profiling for Personalized Task Matching in Mobile Crowdsensing
TL;DR: Zhang et al. as mentioned in this paper proposed a personalized task recommender system for mobile crowdsensing, which recommends tasks to users based on a recommendation score that jointly takes each user's preference and reliability into consideration.
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