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

By Hong Lu, Wei Pan, Nicholas Donald Lane, Tanzeem Choudhury, Andrew T. Campbell. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2009

688 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/55d3c8eaaba63c7f1e7fbdce35629d145bfdef6c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

A survey of mobile phone sensing (2010, positive): “In SoundSense [ 11 ] a general-purpose sound classification system for mobile phones is developed using a combination of supervised and unsupervised learning.”
A survey on privacy in mobile participatory sensing applications (2011, background): “In addition to noise level, the sound samples can be further analyzed to determine, e.g., whether human voices were recorded in order to recognize the sound context in SoundSense [31].”
Diagnosing New York city's noises with ubiquitous data (2014, background): “Noise Understanding A number of works [2][5][9][11][13] have focused on classifying environmental noises so as to understand a user’s contexts, such as in a car or on a street.”
Deanonymizing mobility traces: using social network as a side-channel (2012, background): “, location, acceleration) generated by everyday mobile devices such as smartphones, tablets, and cars have become quite popular [3, 11, 15, 21, 32].”
A tutorial on human activity recognition using body-worn inertial sensors (2014, positive): “For example, long-term acceleration data recorded on a mobile phone can be segmented using GPS traces [Ashbrook and Starner 2003] or sound recorded using the internal microphone [Lu et al. 2009].”
SensLoc: sensing everyday places and paths using less energy (2010, neutral): “SoundSense uses acoustic signatures to recognize activities and places [21].”
Mobile Phone Sensing Systems: A Survey (2013, methodology): “Then the road bump monitoring application was opportunistically deployed on the phones and the bumps detected by the application were compared with the manual recording of road bumps.”
Cloud-enabled privacy-preserving collaborative learning for mobile sensing (2012, methodology): “Our data set is adversarially chosen; prior work on mobile-phone based sound classification [35] has used half the number of dimensions and an order of magnitude smaller training data set.”
Inferring human mobility patterns from taxicab location traces (2013, background): “INTRODUCTION Applications that are driven by the analysis of geo-tagged sensor data generated by everyday mobile devices such as smartphones, tablets, and cars have become quite popular in the recent past [5, 10, 13].”
BodyScope: a wearable acoustic sensor for activity recognition (2012, methodology): “’s work [19] to automatically find such activities.”
Design of an intelligible mobile context-aware application (2011, neutral): “Features extracted are similar to [12]: e.”
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, background): “Other applications are not so tolerant, but still do not require strict constraints, such as mobile social networks [59], [60], [148]– [151], [153], health care [17], [19], price comparison [129], reporting info about environment [119], sounds [164], conferences [165], or sharing and recommending tr”
Indoor localization without infrastructure using the acoustic background spectrum (2011, neutral): “Accordingly, we focus on background sounds while SoundSense focuses on transient sounds.”
Bewell: A smartphone application to monitor, model and promote wellbeing (2011, methodology): “Inferences are made by applying a combination of feature and classification models developed in previous work, SoundSense [18] and Jigsaw [18].”