C4.5: programs for machine learning
C4.5: programs for machine learning
Cepstral domain segmental feature vector normalization for noise robust speech recognition
Speech Communication - Special issue on robust speech recognition
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Micro-Blog: sharing and querying content through mobile phones and social participation
Proceedings of the 6th international conference on Mobile systems, applications, and services
Managing Context Information in Mobile Devices
IEEE Pervasive Computing
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Proceedings of the 7th international conference on Mobile systems, applications, and services
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
SurroundSense: mobile phone localization via ambience fingerprinting
Proceedings of the 15th annual international conference on Mobile computing and networking
Mining user reviews: from specification to summarization
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Darwin phones: the evolution of sensing and inference on mobile phones
Proceedings of the 8th international conference on Mobile systems, applications, and services
Indoor localization without infrastructure using the acoustic background spectrum
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
SpeakerSense: energy efficient unobtrusive speaker identification on mobile phones
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Real-time speaker identification and verification
IEEE Transactions on Audio, Speech, and Language Processing
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
IODetector: a generic service for indoor outdoor detection
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Using stranger as sensors: temporal and geo-sensitive question answering via social media
Proceedings of the 22nd international conference on World Wide Web
From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews
Proceedings of the 22nd international conference on World Wide Web
The FLDA model for aspect-based opinion mining: addressing the cold start problem
Proceedings of the 22nd international conference on World Wide Web
Proceedings of the 22nd international conference on World Wide Web
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Local search users today decide what business to visit solely based on distance information, and business ratings that can be sparse or stale. We believe that when users search for local businesses, such as bars or restaurants, they need to know more about the ambience of each business, such as how crowded it is, how loud and of what type the music it plays is, as well as how loud the human chatter in the business is. Unfortunately, this information doesn't exist today. In this paper, we propose to automatically crowdsource such rich, local business ambience metadata through real user check-in events. Every time a user checks into a business, the phone is in user's hands, and the phone's sensors can sense the business environment. We leverage the phone's microphone during this time to infer the occupancy and human chatter levels, the music type, as well as the music and noise levels in the business. As people check-in to businesses throughout the day, business metadata can be automatically updated over time, enabling a new generation of local search experience. Using approximately 150 audio traces collected from real businesses of various types over a period of 3 months, we show that by properly extracting the temporal and frequency signatures of the audio signal, it is feasible to train models that can simultaneously infer occupancy, human chatter, music, and noise levels in a business, with higher than 79% accuracy.