C - Wang - Sound-Based Transportation Mode Recognition With Smartphones (ICASSP, 2019) (1).pdf (1.27 MB)
Sound-based transportation mode recognition with smartphones
conference contribution
posted on 2023-06-09, 17:07 authored by Lin Wang, Daniel RoggenDaniel RoggenSmartphone-based identification of the mode of transportation of the user is important for context-aware services. We investigate the feasibility of recognizing the 8 most common modes of locomotion and transportation from the sound recorded by a smartphone carried by the user. We propose a convolutional neural network based recognition pipeline, which operates on the short- time Fourier transform (STFT) spectrogram of the sound in the log domain. Experiment with the Sussex-Huawei locomotion- transportation (SHL) dataset on 366 hours of data shows promising results where the proposed pipeline can recognize the activities Still, Walk, Run, Bike, Car, Bus, Train and Subway with a global accuracy of 86.6%, which is 23% higher than classical machine learning pipelines. It is shown that sound is particularly useful for distinguishing between various vehicle activities (e.g. Car vs Bus, Train vs Subway). This discriminablity is complementary to the widely used motion sensors, which are poor at distinguish between rail and road transport.
History
Publication status
- Published
File Version
- Accepted version
Journal
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)ISSN
1520-6149Publisher
IEEEExternal DOI
Page range
930-934Event name
IEEE ICASSP 2019: Spatial Audio Recording and Detection and Classification of Acoustic Scenes and EventsEvent location
Brighton, U.K.Event type
conferenceEvent date
12 -17 May 2019Department affiliated with
- Engineering and Design Publications
Research groups affiliated with
- Sensor Technology Research Centre Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2019-03-06First Open Access (FOA) Date
2019-03-07First Compliant Deposit (FCD) Date
2019-03-05Usage metrics
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