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Sensors (Basel). 2019 Aug 9;19(16). pii: E3481. doi: 10.3390/s19163481.

Feature Extraction Methods Proposed for Speech Recognition Are Effective on Road Condition Monitoring Using Smartphone Inertial Sensors.

Author information

1
Faculty of Engineering, National University of East Timor, Hera, East Timor.
2
Faculty of Engineering, Gifu University, Gifu 501-1193, Japan. fukai@gifu-u.ac.jp.
3
Faculty of Engineering, Gifu University, Gifu 501-1193, Japan.

Abstract

The objective of our project is to develop an automatic survey system for road condition monitoring using smartphone devices. One of the main tasks of our project is the classification of paved and unpaved roads. Assuming recordings will be archived by using various types of vehicle suspension system and speeds in practice, hence, we use the multiple sensors found in smartphones and state-of-the-art machine learning techniques for signal processing. Despite usually not being paid much attention, the results of the classification are dependent on the feature extraction step. Therefore, we have to carefully choose not only the classification method but also the feature extraction method and their parameters. Simple statistics-based features are most commonly used to extract road surface information from acceleration data. In this study, we evaluated the mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction coefficients (PLP) as a feature extraction step to improve the accuracy for paved and unpaved road classification. Although both MFCC and PLP have been developed in the human speech recognition field, we found that modified MFCC and PLP can be used to improve the commonly used statistical method.

KEYWORDS:

deep neural network; feature extraction; paved and unpaved classification; road condition monitoring; signal processing; smartphone inertial sensors

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