Affiliation:
1. School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China
2. School of Software Engineering, Southeast University, Suzhou, Jiangsu, China
Abstract
In the evaluation of vehicle audio warning system, there is no automatic method. Besides, due to the noise interference of in-vehicle environmental, the quantity limitation and only positive training samples, the accuracy of traditional template matching or identification methods for audio is low. To solve the above problems, an efficient, accurate, and automatic evaluation method is proposed for vehicle audio warning system. Firstly, logmmse-spectrum subtraction method is used to filter the dynamic noise and static noise of the evaluation audio acquired in the in-vehicle environment. Secondly, the end point detection based on short-time energy is used to obtain the effective audio segment after noise reduction, and the start time of the audio warning segment can be accurately obtained. Then, the Mel Frequency Cepstrum Coefficient (MFCC) feature and the polynomial fitting feature of each audio segment are extracted. The hybrid features are treated as the input of the Hidden Markov Model-Gaussian Mixture Model (GMM-HMM) based audio matching model. Finally, according to frame shift set by endpoint detection and the audio sampling frequency, the emitted time of matched audio warning can be calculated to support the evaluation of vehicle audio warning system. The experimental result shows that, with dynamic-static noise reduction and MFCC-polynomial hybrid feature, the average matching accuracy of the proposed method reaches 99.6% in the case of only five training samples.
Funder
National Natural Science Foundation of China
National Key Research and Development Project
Fundamental Research Funds for the Central Universities
Opening Project of Key Laboratory of Technology on Intelligent Transportation Systems