ArticleScientific reports2021
Hybrid neural network based on novel audio feature for vehicle type identification.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization.Sensors (Basel, Switzerland) · 2022Article
- Research on imaging method of driver's attention area based on deep neural network.Scientific reports · 2022Article
- A lightweight YOLOv3 algorithm used for safety helmet detection.Scientific reports · 2022Article
- Fast environmental sound classification based on resource adaptive convolutional neural network.Scientific reports · 2022Article
- Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments.Frontiers in artificial intelligence · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the audio information of different types of vehicle models are distinct, the vehicle information can be identified by the audio signal of vehicle accurately. In real life, in order to determine the type of vehicle, we do not need to obtain the visual information of vehicles and just need to obtain the audio information. In this paper, we extract and stitching different features from different aspects: Mel frequency cepstrum coefficients in perceptual characteristics, pitch class profile in psychoacoustic characteristics and short-term energy in acoustic characteristics. In addition, we improve the neural networks classifier by fusing the LSTM unit into the convolutional neural networks. At last, we put the novel feature to the hybrid neural networks to recognize different vehicles. The results suggest the novel feature we proposed in this paper can increase the recognition rate by 7%; destroying the training data randomly by superimposing different kinds of noise can improve the anti-noise ability in our identification system; and LSTM has great advantages in modeling time series, adding LSTM to the networks can improve the recognition rate of 3.39%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.