Vehicle speed estimation using audio features and neural networks

Giraldo-Guzmán, Jader; Marrugo, Andrés G.; Contreras-Ortiz, Sonia H.

doi:10.1109/andescon.2016.7836250

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

2024

Publication Types

Select...

Other3

Article2

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 8 publications

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Cyclist Safety Device based on Spectral Audio Processing for Approaching Vehicles Detection

Flammini,

Sisinni,

Iacono

et al. 2024

2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

Cyclist Safety Device based on Spectral Audio Processing for Approaching Vehicles Detection

Flammini,

Sisinni,

Iacono

et al. 2024

2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

Mel-spectrogram features for acoustic vehicle detection and speed estimation

Bulatovic

Djukanović

2022

2022 26th International Conference on Information Technology (IT)

View full text Add to dashboard Cite

The paper addresses acoustic vehicle detection and speed estimation from single sensor measurements. We predict the vehicle's pass-by instant by minimizing clipped vehicle-to-microphone distance, which is predicted from the mel-spectrogram of input audio, in a supervised learning approach. In addition, mel-spectrogram-based features are used directly for vehicle speed estimation, without introducing any intermediate features. The results show that the proposed features can be used for accurate vehicle detection and speed estimation, with an average error of 7.87 km/h. If we formulate speed estimation as a classification problem, with a 10 km/h discretization interval, the proposed method attains the average accuracy of 48.7% for correct class prediction and 91.0% when an offset of one class is allowed. The proposed method is evaluated on a dataset of 304 urban-environment on-field recordings of ten different vehicles.

show abstract

Vehicle Speed Estimation From Audio Signals Using 1D Convolutional Neural Networks

Cavor

Djukanović

2023

2023 27th International Conference on Information Technology (IT)

View full text Add to dashboard Cite

Vehicle speed estimation using audio features and neural networks

Cited by 8 publications

References 12 publications

Cyclist Safety Device based on Spectral Audio Processing for Approaching Vehicles Detection

Cyclist Safety Device based on Spectral Audio Processing for Approaching Vehicles Detection

Mel-spectrogram features for acoustic vehicle detection and speed estimation

Vehicle Speed Estimation From Audio Signals Using 1D Convolutional Neural Networks

Contact Info

Product

Resources

About