2022 26th International Conference on Information Technology (IT) 2022
DOI: 10.1109/it54280.2022.9743540
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Mel-spectrogram features for acoustic vehicle detection and speed estimation

Abstract: 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 detectio… Show more

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Cited by 14 publications
(4 citation statements)
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“…The resulting dataset consisted of 22 The neural network architecture is inspired by the 1-dimensional Convolutional Neural Network (1D-CNN) 74 with different audio features extracted from audio clips added to enhance classification 75 . These features include mean Mel Frequency Cepstral Coefficients (MFCCs) 41 , Mean Chromagram 44 , Mean Mel Spectrogram 40 , Mean Spectral Contrast 43 and Mean Tonal Centroid 42 . The features are concatenated into a one-dimensional vector by taking a mean along the time axis for each of the 10-second segments.…”
Section: Audio Neural Networkmentioning
confidence: 99%
See 1 more Smart Citation
“…The resulting dataset consisted of 22 The neural network architecture is inspired by the 1-dimensional Convolutional Neural Network (1D-CNN) 74 with different audio features extracted from audio clips added to enhance classification 75 . These features include mean Mel Frequency Cepstral Coefficients (MFCCs) 41 , Mean Chromagram 44 , Mean Mel Spectrogram 40 , Mean Spectral Contrast 43 and Mean Tonal Centroid 42 . The features are concatenated into a one-dimensional vector by taking a mean along the time axis for each of the 10-second segments.…”
Section: Audio Neural Networkmentioning
confidence: 99%
“…We then explored the potential of accurately predicting autism from the full audio band of the ADOS assessment. After normalizing the audio recordings and splitting them into 10-second segments, we extracted several acoustic features [40][41][42][43][44] that were then passed through a convolutional neural network (see Figure 2 and Methods). Following standard model hyperparameter tuning procedure, we deployed the 80-20 training validation split.…”
Section: Autism Prediction Using Audio Featuresmentioning
confidence: 99%
“…MA is predicted in a supervised fashion from the logmel spectrogram (LMS) of input audio. LMS represents a very popular feature in acoustic classification applications [32] and it proved very reliable in vehicle detection and speed estimation [3], [4], [8].…”
Section: B Acoustic Featuresmentioning
confidence: 99%
“…Large volumes of traffic data enable significant improvements in the performance of transportation, traffic safety and automatic traffic monitoring (TM) [1]. The TM data are used for valuable information extraction [2], which may include vehicle count [3], [4], shape [5], [6], speed [7], [8], acceleration [9], type [10], [11], plate number [12] and may be used to predict road accidents [13], [14].…”
Section: Introductionmentioning
confidence: 99%