Acoustic Vehicle Classification Using Mel-Frequency Features with Long Short-Term Memory Neural Networks

Yassin, Ahmad Ihsan; Mohd Shariff, Khairul Khaizi; Kechik, Mustapha Awang; Ali, Adli Md; Megat Amin, Megat Syahirul

doi:10.18421/tem123-29

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…The effectiveness of noise signal type recognition is assessed using the accuracy metric R. Prior to computing the accuracy rate, the recognized noise signals need to be manually labeled with their corresponding frame noise signal types. The accuracy rate, R, for each noise type recognition can be computed using Equation (7).…”

Section: Experimental Paradigmmentioning

confidence: 99%

“…As computational power has advanced and the volume of audio feature data has expanded significantly, recognition models rooted in machine learning and deep learning have become indispensable for audio signal recognition [2]. These models encompass a range of approaches, including convolutional neural networks (CNNs) [3][4][5][6], recurrent neural networks (RNNs) [7], convolutional recurrent neural networks (CRNNs), randomized learning [8], deep convolutional neural networks (DCNNs) [9], support vector machines (SVMs) [4], Gaussian mixture models (GMMs), deep attention networks [10], transfer learning [9], and ensemble learning [11], among others. These methods can be applied independently or in combination to enhance the performance of audio category recognition.…”

Section: Introductionmentioning

confidence: 99%

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An Investigation of ECAPA-TDNN Audio Type Recognition Method Based on Mel Acoustic Spectrograms

Wang,

Han

et al. 2023

Electronics

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Audio signals play a crucial role in our perception of our surroundings. People rely on sound to assess motion, distance, direction, and environmental conditions, aiding in danger avoidance and decision making. However, in real-world environments, during the acquisition and transmission of audio signals, we often encounter various types of noises that interfere with the intended signals. As a result, the essential features of audio signals become significantly obscured. Under the interference of strong noise, identifying noise segments or sound segments, and distinguishing audio types becomes pivotal for detecting specific events and sound patterns or isolating abnormal sounds. This study analyzes the characteristics of Mel’s acoustic spectrogram, explores the application of the deep learning ECAPA-TDNN method for audio type recognition, and substantiates its effectiveness through experiments. Ultimately, the experimental results demonstrate that the deep learning ECAPA-TDNN method for audio type recognition, utilizing Mel’s acoustic spectrogram as features, achieves a notably high recognition accuracy.

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Section: Experimental Paradigmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Investigation of ECAPA-TDNN Audio Type Recognition Method Based on Mel Acoustic Spectrograms

Wang,

Han

et al. 2023

Electronics

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Acoustic Vehicle Classification using Deep Learning Trained on a Spectrogram and Scalogram Fusion

Mohd Shariff,

Raju,

Yassin

et al. 2024

Preprint

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This paper explores an audio-based on-road vehicle classification method that utilizes visual representations of sound through spectrograms, scalograms, and their fusion as features, classified using a modified VGG16 Convolutional Neural Network (CNN) architecture. The proposed method offers a non-intrusive, potentially less costly, and environmentally adaptable alternative to traditional sensor-based and computer vision techniques. Our results indicate that the fusion of scalogram and spectrogram features provides enhanced accuracy and reliability in distinguishing between vehicle types. Performance metrics such as training and loss, alongside precision and recall of classes, support the efficacy of a richer feature set in improving classification outcomes. The fusion features demonstrate a marked improvement in distinguishing closely related vehicle classes like 'Cars' and 'Trucks'. These findings underline the potential of our approach in refining and expanding vehicle classification systems for intelligent traffic monitoring and management.

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Prediction of Operational Noise Uncertainty in Automotive Micro-Motors Based on Multi-Branch Channel–Spatial Adaptive Weighting Strategy

Hu,

Deng,

Yan

et al. 2024

Electronics

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The acoustic performance of automotive micro-motors directly impacts the comfort and driving experience of both drivers and passengers. However, various motor production and testing uncertainties can lead to noise fluctuations during operation. Thus, predicting the operational noise range of motors on the production line in advance becomes crucial for timely adjustments to production parameters and process optimization. This paper introduces a prediction model based on a Multi-Branch Channel–Spatial Adaptive Weighting Strategy (MCSAWS). The model includes a multi-branch feature extraction (MFE) network and a channel–spatial attention module (CSAM). It uses the vibration and noise data from micro-motors’ idle operations on the production line as input to efficiently predict the operational noise uncertainty interval of automotive micro-motors. The model employs the VAE-GAN approach for data augmentation (DA) and uses Gammatone filters to emphasize the noise at the commutation frequency of the motor. The model was compared with Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs). Experimental results demonstrate that the MCSAWS method is superior to conventional methods in prediction accuracy and reliability, confirming the feasibility of the proposed approach. This research can help control noise uncertainty in micro-motors’ production and manufacturing processes in advance.

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Acoustic Vehicle Classification Using Mel-Frequency Features with Long Short-Term Memory Neural Networks

Cited by 3 publications

References 20 publications

An Investigation of ECAPA-TDNN Audio Type Recognition Method Based on Mel Acoustic Spectrograms

An Investigation of ECAPA-TDNN Audio Type Recognition Method Based on Mel Acoustic Spectrograms

Acoustic Vehicle Classification using Deep Learning Trained on a Spectrogram and Scalogram Fusion

Prediction of Operational Noise Uncertainty in Automotive Micro-Motors Based on Multi-Branch Channel–Spatial Adaptive Weighting Strategy

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