TY  - EJOU
AU  - Ali, Mudasir 
AU  - Mushtaq, Muhammad Faheem 
AU  - Akram, Urooj 
AU  - Samee, Nagwan Abdel 
AU  - Jamjoom, Mona M. 
AU  - Ashraf, Imran 

TI  - Detecting Vehicle Mechanical Defects Using an Ensemble Deep Learning Model with Mel Frequency Cepstral Coefficients from Acoustic Data
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 145
IS  - 2
SN  - 1526-1506

AB  - Differentiating between regular and abnormal noises in machine-generated sounds is a crucial but difficult problem. For accurate audio signal classification, suitable and efficient techniques are needed, particularly machine learning approaches for automated classification. Due to the dynamic and diverse representative characteristics of audio data, the probability of achieving high classification accuracy is relatively low and requires further research efforts. This study proposes an ensemble model based on the LeNet and hierarchical attention mechanism (HAM) models with MFCC features to enhance the models’ capacity to handle bias. Additionally, CNNs, bidirectional LSTM (BiLSTM), CRNN, LSTM, capsule network model (CNM), attention mechanism (AM), gated recurrent unit (GRU), ResNet, EfficientNet, and HAM models are implemented for performance comparison. Experiments involving the DCASE2020 dataset reveal that the proposed approach works better than the others, achieving an impressive 99.13% accuracy and 99.56% k-fold cross-validation accuracy. Comparison with state-of-the-art studies further validates this performance. The study’s findings highlight the potential of the proposed approach for accurate fault detection in vehicles, particularly involving the use of acoustic data.
KW  - Vehicle defect detection; sound classification; acoustic analysis; deep learning; hybrid model; Mel frequency cepstral coefficients

DO  - 10.32604/cmes.2025.070389