Mudasir Ali1, Muhammad Faheem Mushtaq2, Urooj Akram2, Nagwan Abdel Samee3,*, Mona M. Jamjoom4, Imran Ashraf5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1863-1901, 2025, DOI:10.32604/cmes.2025.070389
- 26 November 2025
Abstract 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 More >
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