Engineering and Technology Horizons | 2026
Authors: Mattanaweerapong T.; Jearanaitanakij K.
DOI: 10.55003/ETH.430107
Journal: Engineering and Technology Horizons
Year: 2026
Publisher: King Mongkut's Institute of Technology Ladkrabang
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
This study proposes a hybrid feature extraction approach that integrates the Discrete Wavelet Transform (DWT) with the Short-Time Fourier Transform (STFT) to improve the accuracy of anomalous sound detection in industrial machines. Conventional STFT-based methods, while effective in representing time–frequency characteristics, exhibit limitations in handling non-stationary noise and transient variations, which often lead to reduced anomaly detection performance in practical industrial environments. To address this problem, the proposed method incorporates multiresolution analysis through DWT, enhancing the system’s capability to capture both spectral and temporal information with improved noise robustness. The MIMII dataset (valve,-6 dB, ID02) was used to evaluate the model, where the DWT–STFT feature, representation was applied to an autoencoder for unsupervised anomaly detection. Experimental results demonstrate that the integration of DWT effectively enhanced noise robustness and improved classification metrics, achieving higher AUC and F1-scores compared to the baseline STFT-based approach. In conclusion, the proposed DWT–STFT fusion provides a more resilient and discriminative feature representation, making it a promising technique for practical industrial anomaly detection systems. © 2026, King Mongkut's Institute of Technology Ladkrabang. All rights reserved.
Autoencoder; Discrete Wavelet Transform (DWT); Machine Sound Anomaly Detection; Unsupervised Learning