An Acoustic Dataset for Deep Learning-Based Non-Destructive Testing (NDT) of Brick Quality via Mel-Spectrogram Analysis

To determine the structural integrity of clay bricks, vision-based methods face many limitations. Another conventional testing method is to assess the impact sound of bricks using human subjective auditory perception. In this context, we present a comprehensive acoustic dataset of brick impact sounds to advance machine learning-based research in objective non-destructive testing (NDT). First, audio data were collected from three active brick kilns—MTB Brick Field, Amin Brick Field, and Trishal Brick Field. For each recording case, two bricks are struck together at a distance of 20-30 cm, and the resulting sound is recorded with a BOYA BY-M1 Pro II microphone. The dataset’s ground-truth labels were determined using preselected, factory-graded brick batches. The complete dataset contains a total of 1,426 distinct impact audio samples, classified into three quality grades: Grade A (465), Grade B (509), and Grade C (452). Second, the audio samples were converted to mel-spectrograms to accurately capture time-frequency acoustic features. This process included noise reduction and silent segment pruning. Third, machine learning-based classification experiments were conducted to evaluate the dataset’s performance. A classification accuracy of 82.74\% was achieved by applying a random forest classifier to the smoothed time-frequency features. These results indicate that the acoustic features present in brick impact sound can be mathematically separated into distinct quality grades. The presented dataset can serve as a useful basis for developing deep learning-based models and for non-destructive testing (NDT) research aimed at automatically assessing the structural quality of bricks.