Improvement of Natural Frequency Prediction from Spectrogram of Bone-Conducted Sound by Data Augmentation

In this study, the effect of data augmentation on machine learning (ML) models that predict the natural frequency of human long bones from the spectrogram of bone-conducted sound was investigated. Bone-conducted sound was recorded from the tibia of 45 healthy university students using a custom-developed hammering device. Short-Time Fourier Transform (STFT) was applied to the recorded sounds to generate spectrograms, from which six ML models were examined to predict the natural frequency. Ground-truth natural frequency was defined as peak values identified from Fast Fourier Transform (FFT) spectra obtained by recorded waveforms of sounds. Data augmentation was performed by shifting bone-conducted sound waveforms, expanded to 5 levels, i.e., 3-fold (3×) to 11-fold (11×). All models showed improved accuracy by data augmentations compared to the original dataset, with a particularly substantial improvement observed in many models when the dataset was expanded from the original to the 3× level. Among the ML models examined here, DenseNet-121 with 11× data augmentation achieved the lowest MAE and the highest R2 (10.00 ± 2.27 Hz; R2 = 0.927 ± 0.046), followed by PeleeNet, which is constructed on a lightweight DenseNet architecture, with 7× data augmentation (MAE = 10.37 ± 2.91 Hz, R2 = 0.920 ± 0.059).