The Sound of Breath and AI
Аннотация
This work presents an innovative extension of the Autoencoder (AE) principle as a universal diagnostic tool, moving beyond the traditional use of dimensionality reduction and algorithmic denoising. The approach is based on the concept of Semantic Denoising (or Similarity Measurement), where the AE, trained on a domain of "Normal" data (healthy sounds or pseudo-spectra), uses the Mean Squared Reconstruction Error (LMSE) as a direct metric of non-conformity. The methodology, previously successfully applied to vibrational spectroscopy (Raman and ATR-IR) for mineral analysis and to industrial Acoustic Anomaly Detection (AAD), is here applied to respiratory sound analysis in the biomedical field. We propose the implementation of a Convolutional AE (CNN-AE), trained exclusively on respiratory cycles labeled as "Normal" (healthy) from the ICBHI 2017 database. A high reconstruction error in the presence of pathological adventitious sounds (Crackles or Wheezes) signals a "semantic error" or pathology, as the signal does not adhere to the learned features of normality. This approach exploits the inherent class imbalance of the ICBHI dataset to the advantage of Anomaly Detection, offering a more interpretable solution capable of detecting even unobserved pathologies, thereby overcoming the limitation of traditional supervised classification.