Can We Trust AI With Our Ears? A Cross-Domain Comparative Analysis of Explainability in Audio Intelligence

The rapid growth of deep learning has led to major successes in audio classification, but the “opaque” nature of these models slows down their use in important areas such as healthcare where trust is critical. This paper addresses this problem through a detailed study on the use of Explainable AI (XAI) for audio-based systems. Six distinct datasets are used to evaluate a consistent deep learning model, including speech emotion recognition, environmental sound classification, and healthcare (heart, lung, and cough sounds). The study analyzes both audio-only models and multimodal models that combine audio features (spectrograms and MFCCs) with demographic data. A set of three distinct XAI techniques, namely LIME, SHAP, and Grad-CAM, is used to explain how the models make their predictions. Our key findings show that XAI methods are very useful for checking how the models work, confirming that they learn to focus on clinically and acoustically relevant features in the audio signals. The analysis also shows the power of multimodal explanations, where tools like SHAP and LIME can trace a prediction back to both acoustic patterns and specific demographic data, showing how the model creates a complete, patient-specific picture. The main contribution of this work is a broad, comparative study that provides a more complete understanding of explainability in the audio domain. The results confirm that XAI is an important part of the entire process of building a model, helping with validation, debugging, and the creation of more reliable and human-aligned AI systems for real-world use.