Sound-AI: A Pedagogical Tool for Exploring AI in Audio and Bioacoustic Research - Azeem, Muhammad; Nguyen, Hoang D.; Minghim, Rosane (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Artificial intelligence offers powerful methods for audio processing and analysis. Still, complex workflows and the required programming skills often limit access for students and domain experts, such as marine bioacousticians and soundscape ecologists. We present "AI EcoSound Tutor", a code-free and interactive tool that lowers these barriers by allowing users to construct and explore a complete AI pipeline for audio data analysis. Starting from raw recordings, users can choose from various feature extraction techniques (MFCC, OpenL3), apply dimensionality reduction methods (PCA, t-SNE, UMAP), and optionally perform unsupervised clustering (K-Means, GMM,HDBSCAN). The results are displayed with an interactive 2D visualisation where the user can compare multiple plots by employing various techniques, including PCA and t-SNE. Interactive plots enable the selection of points or clusters of interest, allowing exploration of spectrograms within the desired frequency range, and playing an audio clip corresponding to the selected points. An integrated "Help" feature provides explanations of each method (i.e., what it is, how it works, and its practical use in different domains, such as bioacoustics), fostering both conceptual understanding and useful skill acquisition as learning outcomes. For precomputed features or embeddings, this tool also supports training and evaluating a variety of machine learning models, providing visual feedback on the results. By merging accessibility, interactivity, pedagogy, and domain relevance, our application demystifies AI methods for interdisciplinary education and supporting research in audio analysis.