AudioInsight: Online Exploration of Large Audio Datasets for Musical Acoustics

New perspectives in correlating physical and perceptual characteristics of musical instruments have emerged through recent advancements in machine learning and high-performance computing. Driving such correlating processes, for instance through training deep learning models, requires the assembly of large audio datasets and the development of tools for Exploratory Data Analysis (EDA). This article presents AudioInsight, a web application for the audio-visual exploration of sound datasets for musical acoustics. This application is a by-product of our research on drumhead acoustics, which aims at the computational inference of damping strategies resulting in a desired sound texture. Developed in Python and Dash-Plotly, AudioInsight features an interactive and responsive user interface providing tools for dataset exploration, statistical analysis and visualization of parameter distributions and relevant correlations. Moreover, AudioInsight permits generating dataset clusters and exploring sounds by interactively navigating within their 2D or 3D graphic representations. Future enhancements of this application, allowing musicians and researchers to contribute to musical instrument datasets through crowdsourcing, may serve as an example application within the Internet of Sounds (IoS) ecosystem.