libsoni: A Python Toolbox for Sonifying Music Annotations and Feature Representations

Music Information Retrieval (MIR) stands as a dedicated research field focused on advancing methodologies and tools for organizing, analyzing, retrieving, and generating data related to music.Key tasks within MIR include beat tracking, structural analysis, chord recognition, melody extraction, and source separation, just to name a few.These tasks involve extracting musically relevant information from audio recordings, typically accomplished by transforming music signals into feature representations such as spectrograms, chromagrams, or tempograms (Müller, 2015).Furthermore, musically relevant annotations such as beats, chords, keys, or structure boundaries become indispensable for training and evaluating MIR approaches.When evaluating and enhancing MIR systems, it is crucial to thoroughly examine the properties of feature representations and annotations to gain a deeper understanding of algorithmic behavior and the underlying data.In the musical context, alongside conventional data visualization techniques, data sonification techniques are emerging as a promising avenue for providing auditory feedback on extracted features or annotated information.This is particularly advantageous given the finely tuned human perception to subtle variations in frequency and timing within the musical domain.This paper introduces libsoni, an open-source Python toolbox tailored for the sonification of music annotations and feature representations.By employing explicit and easy-to-understand sound synthesis techniques, libsoni offers functionalities for generating and triggering sound events, enabling the sonification of spectral, harmonic, tonal, melodic, and rhythmic aspects.Unlike existing software libraries focused on creative applications of sound generation, libsoni is designed to meet the specific needs of MIR researchers and educators.It aims to simplify the process of music exploration, promoting a more intuitive and efficient approach to data analysis by enabling users to interact with their data in acoustically meaningful ways.As a result, libsoni not only improves the analytical capabilities of music scientists but also opens up new avenues for innovative music analysis and discovery.Furthermore, libsoni provides well-documented and stand-alone functions covering all essential building blocks crucial for both sound generation and sonification, enabling users to efficiently apply and easily extend the methods.Additionally, the toolbox includes educational Jupyter notebooks with illustrative code examples demonstrating the application of sonification and visualization methods to deepen understanding within specific MIR scenarios.