muda
A Python library for annotation-aware musical data augmentation to improve statistical model training.
What is muda?
muda is a Python library for musical data augmentation that automatically synchronizes audio transformations with their corresponding annotations. It solves the problem of generating augmented training data for music machine learning models while preserving time-aligned metadata like labels, beats, or segment boundaries. This ensures that augmented datasets remain consistent and valid for model training and evaluation in music information retrieval tasks.
Researchers and practitioners in music information retrieval (MIR) and audio machine learning who need to augment annotated music datasets for training robust statistical models. It is particularly useful for those working on tasks like beat tracking, chord recognition, or onset detection where label alignment is critical.
Developers choose muda because it provides a structured, annotation-aware framework specifically designed for music data, unlike generic audio augmentation tools. Its unique selling point is the automatic propagation of transformations to annotations, ensuring data integrity and saving manual effort in re-annotating augmented audio.
Overview
A library for augmenting annotated audio data
Use Cases
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