madmom: a new Python Audio and Music Signal Processing Library

TL;DR

madmom is an open-source Python library that simplifies audio and music signal processing, offering high-level features, machine learning integration, and state-of-the-art MIR algorithms for rapid prototyping and reproducible research.

Contribution

It introduces a modular, object-oriented Python library with efficient processing pipelines, high-level features, and state-of-the-art algorithms for music information retrieval tasks.

Findings

Provides state-of-the-art algorithms for onset detection, beat and meter tracking, and tempo estimation.

Enables fast prototyping with multiprocessing and serialization features.

Supports high-level musically meaningful features with machine learning techniques.

Abstract

In this paper, we present madmom, an open-source audio processing and music information retrieval (MIR) library written in Python. madmom features a concise, NumPy-compatible, object oriented design with simple calling conventions and sensible default values for all parameters, which facilitates fast prototyping of MIR applications. Prototypes can be seamlessly converted into callable processing pipelines through madmom's concept of Processors, callable objects that run transparently on multiple cores. Processors can also be serialised, saved, and re-run to allow results to be easily reproduced anywhere. Apart from low-level audio processing, madmom puts emphasis on musically meaningful high-level features. Many of these incorporate machine learning techniques and madmom provides a module that implements some in MIR commonly used methods such as hidden Markov models and neural networks. Additionally, madmom comes with several state-of-the-art MIR algorithms for onset detection, beat, downbeat and meter tracking, tempo estimation, and piano transcription. These can easily be incorporated into bigger MIR systems or run as stand-alone programs.