# madmom: a new Python Audio and Music Signal Processing Library

arXiv: https://arxiv.org/abs/1605.07008 · HTML: https://mlsanity.com/paper/1605.07008/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2016.

## Built on by (most cited first, 30 of 64)

- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs](https://mlsanity.com/paper/2101.02402/) (2021)
- [SingSong: Generating musical accompaniments from singing](https://mlsanity.com/paper/2301.12662/) (2023)
- [MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAE](https://mlsanity.com/paper/2105.04090/) (2021)
- [MARBLE: Music Audio Representation Benchmark for Universal Evaluation](https://mlsanity.com/paper/2306.10548/) (2023)
- [Relative Positional Encoding for Transformers with Linear Complexity](https://mlsanity.com/paper/2105.08399/) (2021)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Video Background Music Generation: Dataset, Method and Evaluation](https://mlsanity.com/paper/2211.11248/) (2022)
- [LLark: A Multimodal Instruction-Following Language Model for Music](https://mlsanity.com/paper/2310.07160/) (2023)
- [Musika! Fast Infinite Waveform Music Generation](https://mlsanity.com/paper/2208.08706/) (2022)
- [Melody transcription via generative pre-training](https://mlsanity.com/paper/2212.01884/) (2022)
- [Content-based Controls For Music Large Language Modeling](https://mlsanity.com/paper/2310.17162/) (2023)
- [Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls](https://mlsanity.com/paper/2402.09508/) (2024)
- [Omnizart: A General Toolbox for Automatic Music Transcription](https://mlsanity.com/paper/2106.00497/) (2021)
- [BeatNet: CRNN and Particle Filtering for Online Joint Beat Downbeat and Meter Tracking](https://mlsanity.com/paper/2108.03576/) (2021)
- [BERT-like Pre-training for Symbolic Piano Music Classification Tasks](https://mlsanity.com/paper/2107.05223/) (2021)
- [JukeDrummer: Conditional Beat-aware Audio-domain Drum Accompaniment Generation via Transformer VQ-VAE](https://mlsanity.com/paper/2210.06007/) (2022)
- [MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music Generation](https://mlsanity.com/paper/2407.15060/) (2024)
- [OpenMU: Your Swiss Army Knife for Music Understanding](https://mlsanity.com/paper/2410.15573/) (2024)
- [MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners](https://mlsanity.com/paper/2506.18729/) (2025)
- [Uncovering audio patterns in music with Nonnegative Tucker Decomposition for structural segmentation](https://mlsanity.com/paper/2104.08580/) (2021)
- [AuTAgent: A Reinforcement Learning Framework for Tool-Augmented Audio Reasoning](https://mlsanity.com/paper/2602.13685/) (2026)
- [CutClaw: Agentic Hours-Long Video Editing via Music Synchronization](https://mlsanity.com/paper/2603.29664/) (2026)
- [Modeling the Compatibility of Stem Tracks to Generate Music Mashups](https://mlsanity.com/paper/2103.14208/) (2021)
- [On the Effectiveness of Speech Self-supervised Learning for Music](https://mlsanity.com/paper/2307.05161/) (2023)
- [FilmComposer: LLM-Driven Music Production for Silent Film Clips](https://mlsanity.com/paper/2503.08147/) (2025)
- [Music Audio-Visual Question Answering Requires Specialized Multimodal Designs](https://mlsanity.com/paper/2505.20638/) (2025)
- [Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks](https://mlsanity.com/paper/2110.06525/) (2021)
- [A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives](https://mlsanity.com/paper/2210.13944/) (2022)
- [MOSA: Music Motion with Semantic Annotation Dataset for Cross-Modal Music Processing](https://mlsanity.com/paper/2406.06375/) (2024)

## Abstract (arXiv)

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.