madmom: A New Python Audio and Music Signal Processing Library

By Sebastian Böck, Filip Korzeniowski, Jan Schlüter, Florian Krebs, Gerhard Widmer. ACM Multimedia, 2016

317 citations recorded by the source corpus.

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 methods commonly used in MIR 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 chord recognition. These can easily be incorporated into bigger MIR systems or run as stand-alone programs.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0e67c26e0868ec4cf1746f20e7fcda4e9f82f8c1

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Citations in the corpus

Training general-purpose audio tagging networks with noisy labels and iterative self-verification (2018, methodology): “Finally it is post-processed with a logarithmic filter-bank again resulting in 128 frequency bins [18].”
Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions (2020, methodology): “ructure. In achieving all these, the proposed framework for automatic music composition has to integrate audio- and symbolic-domain musicinformationretrieval(MIR) techniques such as downbeat tracking [2, 3, 16] and chord recognition [17, 35], which differentiates it from existing approaches. Moreove”
Musika! Fast Infinite Waveform Music Generation (2022, unknown): “For the conditional model, we apply the CNN-based onset detector [40] of the madmom Python library [41] to all audio ﬁles in the dataset.”
Relative Positional Encoding for Transformers with Linear Complexity (2021, unknown): “Hsiao et al. further employed Onsets and Frames piano transcription (Hawthorne et al., 2018), madmom beat tracking tool (B¨ock et al., 2016), and chorder rule-based chord detection 10 to transcribe the audio into MIDI format with tempo, beat, and chord information.”
Open-Source Practices for Music Signal Processing Research: Recommendations for Transparent, Sustainable, and Reproducible Audio Research (2019, background): “Many open source libraries exist for audio feature extraction, such as Essentia [23], librosa [24], aubio [25], Madmom [26], or Marsyas [27].”