Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

aubio is a collection of tools for music and audio analysis.

This package integrates the aubio library with NumPy to provide a set of efficient tools to process and analyse audio signals, including:

  • read audio from any media file, including videos and remote streams
  • high quality phase vocoder, spectral filterbanks, and linear filters
  • Mel-Frequency Cepstrum Coefficients and standard spectral descriptors
  • detection of note attacks (onset)
  • pitch tracking (fundamental frequency estimation)
  • beat detection and tempo tracking

aubio works with both Python 2 and Python 3.

Links

Demos

Some examples are available in the python/demos folder. Each script is a command line program which accepts one ore more argument.

Notes: installing additional modules is required to run some of the demos.

Analysis

  • demo_source.py uses aubio to read audio samples from media files
  • demo_onset_plot.py detects attacks in a sound file and plots the results using matplotlib
  • demo_pitch.py looks for fundamental frequency in a sound file and plots the results using matplotlib
  • demo_spectrogram.py, demo_specdesc.py, demo_mfcc.py for spectral analysis.

Real-time

Others

  • demo_timestretch.py can change the duration of an input file and write the new sound to disk,
  • demo_wav2midi.py detects the notes in a file and uses mido to write the results into a MIDI file

Example

Use demo_timestretch_online.py to slow down loop.wav, write the results in stretched_loop.wav:

$ python demo_timestretch_online.py loop.wav stretched_loop.wav 0.92

Built with

The core of aubio is written in C for portability and speed. In addition to NumPy, aubio can be optionally built to use one or more of the following libraries:

  • media file reading:

    • ffmpeg / avcodec to decode and read audio from almost any format,
    • libsndfile to read audio from uncompressed sound files,
    • libsamplerate to re-sample audio signals,
    • CoreAudio to read all media formats supported by macOS, iOS, and tvOS.
  • hardware acceleration:

    • Atlas and Blas, for accelerated vector and matrix computations,
    • fftw3, to compute fast Fourier Transforms of any size,
    • Accelerate for accelerated FFT and matrix computations (macOS/iOS),
    • Intel IPP, accelerated vector computation and FFT implementation.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

aubio_muse-0.5.0a0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (403.5 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

File details

Details for the file aubio_muse-0.5.0a0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for aubio_muse-0.5.0a0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 d1e871e5cd360a1332d21849fdbcc4a0eefe798367ed8168bcf3a015804082f6
MD5 1805b2d6306fa9870fc26d2509eccae9
BLAKE2b-256 9ca1c613236e853da3ad213321bb1c993567ef25f7cb1217539f3ec268eb23a5

See more details on using hashes here.

Release history Release notifications | RSS feed

This release

0.5.0a0 This release

1 file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page