minispec¶

minispec is a minimal module for computing audio spectrograms.

Getting started¶

Installation instructions¶

pypi¶

The simplest way to install minispec is through the Python Package Index (PyPI). This will ensure that all required dependencies are fulfilled. This can be achieved by executing the following command:

pip install minispec

or:

sudo pip install minispec

to install system-wide, or:

pip install -u minispec

to install just for your own user.

Source¶

If you’ve downloaded the archive manually from the releases page, you can install using the setuptools script:

tar xzf minispec-VERSION.tar.gz
cd minispec-VERSION/
python setup.py install

If you intend to develop minispec or make changes to the source code, you can install with pip install -e to link to your actively developed source tree:

tar xzf minispec-VERSION.tar.gz
cd minispec-VERSION/
pip install -e .

Alternately, the latest development version can be installed via pip:

pip install git+https://github.com/minispec/minispecbrew install ffmpeg` or get a binary version from their website https://www.ffmpeg.org.

Troubleshooting¶

If you have questions about how to use minispec, please consult the discussion forum. For bug reports and other, more technical issues, consult the github issues.

API documentation¶

Advanced topics¶

Reference¶

Changelog¶

v0.1.1¶

2019-02-28

Merge minispec with librosa v0.6.3

v0.1.0¶

2018-11-29

Initial release.

Glossary¶

time series: Typically an audio signal, denoted by y, and represented as a one-dimensional numpy.ndarray of floating-point values. y[t] corresponds to amplitude of the waveform at sample t.
sampling rate: The (positive integer) number of samples per second of a time series. This is denoted by an integer variable sr.
frame: A short slice of a time series used for analysis purposes. This usually corresponds to a single column of a spectrogram matrix.
window: A vector or function used to weight samples within a frame when computing a spectrogram.
frame length: The (positive integer) number of samples in an analysis window (or frame). This is denoted by an integer variable n_fft.
hop length: The number of samples between successive frames, e.g., the columns of a spectrogram. This is denoted as a positive integer hop_length.
window length: The length (width) of the window function (e.g., Hann window). Note that this can be smaller than the frame length used in a short-time Fourier transform. Typically denoted as a positive integer variable win_length.
spectrogram: A matrix S where the rows index frequency bins, and the columns index frames (time). Spectrograms can be either real-valued or complex-valued. By convention, real-valued spectrograms are denoted as numpy.ndarrays S, while complex-valued STFT matrices are denoted as D.
onset (strength) envelope: An onset envelope onset_env[t] measures the strength of note onsets at frame t. Typically stored as a one-dimensional numpy.ndarray of floating-point values onset_envelope.
chroma: Also known as pitch class profile (PCP). Chroma representations measure the amount of relative energy in each pitch class (e.g., the 12 notes in the chromatic scale) at a given frame/time.

Index