/CQT_toolbox_python

Constant-Q Transform Toolbox for Python/MATLAB

Primary LanguageMMIT LicenseMIT

Constant-Q Transform Toolbox for Python/MATLAB

Introduction

A Python/MATLAB reference implementation of a computationally efficient method for computing the constant-Q transform (CQT) of a time-domain signal.

Note: I just translate the core original MATLAB codes (/MATLAB/*.m) to Python version (/CQT.py) with following functions:

  • Core:

    • cqt
    • icqt
    • genCQTkernel
    • getCQT
    • cell2sparse
    • sparse2cell
    • plotCQT

  • Extra bonus:

    • buffer
    • upsample
    • round_half_up
    • nextpow2
    • hann

See the authors' homepage for more information and MATLAB packaged downloads:

Or you can read my blog post (Chinese) for inspriation:

Related publications

C. Schörkhuber and A. Klapuri, “Constant-Q transform toolbox for music processing,” in Proceedings of the 7th Sound and Music Computing Conference, Barcelona, Spain, 2010. PDF or Constant-Q_transform_toolbox_for_music_processing.pdf

Requirements

  • Python 3.6+
  • Numpy
  • Scipy
  • Matplotlib

Demo

Note: It might not be as efficient than the original MATLAB version, partly because the sparse property have yet to be fully utilised in this Python version.

from CQT import *
fname = './demo.dat'
data = np.loadtxt(fname)
t, hp, hc = data[:,0], data[:,1], data[:,2]

fs = 1/(t[1]-t[0])
print('fs =', fs)

bins_per_octave = 24
fmax = 400
fmin = 20

Xcqt = cqt(hp, fmin, fmax, bins_per_octave, fs,)
_ = plotCQT(Xcqt, fs, 0.6)

y = icqt(Xcqt)

License

MIT