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:

• https://code.soundsoftware.ac.uk/projects/constant-q-toolbox

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

• 恒 Q 变换 (Constant-Q transform)

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