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A recursive Fourier transform (FT) structure that suppresses convolutional noise.

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multi-layered-cepstrum

Source code of the paper Multi-Layered Cepstrum for Instantaneous Frequency Estimation accepted at GlobalSIP 2018.

Requirements

  • Numpy
  • Scipy
  • Torch (optional for faster performance)
  • Librosa (file IO)
  • Matplotlib (visualization)
  • Pypianoroll (visualization)

Quick Start

  1. Donwload bach10 dataset.
  2. Run with default parameters.
python bach10.py your/download/path/01-AchGottundHerr/01-AchGottundHerr.wav \
       --f0_file your/download/path/01-AchGottundHerr/01-AchGottundHerr-GTF0s.mat
.
.
.
Time cost: 5.6769 seconds
Precision: 0.7661, Recall: 0.9170, F-score:, 0.8348

Torch

We also implement a faster version using PyTorch as torch_bach10.py, and it can run roughly 2 times faster on CPU. Add --cuda can further utilize computational resources on GPU.

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A recursive Fourier transform (FT) structure that suppresses convolutional noise.

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