The constant-Q transform feature(CQT), is often useful for some recognized task, especiall for some task about music. This repo is for c++ code for Efficient solver of CQT and also offer pyWrapper for easily using.
git clone https://github.com/Liu-Feng-deeplearning/FastCqt.git
cd FastCqtcd 3rd
tar -zxf eigen-3.3.7.tar.gz # eigen do not need to install
tar -zxf fftw-3.3.10.tar.gz
./configure --enable-shared --enable-float -prefix=${PWD}/build
make CFLAGS="-fPIC"
make install
cd ../Now we prepared 3rd for FastCqt
mkdir build && cd build && cmake ../
make -jAfter building, we get libFastCqt.so
Besides of using c++ code, we also offer py-wrapper for the cqt feature. Example python code can be seen at src/PyWrapper.py
import librosa, FastCqt
import numpy as np
sig, _ = librosa.load("demo/demo.wav", sr=16000)
sig = np.ascontiguousarray(sig, dtype=np.float32)
cpp_cqt_ext = FastCqt(sample_rate=16000, hop_size=0.04)
cpp_cqt = cpp_cqt_ext.compute_cqt(sig)
print("input length:{}".format(np.shape(sig)))
print("output shape:{}".format(np.shape(cpp_cqt)))we can get result as below:
input length:(1459200,)
output shape:(2280, 96)
Compared with other speech features such as mel, the biggest advantage of the Cqt is that it is very convenient to handle the shift of the pitch. Here is an example of audio "Red high-heeled shoes"(红色高跟鞋 in Chinese) by Yaxun Xiao. We cut of signal with head 30s and compute cqt featrue with hop-size of 0.04, so we get matrix of 96x750(or 750x96). Compare init data and pitch-shifted data(increase by 20%), we can see than time domain pitch-shift equals to frequence domain role alone with freq-axis. And it is useful at some task because we can do data augmentation easily.
This implement is only an approximation algorithm which is to multiply between fft
of signal and transformed matrix. It is fast enough and result is verified by some
different task such as Cover-song-identification(CSI). According to my experience,
set hop_size as 0.04 is suitable for audio with sample-rate of 16k.
This is benchmark for FastCqt and librosa.cqt:
| chunks[s] | FastCqt rtf | librosa.cqt rtf |
|---|---|---|
| 5 | 0.0051 | 0.0107 |
| 15 | 0.0054 | 0.0078 |
| 45 | 0.0054 | 0.0069 |
| 135 | 0.0037 | 0.0045 |
Note: when program is run, we use "task -c 1" to make sure cpu usage is about 100%