I am trying to extract pitch features from an audio file which I would use for a classification problem. I am using python(scipy/numpy) for classification.
I think I can get frequency features using scipy.fft but I don't know how to approximate musical notes using frequencies. I researched a bit and found that I need to get chroma features which map frequencies to 12 bins for notes of a chromatic scale.
I think there's a chroma toolbox for matlab but I don't think there's anything similiar for python.
How should I go forward with this? Could anyone also suggest reading material I should look into?
You can map frequencies to musical notes:
with
being the midi note number to be calculated, 
the frequency and 
the chamber pitch (in modern music 440.0 Hz is common).
As you may know a single frequency doesn't make a musical pitch. "Pitch" arises from the sensation of the fundamental of harmonic sounds, i.e. sounds that mainly consist of integer multiples of one single frequency (= the fundamental).
If you want to have Chroma Features in Python, you can use the Bregman Audio-Visual Information Toolbox. Note that chroma features don't give you information about the octave of a pitch, so you just get information about the pitch class.
from bregman.suite import Chromagram
audio_file = "mono_file.wav"
F = Chromagram(audio_file, nfft=16384, wfft=8192, nhop=2205)
F.X # all chroma features
F.X[:,0] # one feature
The general problem of extracting pitch information from audio is called pitch detection.
You can try reading the literature on pitch detection, which is quite extensive. Generally autocorrelation-based methods seem to work pretty well; frequency-domain or zero-crossing methods are less robust (so FFT doesn't really help much). A good starting point may be to implement one of these two algorithms:
YAAPT, from: Stephen A. Zahorian and Hongbing Hu, "A spectral-temporal method for robust fundamental frequency tracking", J. Acoust. Soc. Am. 123, 4559 (2008). http://bingweb.binghamton.edu/~hhu1/paper/Zahorian2008spectral.pdf and MATLAB code here: http://ws2.binghamton.edu/zahorian/yaapt.htm
YIN, from: De Cheveigné, A., Kawahara, H. "YIN, a fundamental frequency estimator for speech and music", J. Acoust. Soc. Am. 111, 1917-1930 (2002). http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf
As far as off-the-shelf solutions, check out Aubio, C code with python wrapper, several pitch-extraction algorithms available including YIN and multiple-comb.
If you're willing to use 3rd party libraries (at least as a reference for how other people accomplished this):
Extracting musical information from sound, a presentation from PyCon 2012, shows how to use the AudioNest Python API:
Here is the relevant EchoNest documentation:
- Track API Methods
- Detailed Analyze Documentation
Relevant excerpt:
pitch content is given by a “chroma” vector, corresponding to the 12 pitch classes C, C#, D to B, with values ranging from 0 to 1 that describe the relative dominance of every pitch in the chromatic scale. For example a C Major chord would likely be represented by large values of C, E and G (i.e. classes 0, 4, and 7). Vectors are normalized to 1 by their strongest dimension, therefore noisy sounds are likely represented by values that are all close to 1, while pure tones are described by one value at 1 (the pitch) and others near 0.
EchoNest does the analysis on their servers. They provide free API keys for non-commercial use.
If EchoNest is not an option, I would look at the open-source aubio project. It has python bindings, and you can examine the source to see how they accomplished pitch detection.
来源：https://stackoverflow.com/questions/20730133/extracting-pitch-features-from-audio-file