Testing YIN in on constant pitch signals

YinFFT_stationary_signal_test.py

from __future__ import division
import numpy as np
import essentia.standard
import matplotlib.pyplot as plt


def sinewave(f0, nseconds, samplerate=44100):
    t = np.arange(1, samplerate * nseconds)
    sig = np.zeros_like(t)
    return sig + 0.5 * np.sin(2 * np.pi * f0 * t / samplerate)


def pitch(signal, samplerate=44100, framesize=2048, hopsize=512):
        audio = essentia.array(signal)
        spectrum = essentia.standard.Spectrum()
        pitchEstimate = essentia.standard.PitchYinFFT(
            sampleRate=samplerate,
            frameSize=framesize
        )

        w = essentia.standard.Windowing(type='hann')

        pitch = []

        for frame in essentia.standard.FrameGenerator(audio, frameSize=framesize, hopSize=hopsize, startFromZero=True):
            spec = spectrum(w(frame))
            p, confidence = pitchEstimate(spec)
            pitch.append(p)

        # essentia array to np
        return np.array(pitch)


if __name__ == "__main__":
    frange = np.arange(100, 2000, .5)
    e = np.zeros(len(frange))
    for i, f in enumerate(frange):
        estimate = pitch(sinewave(f, 5))
        ground_truth = np.ones(len(estimate)) * f
        error = np.mean(ground_truth - estimate)
        print "Error: %f, Freq %f" % (error, f)
        e[i] = error

    plt.plot(frange, e)
    plt.axhline(0, linewidth=3, color='r')
    plt.xlabel('Frequency (Hz)')
    plt.ylabel('Error (Hz)')
    plt.show()