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spafe.frequencies.dominant_frequencies#

Description : Implementation Dominant Frequency Extraction Using the YIN-Algorithm.
Copyright (c) 2019-2024 Ayoub Malek. This source code is licensed under the terms of the BSD 3-Clause License. For a copy, see <https://github.com/SuperKogito/spafe/blob/master/LICENSE>.

spafe.frequencies.dominant_frequencies.get_dominant_frequencies(sig: ndarray, fs: int, butter_filter: bool = False, lower_cutoff: float = 50, upper_cutoff: float = 3000, nfft: int = 512, win_len: float = 0.025, win_hop: float = 0.01, win_type: typing_extensions.Literal[hanning, bartlet, kaiser, blackman, hamming] = 'hamming', only_positive: bool = True) → ndarray[source]#

Returns a list of dominant audio frequencies of a given wave file based on [Rastislav] and [Luca].

Parameters

sig (numpy.ndarray) – a mono audio signal (Nx1) from which to compute features.
fs (int) – sampling rate (= average number of samples pro 1 sec)
butter_filter (bool) – choose whether to apply a Butterworth filter or not. (Default is False).
lower_cutoff (float) – filter lower cut-off frequency. (Default is 50).
upper_cutoff (float) – filter upper cot-off frequency. (Default is 3000).
nfft (int) – number of FFT points. (Default is 512).
win_len (float) – window length in sec. (Default is 0.025).
win_hop (float) – step between successive windows in sec. (Default is 0.01).
win_type (str) – window type to apply for the windowing. (Default is “hamming”).
only_positive (bool) – if True then returns only positive frequncies. (Default is True).

Returns

array of dominant frequencies.

Return type

(numpy.ndarray)

References

Rastislav: : Rastislav T. (2013). Dominant Frequency Extraction. CoRR, abs/1306.0103.
Luca: : Luca, The exact definition of dominant frequency? https://dsp.stackexchange.com/a/40183/37123

Examples

import numpy as np
import matplotlib.pyplot as plt
from scipy.io.wavfile import read
from spafe.frequencies.dominant_frequencies import get_dominant_frequencies

# init vars
nfft = 512
win_len = 0.020
win_hop = 0.010

# read audio
fpath = "../../../tests/data/test.wav"
fs, sig = read(fpath)

# compute dominant frequencies
dominant_frequencies = get_dominant_frequencies(sig,
                                                fs,
                                                butter_filter=False,
                                                lower_cutoff=0,
                                                upper_cutoff=fs/2,
                                                nfft=nfft,
                                                win_len=win_len,
                                                win_hop=win_hop,
                                                win_type="hamming")

# compute FFT, Magnitude, Power spectra
fourrier_transform = np.absolute(np.fft.fft(sig, nfft))
magnitude_spectrum = fourrier_transform[:int(nfft / 2) + 1]
power_spectrum = (1.0 / nfft) * np.square(fourrier_transform)
power_spectrum = 20*np.log10(power_spectrum)
freqs = np.fft.rfftfreq(power_spectrum.size, 1/fs)
idx = np.argsort(freqs)

# plot
fmin = 500
fmax = 1500

y = power_spectrum
x = freqs
idx = np.argsort(freqs)

plt.figure(figsize=(14, 4))
plt.plot(x[idx], y[idx], "g")
plt.axis((fmin-10, fmax+10, 0, max(y)*(1.1)))

for i, dom_freq in enumerate(np.unique(dominant_frequencies)):
    if  (fmin < dom_freq < fmax):
        plt.vlines(x=dom_freq, ymin=0, ymax=max(y), colors="red", linestyles=":")
        plt.text(dom_freq, max(y) , "({:.1f})".format(dom_freq))

plt.grid()
plt.title("Dominant frequencies (Hz)")
plt.xlabel("Frequency (Hz)")
plt.ylabel("Spectrum amplitude")
plt.show()

spafe.frequencies

spafe.frequencies.fundamental_frequencies