TimeSignatureEstimator/prep.py at main · pianistprogrammer/TimeSignatureEstimator · GitHub

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import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np
from numpy import lib

file = "Rapp.wav"

#waveform
signal, sr = librosa.load(file, sr=22050)
# librosa.display.waveplot(signal, sr = sr)
# plt.xlabel("Time")
# plt.ylabel("Amplitude")
# # plt.show()

#fft -> spectrum
fft = np.fft.fft(signal)
magnitude = np.abs(fft)
frequency = np.linspace(0, sr, len(magnitude))

left_frequency = frequency[:int(len(frequency)/2)]
left_magnitude = magnitude[:int(len(magnitude)/2)]
# plt.plot(left_frequency, left_magnitude)
# plt.xlabel("Frequency")
# plt.ylabel("Magnitude")
# plt.show()

# stft -> spectrogram
n_fft = 2048
hop_len = 512
stft = librosa.core.stft(signal, n_fft=n_fft, hop_length=hop_len)
spectrogram = np.abs(stft)

log_spectrogram = librosa.amplitude_to_db(spectrogram)

# librosa.display.specshow(log_spectrogram, sr=sr, hop_length=hop_len)
# plt.xlabel("Time")
# plt.ylabel("Frequency")
# plt.colorbar()
# plt.show()

#mfccs
mfccs = librosa.feature.mfcc(signal, sr=sr, hop_length=hop_len,  n_mfcc=13)
librosa.display.specshow(mfccs, sr=sr, hop_length=hop_len)
plt.xlabel("Time")
plt.ylabel("MFCC")
plt.colorbar()
plt.show()