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import matplotlib.pyplot as plt |
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from scipy import signal |
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from scipy.io import wavfile |
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import os |
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import wavio |
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import numpy as np |
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import wave |
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from scipy.io.wavfile import read as read_wav |
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import pylab |
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from numpy.lib import stride_tricks |
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""" NOTE: While a lot of this was self authored (lines 60-89), the spectrogram images I was producing were just not the correct colors. I couldn't find a way to make the |
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contrast between the noise caught by the microphone and the background more visible. The code between lines 16-30, 32-57, and 91-112 was made following this stack overflow |
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post https://stackoverflow.com/questions/44787437/how-to-convert-a-wav-file-to-a-spectrogram-in-python3. All it really is the template for the graph and the correct coloring |
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for it. The actual accesing of the files, processing of the wav data, and saving of the images was all pretty simple itself.""" |
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def stft(sig, frameSize, overlapFac=0.5, window=np.hanning, hopFactor=1): |
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win = np.hamming(frameSize) + 1e-10 |
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hopSize = int(frameSize - np.floor(overlapFac * frameSize)) * hopFactor |
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samples = np.append(np.zeros(int(np.floor(frameSize/2.0))), sig) |
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cols = np.ceil( (len(samples) - frameSize) / float(hopSize)) + 1 |
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samples = np.append(samples, np.zeros(frameSize)) |
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frames = stride_tricks.as_strided(samples, shape=(int(cols), frameSize), strides=(samples.strides[0]*hopSize, samples.strides[0])).copy() |
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frames *= win |
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return np.fft.rfft(frames) |
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def logscale_spec(spec, sr=44100, factor=20.): |
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timebins, freqbins = np.shape(spec) |
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scale = np.linspace(0, 1, freqbins) ** factor |
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scale *= (freqbins-1)/max(scale) |
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scale = np.unique(np.round(scale)) |
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newspec = np.complex128(np.zeros([timebins, len(scale)])) |
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for i in range(0, len(scale)): |
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if i == len(scale)-1: |
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newspec[:,i] = np.sum(spec[:,int(scale[i]):], axis=1) |
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else: |
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newspec[:,i] = np.sum(spec[:,int(scale[i]):int(scale[i+1])], axis=1) |
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allfreqs = np.abs(np.fft.fftfreq(freqbins*2, 1./sr)[:freqbins+1]) |
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freqs = [] |
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for i in range(0, len(scale)): |
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if i == len(scale)-1: |
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freqs += [np.mean(allfreqs[int(scale[i]):])] |
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else: |
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freqs += [np.mean(allfreqs[int(scale[i]):int(scale[i+1])])] |
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return newspec, freqs |
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folders = ["Pipistrellus pygmaus with social sound", "Noctula nyctalus with noise", "Pipistrellus pygmaus wo social sound", "Noctula nyctalus with out social sound and noise"] |
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folders1 = ["test"] |
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folders1 = ["Noctula nyctalus with out social sound and noise"] |
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def wavToSpectro(folders): |
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for folder in folders: |
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for fN in os.listdir(f"/Users/elijahmendoza/OCS_Materials/Neural_Networks/NeuralNetworksProject/{folder}/to crop"): |
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fileName = fN[:-4] |
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if ".wav" in fN: |
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fileToImport = f"/Users/elijahmendoza/OCS_Materials/Neural_Networks/NeuralNetworksProject/{folder}/to crop/{fileName}.wav" |
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pngName = f"/Users/elijahmendoza/OCS_Materials/Neural_Networks/NeuralNetworksProject/{folder}/Bar Spectrograms/{fileName}" |
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samp_rate, samp = wavfile.read(fileToImport) |
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frequencies, times, spectrogram = signal.spectrogram(samp, samp_rate) |
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binsize = 2**10 |
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colormap = "jet" |
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s = stft(samp, binsize, hopFactor=2) |
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sshow, freq = logscale_spec(s, factor=1, sr=samp_rate) |
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ims = 20. * np.log10(np.where(np.abs(sshow) < 1e-10, 1e-10, np.abs(sshow))) |
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timebins, freqbins = np.shape(ims) |
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plt.figure(figsize=(3.0, 2.0), dpi=100) |
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plt.imshow(np.transpose(ims), origin="lower", aspect="auto", cmap=colormap, interpolation="bilinear") |
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plt.axis('off') |
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plt.margins(0, 0) |
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plt.xlim([0, timebins-1]) |
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plt.ylim([3, 250]) |
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plt.savefig(pngName, bbox_inches="tight", pad_inches=0.0) |
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plt.clf() |
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plt.close() |
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wavToSpectro(folders) |
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