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| import matplotlib.pyplot as plt | |
| from scipy import signal | |
| from scipy.io import wavfile | |
| import os | |
| import wavio | |
| import numpy as np | |
| import wave | |
| from scipy.io.wavfile import read as read_wav | |
| import pylab | |
| from numpy.lib import stride_tricks | |
| """ 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 | |
| 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 | |
| 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 | |
| for it. The actual accesing of the files, processing of the wav data, and saving of the images was all pretty simple itself.""" | |
| #short time fourier transform of audio signal | |
| def stft(sig, frameSize, overlapFac=0.5, window=np.hanning, hopFactor=1): | |
| win = np.hamming(frameSize) + 1e-10 | |
| hopSize = int(frameSize - np.floor(overlapFac * frameSize)) * hopFactor | |
| # zeros at beginning (thus center of 1st window should be for sample nr. 0) | |
| samples = np.append(np.zeros(int(np.floor(frameSize/2.0))), sig) | |
| # cols for windowing | |
| cols = np.ceil( (len(samples) - frameSize) / float(hopSize)) + 1 | |
| # zeros at end (thus samples can be fully covered by frames) | |
| samples = np.append(samples, np.zeros(frameSize)) | |
| frames = stride_tricks.as_strided(samples, shape=(int(cols), frameSize), strides=(samples.strides[0]*hopSize, samples.strides[0])).copy() | |
| frames *= win | |
| return np.fft.rfft(frames) | |
| def logscale_spec(spec, sr=44100, factor=20.): | |
| timebins, freqbins = np.shape(spec) | |
| scale = np.linspace(0, 1, freqbins) ** factor | |
| scale *= (freqbins-1)/max(scale) | |
| scale = np.unique(np.round(scale)) | |
| # create spectrogram with new freq bins | |
| newspec = np.complex128(np.zeros([timebins, len(scale)])) | |
| for i in range(0, len(scale)): | |
| if i == len(scale)-1: | |
| newspec[:,i] = np.sum(spec[:,int(scale[i]):], axis=1) | |
| else: | |
| newspec[:,i] = np.sum(spec[:,int(scale[i]):int(scale[i+1])], axis=1) | |
| # list center freq of bins | |
| allfreqs = np.abs(np.fft.fftfreq(freqbins*2, 1./sr)[:freqbins+1]) | |
| freqs = [] | |
| for i in range(0, len(scale)): | |
| if i == len(scale)-1: | |
| freqs += [np.mean(allfreqs[int(scale[i]):])] | |
| else: | |
| freqs += [np.mean(allfreqs[int(scale[i]):int(scale[i+1])])] | |
| return newspec, freqs | |
| folders = ["Pipistrellus pygmaus with social sound", "Noctula nyctalus with noise", "Pipistrellus pygmaus wo social sound", "Noctula nyctalus with out social sound and noise"] | |
| folders1 = ["test"] | |
| folders1 = ["Noctula nyctalus with out social sound and noise"] | |
| def wavToSpectro(folders): | |
| for folder in folders: | |
| for fN in os.listdir(f"/Users/elijahmendoza/OCS_Materials/Neural_Networks/NeuralNetworksProject/{folder}/to crop"): | |
| #print(fN) | |
| fileName = fN[:-4] | |
| if ".wav" in fN: | |
| fileToImport = f"/Users/elijahmendoza/OCS_Materials/Neural_Networks/NeuralNetworksProject/{folder}/to crop/{fileName}.wav" | |
| pngName = f"/Users/elijahmendoza/OCS_Materials/Neural_Networks/NeuralNetworksProject/{folder}/Bar Spectrograms/{fileName}" | |
| samp_rate, samp = wavfile.read(fileToImport) | |
| # our samp is 5_000_000 (for a given clip) | |
| # our samp rate is 500_000 (for a given clip) | |
| # if we divide our samp/samp_rate then we get the length of our clip (in this case 10) | |
| # adjust sample rate | |
| frequencies, times, spectrogram = signal.spectrogram(samp, samp_rate) | |
| binsize = 2**10 | |
| colormap = "jet" | |
| #hopfactor Max: 15 | |
| #hopfactor min: ? | |
| s = stft(samp, binsize, hopFactor=2) | |
| sshow, freq = logscale_spec(s, factor=1, sr=samp_rate) | |
| ims = 20. * np.log10(np.where(np.abs(sshow) < 1e-10, 1e-10, np.abs(sshow))) # amplitude to decibel | |
| timebins, freqbins = np.shape(ims) | |
| plt.figure(figsize=(3.0, 2.0), dpi=100) | |
| plt.imshow(np.transpose(ims), origin="lower", aspect="auto", cmap=colormap, interpolation="bilinear") | |
| #plt.colorbar() | |
| plt.axis('off') # Turn off axis | |
| plt.margins(0, 0) # Set margins to zero | |
| #plt.gca().set_aspect('equal') | |
| #plt.xlabel("time (s)") | |
| #plt.ylabel("frequency (hz)") | |
| plt.xlim([0, timebins-1]) | |
| plt.ylim([3, 250]) | |
| #xlocs = np.float32(np.linspace(0, timebins-1, 5)) | |
| #plt.xticks(xlocs, ["%.02f" % l for l in ((xlocs*len(samp)/timebins)+(0.5*binsize))/samp_rate]) | |
| #ylocs = np.int16(np.round(np.linspace(0, freqbins-1, 10))) | |
| #plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs]) | |
| plt.savefig(pngName, bbox_inches="tight", pad_inches=0.0) | |
| plt.clf() | |
| plt.close() | |
| wavToSpectro(folders) | |