Upload WavToSpectro.py

WavToSpectro.py

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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)