# which is the maximum dB range value for a 16bits audio recording. We add
# 96dB in order to get have only positive values in the spectrogram.
s, fs = load('../../data/tropical_forest_morning.wav')
Sxx, tn, fn, ext = spectrogram(s, fs, fcrop=[0,20000], tcrop=[0,60])
Sxx_dB = power2dB(Sxx, db_range=96) + 96
plot2d(Sxx_dB, extent=ext, title='original',
vmin=np.median(Sxx_dB), vmax=np.median(Sxx_dB)+40)
print ("Original sharpness : %2.3f" % sharpness(Sxx_dB))
#%%
# Test different methods to remove stationary background noise
# ------------------------------------------------------------
# Test the function "remove_background"
start = timer()
X1, noise_profile1, _ = remove_background(Sxx_dB)
elapsed_time = timer() - start
print("---- test remove_background -----")
print("duration %2.3f s" % elapsed_time)
print ("sharpness : %2.3f" % sharpness(X1))
plot2d(X1, extent=ext, title='remove_background',
vmin=np.median(X1), vmax=np.median(X1)+40)
#%%
# Test the function "median_equalizer"
start = timer()
X2 = median_equalizer(Sxx)
X2 = power2dB(X2)
elapsed_time = timer() - start
print("---- test median_equalizer -----")
# Start by loading an example audio file. We will remove low frequency ambient noise with a lowpass filter and then compute the spectrogram.
s, fs = sound.load('../../data/rock_savanna.wav')
s_filt = sound.select_bandwidth(s, fs, fcut=100, forder=3, ftype='highpass')
db_max = 70 # used to define the range of the spectrogram
Sxx, tn, fn, ext = sound.spectrogram(s_filt, fs, nperseg=1024, noverlap=512)
Sxx_db = power2dB(Sxx, db_range=db_max) + db_max
plot2d(Sxx_db, **{'extent': ext})
#%%
# 1. Find regions of interest
# ---------------------------
# To find regions of interest in the spectrogram, we will remove stationary background noise and then find isolated sounds using a double threshold method. Small ROIs due to noise in the signal will be removed.
Sxx_db_rmbg, _, _ = sound.remove_background(Sxx_db)
Sxx_db_smooth = sound.smooth(Sxx_db_rmbg, std=1.2)
im_mask = rois.create_mask(im=Sxx_db_smooth,
mode_bin='relative',
bin_std=2,
bin_per=0.25)
im_rois, df_rois = rois.select_rois(im_mask, min_roi=50, max_roi=None)
# Format ROIs and visualize the bounding box on the audio spectrogram.
df_rois = format_features(df_rois, tn, fn)
ax0, fig0 = overlay_rois(Sxx_db, df_rois, **{
'vmin': 0,
'vmax': 60,
'extent': ext
})