mode_bin='relative',
bin_std=8,
bin_per=0.5,
verbose=False,
display=False)
# Finaly, we put together pixels that belong to the same acoustic event, and
# remove very small events (<=25 pixel²)
im_rois, df_rois = rois.select_rois(im_mask,
min_roi=25,
max_roi=None,
display=True,
**{'extent': ext})
# format dataframe df_rois in order to convert pixels into time and frequency
df_rois = format_features(df_rois, tn, fn)
# overlay bounding box on the original spectrogram
ax0, fig0 = rois.overlay_rois(Sxx_db, df_rois, **{
'vmin': 0,
'vmax': dB_max,
'extent': ext
})
# Compute and visualize centroids
df_centroid = features.centroid_features(Sxx_db, df_rois, im_rois)
df_centroid = format_features(df_centroid, tn, fn)
ax0, fig0 = features.overlay_centroid(Sxx_db,
df_centroid,
savefig=None,
**{
from maad.util import read_audacity_annot, linear_scale, format_features, get_unimode, running_mean
from maad.rois import overlay_rois, create_mask, select_rois, find_rois_cwt, remove_background, median_equalizer
from skimage import morphology
import numpy as np
import pandas as pd
###=============== load audio =================
s, fs = load('./data/spinetail.wav')
rois = read_audacity_annot(
'./data/spinetail.txt') ## annotations using Audacity
###=============== compute spectrogram =================
Sxx, tn, fn, ext = spectrogram(s, fs)
Sxx = 10 * np.log10(Sxx)
rois = format_features(rois, tn, fn)
###=============== from Audacity =================
### with all labels
ax, fig = overlay_rois(Sxx, ext, rois, vmin=-120, vmax=20)
# Compute an visualize features
shape, params = shape_features(Sxx, resolution='low', rois=rois)
plot_shape(shape.mean(), params)
# Compute and visualize centroids
centroid = centroid_features(Sxx, rois)
centroid = format_features(centroid, tn, fn)
ax, fig = overlay_centroid(Sxx,
ext,
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn import preprocessing
s, fs = load('../data/spinetail.wav')
rois_tf = read_audacity_annot('../data/spinetail.txt') ## annotations using Audacity
rois_cr = rois_tf.loc[rois_tf.label=='CRER',]
rois_sp = rois_tf.loc[rois_tf.label=='SP',]
Sxx_power, ts, f, ext = spectrogram(s, fs)
Sxx_dB = power2dB(Sxx_power, db_range=90) + 96
# Visualize large vocalizations
rois_cr = format_features(rois_cr, ts, f)
overlay_rois(Sxx_dB, rois_cr, **{'extent':ext, 'vmin':0, 'vmax':80})
# Visualize short vocalizations
rois_sp = format_features(rois_sp, ts, f)
overlay_rois(Sxx_dB, rois_sp, **{'extent':ext, 'vmin':0, 'vmax':80})
# Compute an visualize features
shape_cr, params = shape_features(Sxx_dB, resolution='med', rois=rois_cr)
ax = plot_shape(shape_cr.mean(), params)
shape_sp, params = shape_features(Sxx_dB, resolution='med', rois=rois_sp)
ax = plot_shape(shape_sp.mean(), params)
######## Simple clustering with PCA
#%%
# 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
})
#%%
# 2. Compute acoustic features
# ----------------------------
# The ``shape_feaures`` function uses bidimensional wavelets to get the texture and spectro-temporal shape coeficients of each ROI. Wavelets have the advantage of being robust when the signal-to-noise ratio is low, and derive homogeneous descriptors which facilitate the clustering process. The wavelet decomposition is performed on the complete spectrogram, hence the coeficients for ROIs do not vary much even when not the time-frequency bounds are not exact. The centroid features gives an estimate of the median frequency of the ROIs.
df_shape, params = features.shape_features(Sxx_db,
resolution='low',
rois=df_rois)
df_centroid = features.centroid_features(Sxx_db, df_rois)
bin_std=6,
bin_per=0.5,
verbose=False,
display=False)
# Finaly, we put together pixels that belong to the same acoustic event, and
# remove very small events (<=25 pixel²)
im_rois, df_rois = rois.select_rois(im_mask,
min_roi=25,
max_roi=None,
ext=ext,
display=False,
figsize=(4, (t1 - t0)))
# format dataframe df_rois in order to convert pixels into time and frequency
df_rois = format_features(df_rois, tn, fn)
# overlay bounding box on the original spectrogram
ax, fig = rois.overlay_rois(Sxx_db, ext, df_rois, vmin=0, vmax=96)
# Compute and visualize centroids
df_centroid = features.centroid_features(Sxx_db, df_rois, im_rois)
df_centroid = format_features(df_centroid, tn, fn)
ax, fig = features.overlay_centroid(Sxx_db,
ext,
df_centroid,
savefig=None,
vmin=0,
vmax=96,
marker='+',
ms=2,