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dep_analyzer.py
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dep_analyzer.py
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import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
from loader import DataLoader
from preprocessing import processor as api
def add_subplot_axes(ax, position):
box = ax.get_position()
position_display = ax.transAxes.transform(position[0:2])
position_fig = plt.gcf().transFigure.inverted().transform(position_display)
x = position_fig[0]
y = position_fig[1]
return plt.gcf().add_axes([x, y, box.width * position[2], box.height * position[3]], axisbg='w')
def plot_clip_overview(clip, ax):
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax_waveform = add_subplot_axes(ax, [0.0, 0.7, 1.0, 0.3])
ax_spectrogram = add_subplot_axes(ax, [0.0, 0.0, 1.0, 0.7])
with clip.audio as audio:
ax_waveform.plot(np.arange(0, len(audio.raw)) / float(api.Clip.RATE), audio.raw)
ax_waveform.get_xaxis().set_visible(False)
ax_waveform.get_yaxis().set_visible(False)
ax_waveform.set_title('{0} \n {1}'.format(clip.category, clip.filename), {'fontsize': 8}, y=1.03)
result = np.array(np.array(clip.feature_list['fft'].get_logamplitude()[0:1]))
# result = np.array(clip.feature_list['mfcc'].get_mel_spectrogram()[0:2])
librosa.display.specshow(result, sr=api.Clip.RATE, x_axis='time', y_axis='mel', cmap='RdBu_r')
ax_spectrogram.get_xaxis().set_visible(False)
ax_spectrogram.get_yaxis().set_visible(False)
def plot_single_clip(clip):
col_names_mfcc = list('MFCC_{}'.format(i) for i in range(np.shape(clip.feature_list["mfcc"].get_mfcc())[1]))
col_names_zcr = list('ZCR_{}'.format(i) for i in range(1))
MFCC = pd.DataFrame(clip.feature_list["mfcc"].get_mfcc()[:, :], columns=col_names_mfcc)
ZCR = pd.DataFrame(clip.feature_list["zcr"].get_zcr()[:], columns=col_names_zcr)
f = plt.figure(figsize=(10, 6))
ax = f.add_axes([0.0, 0.0, 1.0, 1.0])
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.set_frame_on(False)
ax_mfcc = add_subplot_axes(ax, [0.0, 0.0, 1.0, 0.75])
ax_mfcc.set_xlim(-400, 400)
ax_zcr = add_subplot_axes(ax, [0.0, 0.85, 1.0, 0.05])
ax_zcr.set_xlim(0.0, 1.0)
plt.title('Feature distribution across frames of a single clip ({0} : {1})'.format(clip.category, clip.filename), y=1.5)
sb.boxplot(data=MFCC, orient='h', order=list(reversed(MFCC.columns)), ax=ax_mfcc)
sb.boxplot(data=ZCR, orient='h', ax=ax_zcr)
plt.show()
def plot_single_feature_one_clip(feature, title, ax):
sb.despine()
ax.set_title(title, y=1.10)
sb.distplot(feature, bins=20, hist=True, rug=False,
hist_kws={"histtype": "stepfilled", "alpha": 0.5},
kde_kws={"shade": False},
color=sb.color_palette("muted", 4)[2], ax=ax)
def plot_single_feature_all_clips(feature, title, ax):
sb.despine()
ax.set_title(title, y=1.03)
sb.boxplot(feature, vert=False, orient='h', order=list(reversed(feature.columns)), ax=ax)
def plot_single_feature_aggregate(feature, title, ax):
sb.despine()
ax.set_title(title, y=1.03)
sb.distplot(feature, bins=20, hist=True, rug=False,
hist_kws={"histtype": "stepfilled", "alpha": 0.5},
kde_kws={"shade": False},
color=sb.color_palette("muted", 4)[1], ax=ax)
def generate_feature_summary(dataset, category, clip, coefficient):
title = "{0} : {1}".format(dataset[category][clip].category, dataset[category][clip].filename)
MFCC = pd.DataFrame()
aggregate = []
for i in range(0, len(dataset[category])):
MFCC[i] = dataset[category][i].feature_list["mfcc"].get_mfcc()[:, coefficient]
aggregate = np.concatenate([aggregate, dataset[category][i].feature_list["mfcc"].get_mfcc()[:, coefficient]])
f = plt.figure(figsize=(14, 12))
f.subplots_adjust(hspace=0.6, wspace=0.3)
ax1 = plt.subplot2grid((3, 3), (0, 0))
ax2 = plt.subplot2grid((3, 3), (1, 0))
ax3 = plt.subplot2grid((3, 3), (0, 1), rowspan=2)
ax4 = plt.subplot2grid((3, 3), (0, 2), rowspan=2)
ax1.set_xlim(0.0, 0.5)
ax2.set_xlim(-100, 250)
ax4.set_xlim(-100, 250)
plot_single_feature_one_clip(dataset[category][clip].feature_list["zcr"].get_zcr(), 'ZCR distribution across frames'
'\n{0}'.format(title), ax1)
plot_single_feature_one_clip(dataset[category][clip].feature_list["mfcc"].get_mfcc()[:, coefficient],
'MFCC_{0} distribution across frames\n{1}'.format(coefficient, title), ax2)
plot_single_feature_all_clips(MFCC, 'Differences in MFCC_{0} distribution\nbetween clips of {1}'.format(coefficient
,dataset[ category][ clip].category), ax3)
plot_single_feature_aggregate(aggregate,'Aggregate MFCC_{0} distribution\n(bag-of-frames across all clips\nof {1})'.
format(coefficient, dataset[category][clip].category), ax4)
plt.show()
def view_clip_overview(categories = 5, clips_shown = 1):
f, axes = plt.subplots(categories, clips_shown, figsize=(clips_shown * 2, categories * 2), sharex=True, sharey=True)
f.subplots_adjust(hspace=0.35)
for c in range(0, categories):
for i in range(0, clips_shown):
plot_clip_overview(clips_10[c][i], axes[c])
plt.show()
loader = DataLoader('/home/runge/projects/sound_detector/TRAIN-10', "audio_clips_segmentation.tfrecords", 512, 1,1, 2)
clips_10 = loader.load_dataset_from_ogg('/home/runge/projects/sound_detector/TRAIN-10')
# plot_single_clip(clips_10[1][0])
# generate_feature_summary(clips_10, 1, 0, 1)
#view_clip_overview(10,1)
save_clip_overview(10, 1)