def run(audio):
f, psd = aa.spectrum(audio)
peaklist = aa.peak_map(psd)
peaksdex = aa.peak_assign(peaklist)
output = guesswork(f, psd, peaklist, peaksdex)
# print(aa.print_identify(aa.identify(output)))
return output
peaksdex = peak_assign(peaklist)
gf.button_grapher(ax2, frequency, peaksdex, peaklist)
def runfromfft(f, psd):
ax = gf.init_image()
gf.semi_graph(ax, f, psd)
maxdex = aa.max_range(psd, 20)
# gf.button_grapher(ax, f, maxdex, psd)
peaklist = aa.peak_map(psd)
aa.peak_impose(ax, f, peaklist)
plt.show()
if __name__ == '__main__':
filename = sys.argv[1]
bandpass = []
if len(sys.argv) > 3:
lowerbound = int(sys.argv[2])
upperbound = int(sys.argv[3])
bandpass = [lowerbound, upperbound]
audio = aa.load_npy(filename)
bandpass = [80, 10000]
f, psd = aa.spectrum(audio, bandpass=bandpass)
ax = gf.init_image()
gf.semi_graph(ax, f, psd, label=filename)
# maxdex = aa.max_range(psd, 20)
# peaklist = aa.peak_map(psd)
# plt.savefig('%s.png' % filename.split('.')[0])
plt.show()
def procdata(data, bandpass):
data = aa.bandpass_filter(data, bandpass)
f, psd = aa.spectrum(data, bandpass = bandpass)
return f, psd
background = sys.argv[1]
audio = sys.argv[2]
fs = 44100
if len(sys.argv) > 3:
fs = int(sys.argv[3])
# Build image
background = aa.load_npy(background)
audio = aa.load_npy(audio)
ax = gf.init_image()
# Set double scale
ax2 = ax.twinx()
# Run spectrum analysis
freq, curve = aa.spectrum(background, fs)
_, bluecurve = aa.spectrum(audio, fs)
# bluecurve = bluecurve * curve[2]/bluecurve[2]
snratio = bluecurve / curve
# Graph ax
gf.semi_graph(ax, freq, curve, label='background')
gf.semi_graph(ax, freq, bluecurve, 'b', 'recording')
gf.std_graph(ax2, freq, snratio, label='S/N ratio')
maxdex = aa.max_range(bluecurve, 10)
print_maxdex(freq, snratio, maxdex)
gf.pin_grapher(ax, freq, maxdex, curve, bluecurve)
ax.legend(loc=2)
ax2.legend(loc=0)
plt.show()
import numpy as np
import os, sys
sys.path.append(
'/Users/aperocky/workspace/Labwork/Drone_Project/Audio-detection/engines')
import audio_algorithms as aa
import graph_functions as gf
audio1 = aa.load_npy('10m_0m_0.npy')
audio2 = aa.load_npy('10m_2m_0.npy')
audio3 = aa.load_npy('10m_4m_0.npy')
audio4 = aa.load_npy('10m_6m_0.npy')
audio5 = aa.load_npy('10m_10m_0.npy')
bandpass = [80, 10000]
f, psd1 = aa.spectrum(audio1, bandpass=bandpass)
f, psd2 = aa.spectrum(audio2, bandpass=bandpass)
f, psd3 = aa.spectrum(audio3, bandpass=bandpass)
f, psd4 = aa.spectrum(audio4, bandpass=bandpass)
f, psd5 = aa.spectrum(audio5, bandpass=bandpass)
ax = gf.init_image()
gf.semi_graph(ax, f, psd1, c='r')
gf.semi_graph(ax, f, psd2, c='b')
gf.semi_graph(ax, f, psd3, c='g')
gf.semi_graph(ax, f, psd4, c='y')
gf.semi_graph(ax, f, psd5, c='k')
# maxdex = aa.max_range(psd, 20)
# peaklist = aa.peak_map(psd)
# plt.savefig('%s.png' % filename.split('.')[0])
gf.show()