try: #don't want user warnings
while True:
data, fs = record()
out = reduce_noise(data, noise)
ns = fil.bandpass_filter(data, bandpass)
try:
p, freq, b = hmn.psddetectionresults(data)
except IndexError:
pass
b = False
b = True
if b:
# fs = 44100#force 44100 sample rate to prediction why?
#mfcc, chroma, mel, spect, tonnetz = fex.extract_feature(data,fs)#ns changed to raw data
mfcc, chroma, mel, spect, tonnetz = fex.extract_feature(ns, fs)
#a,e,k = lpg.lpc(ns,10)
mfcc_test = par.get_parsed_mfccdata(mfcc, chroma, mel, spect,
tonnetz)
#lpc_test = par.get_parsed_lpcdata(a,k,freq)
if test:
x1 = random.randint(0, 3)
x1 = xx[i]
else:
x1 = clf.predict(mfcc_test)
#x02 = clm.predict(mfcc_test)
#x1 = ((x01[0]+x01[0])/2)
#x2 = clf1.predict(lpc_test)
print("Drone at %s, %s" % (dist_prediction_label(int(x1)), x1))
log.insertdf(int(x1), str(datetime.datetime.now())[:-7])
try: #don't want useless user warnings
while True:
data, fs = record()
#out = reduce_noise(data,noise)
#ns = fil.bandpass_filter(data,bandpass)
try:
p, freq, b = hmn.psddetectionresults(data)
except IndexError:
pass
b = False
b = True
if b:
fs = 44100 #force 44100 sample rate to prediction
#mfcc, chroma, mel, spect, tonnetz = fex.extract_feature(ns,fs)#ns changed to raw data
mfcc, chroma, mel, spect, tonnetz = fex.extract_feature(
data, fs) #ns changed to raw data
#a,e,k = lpg.lpc(ns,10)
mfcc_test = par.get_parsed_mfccdata(mfcc, chroma, mel, spect,
tonnetz)
#lpc_test = par.get_parsed_lpcdata(a,k,freq)
x1 = clf.predict(mfcc_test)
#x02 = clm.predict(mfcc_test)
#x1 = ((x01[0]+x01[0])/2)
#x2 = clf1.predict(lpc_test)
print("Drone at %s" % dist_prediction_label(int(x1)))
log.insertdf(int(x1), str(datetime.datetime.now())[:-7])
print(x1)
output = log.get_result()
'''-----------uncomment if you want to save logs-----------------'''
#log.logdf(sys.argv[1],x01[0],x02[0],str(datetime.datetime.now())[:-7])
'''---------------------------------------------------------------'''
bandpass = [600,10000]#filter unwanted frequencies
prev_time= tm.time()#initiate time
reccount = 0
recdata = np.array([],dtype="float32")
basename = "drone"
labels=[]
"""save server recodings in assets folder"""
for root, dirs, files in os.walk("assets"):
for file in files:
if file.endswith(".wav"):
data, fs = librosa.load(os.path.join(root, file))
tests = np.split(data, 10)
for test in tests:
fs = 44100
ns = fil.bandpass_filter(test,bandpass)
mfcc, chroma, mel, spect, tonnetz = fex.extract_feature(ns,fs)
mfcc1, chroma1, mel1, spect1, tonnetz1 = fex.extract_feature(test,fs)
#a,e,k = lpg.lpc(ns,10)
mfcc_test = par.get_parsed_mfccdata(mfcc, chroma,mel,spect,tonnetz)
mfcc_test1 = par.get_parsed_mfccdata(mfcc1, chroma1,mel1,spect1,tonnetz1)
#lpc_test = par.get_parsed_lpcdata(a,k,freq)
x1 = clf.predict(mfcc_test)
x11 = clf.predict(mfcc_test1)
label = dist_prediction_label(int(x1))
label1 = dist_prediction_label(int(x11))
labels.append([i,file,label,label1])
i+=1
import pandas as pd
df = pd.DataFrame(labels)
df.columns=['idx','filename','labelwithfilter','labelwithrawdata']
