[:-7]) #inserted dummy value to eliminate inconsistency
i = 0
bandpass = [800, 8500] #filter unwanted frequencies
prev_time = tm.time() #initiate time
waveq = queue.Queue(datacount)
recdata = np.array([], dtype="float32") # for wave concatenation
basename = "drone"
xx = [3, 3, 3, 0, 0] # test prediction value, basic check
#xx = [0,0,1,2,3,0,0,3,3,3,0,0] # test prediction value, advanced check
"""main code"""
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)
i = 0
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)
