def add_to_database(parameters, noteNumber, data, orchestra):
instrument = parameters[2]
tech = parameters[3]
dyn = parameters[4]
#note = parameters[5]
dyn_db = assignDynamics.assign_dynamics(
dyn, instrument, dynamics_list
) # function parameters: dynamics, instrument name, dynamics list
data = cutSample(data)
data = normalize_sound_file.normalize_audio_file(
data, dyn_db) #Set sound file level according to the loaded text file
#print("data normalized")
#mfcc_data=librosa.feature.mfcc(y=data,sr=fs,n_mfcc=12,win_length=fs)
M = len(data) #Length of data (should be 44100)
spectrum = np.fft.fft(data, axis=0)[:M // 2 + 1:-1] #Calculate the fft
#print("spectrum calculated")
S = np.abs(spectrum) #Get rid of complex numbers
S = 20 * np.log10(S) #dB values of data
try:
masking_freq, masking_threshold = maskingCurve.maskingCurve(
S, noteNumber) #Calculate the masking curve
except:
print("Masking calculation fail, using flat masking curve")
masking_freq = constants.threshold[:, 0]
masking_threshold = np.ones(106)
#print("masking calculated")
mfcc_data, centroid = MFCC.custom_mfcc(
data) #Calculate mfcc and spectral centroid
#print("mfcc calculated")
LpcLocs, LpcFreqs = lpc_coeffs.lpc_coeffs(
data) #calculate LPC-frequency response
#print("lpc calculated")
#Add everything to database (except fft spectrum):
nested_update(
orchestra, {
instrument: {
tech: {
dyn: {
noteNumber: {
"data": data,
"masking_curve": masking_threshold,
"masking_locs": masking_freq,
"lpc_curve": LpcFreqs,
"lpc_locs": LpcLocs,
"mfcc": mfcc_data,
"centroid": centroid
}
}
}
}
})
return orchestra
add_to_database(fileParts, noteN, data)
#print(data.shape)
#print(data)
#sd.play(data, fs)
inst = 'alto_flute'
orchestra = {inst: {'data': data}}
M = len(data)
spectrum = np.fft.fft(orchestra[inst]['data'], axis=0)[:M // 2 + 1:-1]
spectrum = np.abs(spectrum)
S = 20 * np.log10(spectrum)
frq = 30
#mfcc_data=librosa.feature.mfcc(y=data,sr=rate,n_mfcc=12,n_fft=int(M),hop_length=int(M+2))[:,0]
mfcc_data, centroid = MFCC.custom_mfcc(data)
LpcLocs, LpcFreqs = lpc_coeffs.lpc_coeffs(data)
# LPC=librosa.lpc(data, lpc_coeffs)
# f,h=freqz(1,LPC, worN=lpc_coeffs, fs=fs)
# h=20 * np.log10(np.abs(h))
A = np.linspace(0, len(spectrum), 101)
#print("mfccs:")
#print(mfcc_data.shape)
#print(mfcc_data)
#print(centroid)
#peaks, _ = findPeaks(S, distance=frq, prominence=20, height=-10)
idx, peaks = findPeaks.peaks(S, noteN)
frq, thr = maskingCurve.maskingCurve(S, noteN)
#peaks = find_peaks_cwt(S,np.arange(1,fs/2+1))