We have programmed a device (consisting of a raspberry pi 3) to sit in a room, listen, and characterize the room based on an existing GMM. Our code works successfully until it throws an error around the 17th or 19th iteration. We have narrowed down to where in the code the problem is occurring, but we are unsure of why.
We are beginner programmers, so apologies for the weird formatting...this is all new to us.
Thanks in advance for any help or insight you can provide!
from sklearn import preprocessing
import python_speech_features as mfcc
def calculate_delta(array):
"""Calculate and returns the delta of given feature vector matrix"""
rows,cols = array.shape
deltas = np.zeros((rows,20))
N = 2
for i in range(rows):
index = []
j = 1
while j <= N:
if i-j < 0:
first = 0
else:
first = i-j
if i+j > rows -1:
second = rows -1
else:
second = i+j
index.append((second,first))
j+=1
deltas[i] = ( array[index[0][0]]-array[index[0][1]] + (2 * (array[index[1][0]]-array[index[1][1]])) ) / 10
return deltas
def extract_features(audio,rate):
try:
"""extract 20 dim mfcc features from an audio, performs CMS and combines
delta to make it 40 dim feature vector"""
# audio is audio signal from which to compute features -> should be n*1 array
# rate is samplerate of the signal we are working with
# 0.025 is the length of the analysis window in seconds (default is 25ms)
# 0.01 is the step between successive windows in seconds (default is 10ms)
# 20 is number of cepstrum to return (default is 13)
# append energy is true if zeroth cepstral coefficient is replaced with log of total frame energy
# mfcc() returns a numpy array of size (NUMFRAMES by numcep) containing features, each row holds 1 feature vector
# further possible parameters & their defaults can be found at python-speech-features.readthedocs.io/en/latest/
mfcc_feat = mfcc.mfcc(audio,rate, 0.025, 0.01, 20, appendEnergy = True)
# Scale all data onto one scale, eliminating sparsity & following same concept of Normalization & Standardization
mfcc_feat = preprocessing.scale(mfcc_feat)
delta = calculate_delta(mfcc_feat)
combined = np.hstack((mfcc_feat,delta))
print("Features extracted")
return combined
except Exception as e:
print(e)
print("Extract features failed.")
We believe the problem occurs at this line:
mfcc_feat = mfcc.mfcc(audio,rate, 0.025, 0.01, 20, appendEnergy = True
The error it gives us is:
Backend terminated (returncode: -9)
