def logMelSpectrum(input, samplingrate=20000, plotfilers=False):
"""
Calculates the log output of a Mel filterbank when the input is the power spectrum
Args:
input: array of power spectrum coefficients [N x nfft] where N is the number of frames and
nfft the length of each spectrum
samplingrate: sampling rate of the original signal (used to calculate the filterbank shapes)
Output:
array of Mel filterbank log outputs [N x nmelfilters] where nmelfilters is the number
of filters in the filterbank
Note: use the trfbank function provided in tools.py to calculate the filterbank shapes and
nmelfilters
"""
filter_bank = trfbank(samplingrate, np.shape(input)[1])
if plotfilers:
plt.plot(filter_bank)
plt.xlabel("spectrum length")
plt.ylabel("filter amplitudes")
plt.title("Mel filters in linear frequency scale")
plt.show()
return np.log(input.dot(filter_bank.T))
def logMelSpectrum(inp, samplingrate=20000):
filters = to.trfbank(samplingrate, 512)
print(inp.shape)
#print(filters)
#print(filters.shape)
#plt.plot(np.transpose(filters))
#plt.title('Mel Filterbank',fontsize=20)
#plt.plot(filters[1,:])
#plt.show()
#plt.show()
mel = np.log(np.matmul(inp, np.transpose(filters)))
print(mel)
return np.log(np.matmul(inp, np.transpose(filters)))
"""
def logMelSpectrum(input, samplingrate):
"""
Calculates the log output of a Mel filterbank when the input is the power spectrum
Args:
input: array of power spectrum coefficients [N x nfft] where N is the number of frames and
nfft the length of each spectrum
samplingrate: sampling rate of the original signal (used to calculate the filterbank shapes)
Output:
array of Mel filterbank log outputs [N x nmelfilters] where nmelfilters is the number
of filters in the filterbank
Note: use the trfbank function provided in tools.py to calculate the filterbank shapes and
nmelfilters
"""
filter_bank = trfbank(samplingrate, input.shape[1])
return np.log(input.dot(filter_bank.T))
def logMelSpectrum(input, samplingrate):
"""
Calculates the log output of a Mel filterbank when the input is the power spectrum
Args:
input: array of power spectrum coefficients [N x nfft] where N is the number of frames and
nfft the length of each spectrum
samplingrate: sampling rate of the original signal (used to calculate the filterbanks)
Output:
array of Mel filterbank log outputs [N x nmelfilters] where nmelfilters is the number
of filters in the filterbank
Note: use the trfbank function provided in tools.py to calculate the filterbank shapes and
nmelfilters
"""
N, nfft = input.shape
flt = tools.trfbank(samplingrate, nfft)
return np.log( np.dot( input, flt.transpose() ) )
def logMelSpectrum(input, samplingrate):
"""
Calculates the log output of a Mel filterbank when the input is the power spectrum
Args:
input: array of power spectrum coefficients [N x nfft] where N is the number of frames and
nfft the length of each spectrum
samplingrate: sampling rate of the original signal (used to calculate the filterbank shapes)
Output:
array of Mel filterbank log outputs [N x nmelfilters] where nmelfilters is the number
of filters in the filterbank
Note: use the trfbank function provided in tools.py to calculate the filterbank shapes and
nmelfilters
"""
nfft = input.shape[1]
triangular_filters = tools.trfbank(samplingrate, nfft)
# plt.plot(triangular_filters)
# plt.title('Mel filterbank filters, number of filters = %d'%len(triangular_filters))
# plt.show()
return np.log(np.dot(input, triangular_filters.T))
def logMelSpectrum(input, samplingrate):
"""
Calculates the log output of a Mel filterbank when the input is the power spectrum
Args:
input: array of power spectrum coefficients [N x nfft] where N is the number of frames and
nfft the length of each spectrum
samplingrate: sampling rate of the original signal (used to calculate the filterbank shapes)
Output:
array of Mel filterbank log outputs [N x nmelfilters] where nmelfilters is the number
of filters in the filterbank
Note: use the trfbank function provided in tools.py to calculate the filterbank shapes and
nmelfilters
"""
global fbank
nfft = len(input[0])
fs = samplingrate
fbank = tools.trfbank(fs, nfft)
Melspec = np.dot(input, fbank.T)
return np.log(Melspec)
def logMelSpectrum(input, samplingrate):
"""
Calculates the log output of a Mel filterbank when the input is the power spectrum
Args:
input: array of power spectrum coefficients [N x nfft] where N is the number of frames and
nfft the length of each spectrum
samplingrate: sampling rate of the original signal (used to calculate the filterbank shapes)
Output:
array of Mel filterbank log outputs [N x nmelfilters] where nmelfilters is the number
of filters in the filterbank
Note: use the trfbank function provided in lab1_tools.py to calculate the filterbank shapes and
nmelfilters
"""
N = len(input) # 92frames
Mel = tools.trfbank(samplingrate, len(input[0])) # 40filters*512
M = Mel.shape[0]
# plot the filters in linear frequency scale x=k*512/fs(20000)
#plt.figure()
x = np.zeros((N, M))
for i in range(N):
for j in range(M):
x[i, j] = np.log(np.sum(input[i, :] * Mel[j, :]))
return x