def gen():
nframes = 128
numpy.random.seed(42)
y = numpy.random.rand(nframes).astype('float32')
n_fft = 64
win_length = n_fft # by default win_length = n_fft
hop_length = win_length // 4 # default hop length is win_length / 4
# window function = 'hann'
D = librosa.stft(y,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length)
#print(D.shape)
# NOTE: librosa uses Fortran order for stft matrix.
if numpy.isfortran(D):
D = numpy.transpose(D)
print('const float g_input[] = {' + print_c_array(y) + '};')
print('const float g_reference[] = {' + print_c_array(D) + '};')
print('const size_t k_n_fft = {};'.format(n_fft))
print('const size_t k_nframes = {};'.format(nframes))
print('const size_t k_out_dim0 = {};'.format(D.shape[0]))
print('const size_t k_out_dim1 = {};'.format(D.shape[1]))
#print('const size_t k_nrows = {};'.format(1))
print('const size_t k_hop_length = {};'.format(hop_length))
print('const size_t k_win_length = {};'.format(win_length))
def gen():
nframes = 16
nrows = 4
numpy.random.seed(42)
volume = numpy.random.rand(nrows, nframes).astype(numpy.float32)
fft_len = nframes - 1
f = numpy.fft.rfft(volume, fft_len)
#print(len(y))
#print(numpy.absolute(y))
#a = numpy.absolute(y)
#print(a.shape)
#print('sum = ', numpy.sum(a, 1))
n = 6
y = numpy.fft.ifft(f, n)
print('const float g_input[] = {' + print_c_array(f) + '};')
print('const float g_reference[] = {' + print_c_array(y) + '};')
print('const size_t k_n = {};'.format(n))
print('const size_t k_ncolumns = {};'.format(f.shape[1]))
print('const size_t k_nrows = {};'.format(f.shape[0]))
def gen():
n = 32
ksize = 3
numpy.random.seed(42)
volume = numpy.random.rand(n).astype(numpy.float32)
out = scipy.signal.medfilt(volume, kernel_size=ksize)
print('const float g_input[] = {' + print_c_array(volume) + '};')
print('const float g_reference[] = {' + print_c_array(out) + '};')
print('const size_t k_input_n = {};'.format(n))
print('const size_t k_window_size = {};'.format(ksize))
def gen():
nframes = 16
ncoeffs = 5
numpy.random.seed(42)
volume = numpy.random.rand(nframes, ncoeffs).astype(numpy.float32)
y = lifter(volume)
print('const float g_input[] = {' + print_c_array(volume) + '};')
print('const float g_reference[] = {' + print_c_array(y) + '};')
print('const size_t k_nframes = {};'.format(nframes))
print('const size_t k_ncoeffs = {};'.format(ncoeffs))
def gen():
win_length = 64
y = scipy.signal.get_window('hann', win_length) # fftbins=True = peridoc
print('const float g_reference[] = {' + print_c_array(y) + '};')
print('const size_t k_win_length = {};'.format(win_length))
def gen():
n = 32
seed = 42
numpy.random.seed(seed)
volume = numpy.random.normal(0.0, 1.0, n).astype(numpy.float32)
print('const float g_reference[] = {' + print_c_array(volume) + '};')
print('const size_t k_n = {};'.format(n))
print('const size_t k_seed = {};'.format(seed))
def gen():
n = 5
m = 3
seed = 42
numpy.random.seed(seed)
a = numpy.random.rand(n).astype(numpy.float32)
v = numpy.random.rand(m).astype(numpy.float32)
c = numpy.convolve(a, v, mode='full')
assert len(c) == (n + m - 1)
print('const float g_a[] = {' + print_c_array(a) + '};')
print('const float g_v[] = {' + print_c_array(v) + '};')
print('const float g_reference[] = {' + print_c_array(c) + '};')
print('const size_t k_n = {};'.format(n))
print('const size_t k_m = {};'.format(m))
print('const size_t k_outnum = {};'.format(len(c)))
def gen():
sr = 22050
n_fft = 2048
M = librosa.filters.mel(sr, n_fft)
if numpy.isfortran(M):
M = numpy.transpose(M)
print('const float g_reference[] = {' + print_c_array(M) + '};')
print('const size_t k_sr = {};'.format(sr))
print('const size_t k_nfft = {};'.format(n_fft))
