def properties(signal, sample_rate):
"""Return a list of some wave properties for a given 1-D signal
"""
# Measurements that include DC component
DC_offset = mean(signal)
# Maximum/minimum sample value
# Estimate of true bit rate
# Remove DC component
signal -= mean(signal)
# Measurements that don't include DC
signal_level = rms_flat(signal)
peak_level = max(absolute(signal))
crest_factor = peak_level/signal_level
# Apply the A-weighting filter to the signal
weighted = A_weight(signal, sample_rate)
weighted_level = rms_flat(weighted)
# TODO: rjust instead of tabs
return [
'DC offset:\t%f (%.3f%%)' % (DC_offset, DC_offset * 100),
'Crest factor:\t%.3f (%.3f dB)' % (crest_factor, dB(crest_factor)),
'Peak level:\t%.3f (%.3f dBFS)' % (peak_level, dB(peak_level)), # Doesn't account for intersample peaks!
'RMS level:\t%.3f (%.3f dBFS)' % (signal_level, dB(signal_level)),
'RMS A-weighted:\t%.3f (%.3f dBFS(A), %.3f dB)' % (weighted_level, dB(weighted_level), dB(weighted_level/signal_level)),
'-----------------',
]
def properties(signal, sample_rate):
"""Return a list of some wave properties for a given 1-D signal
"""
# Measurements that include DC component
DC_offset = mean(signal)
# Maximum/minimum sample value
# Estimate of true bit rate
# Remove DC component
signal -= mean(signal)
# Measurements that don't include DC
signal_level = rms_flat(signal)
peak_level = max(absolute(signal))
crest_factor = peak_level / signal_level
# Apply the A-weighting filter to the signal
weighted = A_weight(signal, sample_rate)
weighted_level = rms_flat(weighted)
# TODO: rjust instead of tabs
return [
'DC offset:\t%f (%.3f%%)' % (DC_offset, DC_offset * 100),
'Crest factor:\t%.3f (%.3f dB)' % (crest_factor, dB(crest_factor)),
'Peak level:\t%.3f (%.3f dBFS)' %
(peak_level, dB(peak_level)), # Doesn't account for intersample peaks!
'RMS level:\t%.3f (%.3f dBFS)' % (signal_level, dB(signal_level)),
'RMS A-weighted:\t%.3f (%.3f dBFS(A), %.3f dB)' %
(weighted_level, dB(weighted_level), dB(
weighted_level / signal_level)),
'-----------------',
]
def THDN(signal, sample_rate):
"""Measure the THD+N for a signal and print the results
Prints the estimated fundamental frequency and the measured THD+N. This is
calculated from the ratio of the entire signal before and after
notch-filtering.
This notch-filters by nulling out the frequency coefficients ±10% of the
fundamental
"""
# Get rid of DC and window the signal
signal -= mean(
signal
) # TODO: Do this in the frequency domain, and take any skirts with it?
windowed = signal * kaiser(len(signal), 100)
del signal
# Zero pad to nearest power of two
new_len = 2**ceil(log(len(windowed)) / log(2))
windowed = concatenate((windowed, zeros(new_len - len(windowed))))
# Measure the total signal before filtering but after windowing
total_rms = rms_flat(windowed)
# Find the peak of the frequency spectrum (fundamental frequency)
f = rfft(windowed)
i = argmax(abs(f))
true_i = parabolic(log(abs(f)), i)[0]
print 'Frequency: %f Hz' % (sample_rate * (true_i / len(windowed)))
# Filter out fundamental by throwing away values ±10%
lowermin = true_i - 0.1 * true_i
uppermin = true_i + 0.1 * true_i
f[lowermin:uppermin] = 0
# Transform noise back into the time domain and measure it
noise = irfft(f)
THDN = rms_flat(noise) / total_rms
# TODO: RMS and A-weighting in frequency domain?
# Apply A-weighting to residual noise (Not normally used for distortion,
# but used to measure dynamic range with -60 dBFS signal, for instance)
weighted = A_weight(noise, sample_rate)
THDNA = rms_flat(weighted) / total_rms
print "THD+N: %.4f%% or %.1f dB" % (THDN * 100, 20 * log10(THDN))
print "A-weighted: %.4f%% or %.1f dB(A)" % (THDNA * 100, 20 * log10(THDNA))
def THDN(signal, sample_rate):
"""Measure the THD+N for a signal and print the results
Prints the estimated fundamental frequency and the measured THD+N. This is
calculated from the ratio of the entire signal before and after
notch-filtering.
This notch-filters by nulling out the frequency coefficients ±10% of the
fundamental
"""
# Get rid of DC and window the signal
signal -= mean(signal) # TODO: Do this in the frequency domain, and take any skirts with it?
windowed = signal * kaiser(len(signal), 100)
del signal
# Zero pad to nearest power of two
new_len = 2**ceil( log(len(windowed)) / log(2) )
windowed = concatenate((windowed, zeros(new_len - len(windowed))))
# Measure the total signal before filtering but after windowing
total_rms = rms_flat(windowed)
# Find the peak of the frequency spectrum (fundamental frequency)
f = rfft(windowed)
i = argmax(abs(f))
true_i = parabolic(log(abs(f)), i)[0]
print 'Frequency: %f Hz' % (sample_rate * (true_i / len(windowed)))
# Filter out fundamental by throwing away values ±10%
lowermin = true_i - 0.1 * true_i
uppermin = true_i + 0.1 * true_i
f[lowermin: uppermin] = 0
# Transform noise back into the time domain and measure it
noise = irfft(f)
THDN = rms_flat(noise) / total_rms
# TODO: RMS and A-weighting in frequency domain?
# Apply A-weighting to residual noise (Not normally used for distortion,
# but used to measure dynamic range with -60 dBFS signal, for instance)
weighted = A_weight(noise, sample_rate)
THDNA = rms_flat(weighted) / total_rms
print "THD+N: %.4f%% or %.1f dB" % (THDN * 100, 20 * log10(THDN))
print "A-weighted: %.4f%% or %.1f dB(A)" % (THDNA * 100, 20 * log10(THDNA))
