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# Copyright (c) 2009 John Glover, National University of Ireland, Maynooth
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
import simpl
import numpy as np
def burg(signal, order):
"""Using the burg method, calculate order coefficients.
Returns a numpy array."""
coefs = np.array([1.0])
# initialise f and b - the forward and backwards predictors
f = np.zeros(len(signal))
b = np.zeros(len(signal))
for i in range(len(signal)):
f[i] = b[i] = signal[i]
# burg algorithm
for k in range(order):
# fk is f without the first element
fk = f[1:]
# bk is b without the last element
bk = b[0:b.size-1]
# calculate mu
if sum((fk*fk)+(bk*bk)):
# check for division by zero
mu = -2.0 * sum(fk*bk) / sum((fk*fk)+(bk*bk))
else:
mu = 0.0
# update coefs
# coefs[::-1] just reverses the coefs array
coefs = np.hstack((coefs,0)) + (mu * np.hstack((0, coefs[::-1])))
# update f and b
f = fk + (mu*bk)
b = bk + (mu*fk)
return coefs[1:]
def predict(signal, coefs, num_predictions):
"""Using Linear Prediction, return the estimated next num_predictions
values of signal, using the given coefficients.
Returns a numpy array."""
predictions = np.zeros(num_predictions)
past_samples = np.zeros(len(coefs))
for i in range(len(coefs)):
past_samples[i] = signal[-1-i]
sample_pos = 0
for i in range(num_predictions):
# each sample in past_samples is multiplied by a coefficient
# results are summed
for j in range(len(coefs)):
predictions[i] -= coefs[j] * past_samples[(j+sample_pos) % len(coefs)]
sample_pos -= 1
if sample_pos < 0:
sample_pos = len(coefs) - 1
past_samples[sample_pos] = predictions[i]
return predictions
class LPPartialTracking(simpl.PartialTracking):
"Partial tracking, based on the McAulay and Quatieri (MQ) algorithm"
def __init__(self):
simpl.PartialTracking.__init__(self)
self._matching_interval = 100 # peak matching interval, in Hz
def update_partials(self, frame, frame_number):
"""Streamable (real-time) LP peak-tracking.
"""
frame_partials = []
for peak in frame:
pass
return frame_partials
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