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#
# Copyright (C) 2016-2019 by Nathan Lovato, Daniel Oakey, Razvan Radulescu, and contributors
#
# This file is part of Power Sequencer.
#
# Power Sequencer 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 3 of the
# License, or (at your option) any later version.
#
# Power Sequencer 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 Power Sequencer. If
# not, see <https://www.gnu.org/licenses/>.
#
import numpy as np
def cross_correlation(mfcc1, mfcc2, nframes):
n1, mdim1 = mfcc1.shape
# n2, mdim2 = mfcc2.shape
n = n1 - nframes + 1
if n < 0:
return None
c = np.zeros(n)
for k in range(n):
cc = np.sum(np.multiply(mfcc1[k : k + nframes], mfcc2[:nframes]), axis=0)
c[k] = np.linalg.norm(cc)
return c
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