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The GCC-PHAT algorithm is applied to align the far end and near end signals based on Pytorch.
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#!/usr/bin/env python3 | |
# Copyright 2022 Lucky Wong | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License | |
"""The GCC-PHAT algorithm is applied to align the far end and near end signals. | |
Ref: Weighted Recursive Least Square Filter and Neural Network based Residual Echo Suppression for the AEC-Challenge | |
Link: http://arxiv.org/abs/2102.08551 | |
""" | |
from typing import Tuple, List, Optional | |
import math | |
import torch | |
def gcc_phat_frame( | |
probe_fft: torch.Tensor, refence_fft: torch.Tensor, | |
last_cross_corr: Optional[torch.Tensor] = None, | |
sample_rate: int = 16000, | |
smooth_parameter: float = 0.8, | |
window_stride: int = -1 | |
): | |
"""Compute relative delay. | |
Args: | |
probe_fft (torch.Tensor): Fourier transform of real-valued of input frame. (time, fft) | |
refence_fft (torch.Tensor): Fourier transform of real-valued of refence frame. (time, fft) | |
Returns: | |
float: relative delay duration (ms) | |
""" | |
# cross correlation | |
cross_corr = probe_fft * torch.conj(refence_fft) | |
# smoothing | |
if last_cross_corr is not None: | |
smooth_cross_corr = smooth_parameter * \ | |
last_cross_corr+(1-smooth_parameter)*cross_corr | |
else: | |
smooth_cross_corr = cross_corr | |
# find max cross correlation index | |
ifft = torch.fft.irfft( | |
smooth_cross_corr / torch.abs(smooth_cross_corr)) | |
ifft = ifft[:window_stride] | |
max_index = torch.argmax(ifft).item() | |
# relative delay | |
tau = int(max_index/sample_rate*1000.) | |
return tau, smooth_cross_corr | |
class GccPhatAlign(): | |
""" The GCC-PHAT algorithm is applied to align the far end and near end signals. | |
Ref: Weighted Recursive Least Square Filter and Neural Network based Residual Echo Suppression for the AEC-Challenge | |
Link: http://arxiv.org/abs/2102.08551 | |
Args: | |
window_stride_ms (int): window stride duration ms | |
fs (int): Sample rate | |
smooth_parameter (float): smoothing parameter | |
""" | |
def __init__(self, window_stride_ms: int = 500, fs: int = 16000, smooth_parameter: float = 0.8): | |
"""Construct an EncoderLayer object.""" | |
self.window_stride = int(window_stride_ms/1000.*fs) | |
self.fs = fs | |
self.window_len = self.window_stride*2 | |
self.n_fft = (int)(2**math.ceil(math.log2(self.window_len))) | |
self.window = torch.hamming_window( | |
self.window_len, dtype=torch.float32) | |
self.smooth_parameter = smooth_parameter | |
def __call__(self, probe, ref): | |
"""Estimate last relative delay. | |
Args: | |
probe (torch.Tensor): Input signal. | |
ref (torch.Tensor): Refence signal. | |
Returns: | |
float: relative delay duration (ms) | |
""" | |
if probe.dtype == torch.int16: | |
probe = probe.to(dtype=torch.float32) | |
if ref.dtype == torch.int16: | |
ref = ref.to(dtype=torch.float32) | |
probe_frames = probe.unfold(0, self.window_len, self.window_stride) | |
refence_frames = ref.unfold(0, self.window_len, self.window_stride) | |
probe_fft = torch.fft.rfft( | |
probe_frames * self.window, n=self.n_fft) | |
refence_fft = torch.fft.rfft( | |
refence_frames * self.window, n=self.n_fft) | |
frame_num = min(probe_fft.size()[0], refence_frames.size()[0]) | |
last_cross_corr = None | |
tau_list = [] | |
for i in range(frame_num): | |
# relative delay | |
tau, smooth_cross_corr = gcc_phat_frame( | |
probe_fft[i], | |
refence_fft[i], | |
last_cross_corr, | |
sample_rate=self.fs, | |
smooth_parameter=self.smooth_parameter, | |
window_stride=self.window_stride | |
) | |
tau_list.append(tau) | |
last_cross_corr = smooth_cross_corr | |
# We can use every window_stride to update relative delay, here is the most frequent delay value. | |
return max(tau_list, key=tau_list.count) |
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