├── LICENSE ├── README.md ├── audio ├── README.md ├── REC_e_post_smooth_SNR_0dB.wav ├── REC_e_post_smooth_SNR_10dB.wav ├── REC_e_post_smooth_SNR_25dB.wav ├── REC_e_post_smooth_targetSourceOnly.wav ├── REC_y_SNR_0dB.wav ├── REC_y_SNR_10dB.wav ├── REC_y_SNR_25dB.wav ├── REC_y_targetSourceOnly.wav ├── e_post_SNR_0dB.wav ├── e_post_SNR_10dB.wav ├── e_post_SNR_25dB.wav ├── e_post_smooth_SNR_0dB.wav ├── e_post_smooth_SNR_10dB.wav ├── e_post_smooth_SNR_25dB.wav ├── s1.wav ├── v_SNR_0dB.wav ├── v_SNR_10dB.wav ├── v_SNR_25dB.wav ├── x1.wav ├── x2.wav ├── y_SNR_0dB.wav ├── y_SNR_10dB.wav └── y_SNR_25dB.wav ├── main.m ├── param └── SQRT_PSD_RETF │ ├── alpha_sqrtMP.mat │ └── lap_div.mat └── tools ├── STFT ├── calc_ISTFT.m └── calc_STFT.m ├── estim ├── ISCLP_KF │ ├── ISCLP.m │ └── ISCLP.m~ └── SQRT_PSD_RETF │ ├── corrmat │ ├── estim_corrmat.m │ ├── forget2tau.m │ └── tau2forget.m │ ├── desmooth_GEVD.m │ ├── estim_PSD_RETF.m │ └── minprob │ ├── solve_RETFup.m │ ├── solve_convMP.m │ ├── solve_convMP_simple.m │ └── solve_sqrtMP.m ├── plot └── plotSpec.m └── spatial ├── calc_diffcoherence.m └── doa2steervec.m /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ISCLP-KF 2 | Integrated sidelobe cancellation and linear prediction Kalman filter for joint multi-microphone speech dereverberation, interfering speech cancellation, and noise reduction. 3 | 4 | See main.m for further details. 5 | 6 | See ./audio for audio examples. 7 | -------------------------------------------------------------------------------- /audio/README.md: -------------------------------------------------------------------------------- 1 | 2 | ==== audio based on simulations, cf. Sec V ==== 3 | 4 | * acoustic scenario, cf. Sec. V-C 5 | 6 | number of microphones - 5 7 | inter-microphone distance - 8cm 8 | reverberation time - 0.61s 9 | source 1 - female speech, 2m distance, 0deg rel. to broadside direction 10 | source 2 - male speech, 2m distance, 60deg rel. to broadside direction 11 | noise type - babble noise 12 | 13 | 14 | * audio files (as loaded/generated by main.m) 15 | 16 | x1.wav - reverberant speech component 1, cf. (2) 17 | x2.wav - reverberant speech component 2, cf. (2) 18 | v_SNR_#dB.wav - noise component at #dB SNR, cf. (2) 19 | y_SNR_#dB.wav - microphone signal at #dB SNR, cf. (1) 20 | s1.wav - early source image 1 (target signal), cf. (6) 21 | e_post_SNR_#dB.wav - enhanced signal, posterior, at #dB SNR cf. (42) 22 | e_post_smooth_SNR_#dB.wav - enhanced signal, posterior, smooth, at #dB SNR cf. (44) 23 | 24 | 25 | 26 | ==== audio based on recordings, cf. Sec. VI ==== 27 | 28 | * acoustic scenario, cf. Sec. VI 29 | 30 | number of microphones - 5 31 | inter-microphone distance - 8cm 32 | reverberation time - 1.5s 33 | source 1 - male speech, 2m distance, 0deg rel. to broadside direction 34 | source 2 - female speech, 2m distance, 45deg rel. to broadside direction 35 | noise type - babble noise, generated by 8 loudspeakers 36 | 37 | * audio files 38 | 39 | REC_y_targetSourceOnly.wav - microphone signal, in absence of interfering noise and speech, cf. (1) 40 | REC_e_targetSourceOnly.wav - enhanced signal, posterior, smooth, in absence of interfering noise and speech, cf. (44) 41 | REC_y_SNR_#dB.wav - microphone signal at #dB SNR, cf. (1) 42 | REC_e_post_smooth_SNR_#dB.wav - enhanced signal, posterior, smooth, at #dB SNR cf. (44) 43 | -------------------------------------------------------------------------------- /audio/REC_e_post_smooth_SNR_0dB.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tdietzen/ISCLP-KF/ccfb8bb92f79a1593a62ff7c512b9cc7cfcf0310/audio/REC_e_post_smooth_SNR_0dB.wav -------------------------------------------------------------------------------- /audio/REC_e_post_smooth_SNR_10dB.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tdietzen/ISCLP-KF/ccfb8bb92f79a1593a62ff7c512b9cc7cfcf0310/audio/REC_e_post_smooth_SNR_10dB.wav -------------------------------------------------------------------------------- /audio/REC_e_post_smooth_SNR_25dB.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tdietzen/ISCLP-KF/ccfb8bb92f79a1593a62ff7c512b9cc7cfcf0310/audio/REC_e_post_smooth_SNR_25dB.wav 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https://raw.githubusercontent.com/tdietzen/ISCLP-KF/ccfb8bb92f79a1593a62ff7c512b9cc7cfcf0310/audio/y_SNR_10dB.wav -------------------------------------------------------------------------------- /audio/y_SNR_25dB.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tdietzen/ISCLP-KF/ccfb8bb92f79a1593a62ff7c512b9cc7cfcf0310/audio/y_SNR_25dB.wav -------------------------------------------------------------------------------- /main.m: -------------------------------------------------------------------------------- 1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2 | % Copyright 2019 Thomas Dietzen 3 | % 4 | % This software is distributed under the terms of the GNU Public License 5 | % version 3 (http://www.gnu.org/licenses/gpl.txt). 6 | % 7 | % If you find it useful, please cite: 8 | % 9 | % [1] T. Dietzen, S. Doclo, M. Moonen, and T. van Waterschoot, “Integrated 10 | % sidelobe cancellation and linear prediction Kalman filter for joint 11 | % multimicrophone dereverberation, interfering speech cancellation, and 12 | % noise reduction,” IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 28, 13 | % pp. 740 – 754, Jan. 2020. 14 | % [2] T. Dietzen, S. Doclo, M. Moonen, and T. van Waterschoot, “Square 15 | % root-based multi-source early PSD estimation and recursive RETF update 16 | % in reverberant environments by means of the orthogonal Procrustes 17 | % problem,” IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 28, 18 | % pp. 755 – 769, Jan. 2020. 19 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 20 | 21 | % Example of the ISCLP Kalman filter as described in [1]. The code in 22 | % contained main.m requests an SNR vaule by the user, loads microphone 23 | % signals, estimates early PSDs and updates RETFs using [2] (see also 24 | % https://github.com/tdietzen/SQRT-PSD-RETF), runs the ISCLP Kalman filter, 25 | % and plays back the enhanced signal. 26 | 27 | 28 | %% PREAMBLE 29 | 30 | clear; 31 | cd(fileparts(mfilename('fullpath'))); 32 | addpath(genpath(pwd)); 33 | set(0,'DefaultFigureWindowStyle','docked'); 34 | 35 | 36 | %% CONFIGURATION 37 | 38 | %%% ACOUSTIC SETTINGS 39 | 40 | % speed of sound 41 | c = 340; 42 | % microphone positions 43 | micPos = [... 44 | 0, 0;... 45 | 0.08, 0;... 46 | 0.16, 0;... 47 | 0.24, 0;... 48 | 0.32, 0;... 49 | ]; 50 | % number of microphones 51 | M = size(micPos,1); 52 | % source angles 53 | sourceAng = [0 60]; 54 | % SNR 55 | SNR = input('which SNR/dB? '); 56 | % microphone signal components 57 | x1_TD = audioread(['.' filesep 'audio' filesep 'x1.wav']); 58 | s1_TD = audioread(['.' filesep 'audio' filesep 's1.wav']); 59 | x2_TD = audioread(['.' filesep 'audio' filesep 'x2.wav']); 60 | v_TD_SNR_0dB = audioread(['.' filesep 'audio' filesep 'v_SNR_0dB.wav']); 61 | v_TD_SNR_scaled = db2mag(-SNR)*v_TD_SNR_0dB; 62 | y_TD = x1_TD + x2_TD + v_TD_SNR_scaled; 63 | 64 | %%% ALGORITHMIC SETTINGS 65 | 66 | %%% STFT 67 | % sample rate 68 | fs = 16000; 69 | % STFT parameters 70 | N_STFT = 512; 71 | R_STFT = N_STFT/2; 72 | win = sqrt(hann(N_STFT,'periodic')); 73 | N_STFT_half = floor(N_STFT/2)+1; 74 | % frequency vector 75 | f = linspace(0,fs/2,N_STFT_half); 76 | 77 | %%% ISCLP Kalman filter [1] 78 | % prediction length 79 | L = 6; 80 | % forgetting factor alpha 81 | alpha_ISCLP_KF = 1-db2pow(-25); 82 | A = sqrt(alpha_ISCLP_KF); 83 | % LP filter variance 84 | psi_wLP = db2pow(-4); 85 | % SC filter variance 86 | psi_wSC = db2pow(linspace(0,-15,257)); 87 | % build Psi_w_tilde_init and Psi_w_delta 88 | psi_LP_init = kron((psi_wLP.^(1:L-1)).', ones(M,1)); 89 | Psi_w_tilde_init = cell(N_STFT_half,1); 90 | Psi_w_delta = cell(N_STFT_half,1); 91 | for k = 1:N_STFT_half 92 | Psi_w_tilde_init{k} = diag([psi_wSC(k)*ones(M-1,1); psi_LP_init]); 93 | Psi_w_delta{k} = (1-alpha_ISCLP_KF)*Psi_w_tilde_init{k}; 94 | end 95 | % gain decay limitation 96 | beta_SCLP_KF = db2mag(-2); 97 | 98 | %%% PSD estimation and RETF update [2] 99 | % forgetting factor zeta 100 | zeta = tau2forget(2*M*R_STFT/fs, R_STFT, fs); 101 | % laplace coefficients of speech per frequency bin 102 | tmp = load('lap_div.mat'); 103 | lap_div = tmp.lap_div; 104 | % RETF updating threshold 105 | xi_thresh = db2pow(-2); 106 | % penelty factor alpha 107 | tmp = load('alpha_sqrtMP.mat'); 108 | alpha_SQRT_PSD_RETF = tmp.alpha_sqrtMP; 109 | % initial RETFs 110 | H_init_FT = doa2steervec(micPos, sourceAng, N_STFT_half, fs, c); 111 | % diffuse coherence matrix 112 | Gamma = calc_diffcoherence(micPos,N_STFT,fs,c,1e-3); 113 | 114 | 115 | %%% FIGURE SETTINGS 116 | 117 | % spectogram figure settings 118 | xTickProp = [0, R_STFT/fs, fs/R_STFT]; 119 | yTickProp = [0, fs/(2000*R_STFT), R_STFT/2]; 120 | cRange = [-55 5]; 121 | 122 | 123 | %% STFT PROCESSING 124 | 125 | % transform 126 | y_STFT = calc_STFT(y_TD, fs, win, N_STFT, R_STFT, 'onesided'); 127 | s1_STFT = calc_STFT(s1_TD, fs, win, N_STFT, R_STFT, 'onesided'); 128 | 129 | % plot 130 | figure('Name',['microphone signal, SNR = ' num2str(SNR) 'dB']); 131 | plotSpec(y_STFT(:,:,1), 'mag', xTickProp, yTickProp, cRange, 0); title(['y, SNR = ' num2str(SNR) 'dB']); ylabel('f/kHz'); 132 | figure('Name','target signal'); 133 | plotSpec(s1_STFT(:,:,1), 'mag', xTickProp, yTickProp, cRange, 0); title('s1'); ylabel('f/kHz'); 134 | drawnow; 135 | 136 | 137 | %% EARLY PSD ESTIMATION, RETF UPDATE [2] 138 | 139 | fprintf(' * estimate early PSDs, update RETFs [2]...\n'); 140 | 141 | % correlation matrix of microphone signal 142 | Psi_y_STFT = estim_corrmat(y_STFT, zeta); 143 | % compute GEVD 144 | [P_STFT, lambda_STFT] = desmooth_GEVD(Psi_y_STFT, Gamma,... 145 | 'lambdaMin', 0,... 146 | 'forgetPSD', zeta); 147 | 148 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 149 | % 150 | % NOTE: instead of desmoothing eigenvalues as in the implementation above, 151 | % one might prefer to use instantaneous eigenvalues, see also [a] and the 152 | % desmooth_GEVD() function in [b]. 153 | % 154 | % [a] T. Dietzen, M. Moonen, and T. van Waterschoot, 'Instantaneous PSD 155 | % estimation for speech enhancement based on generalized principal 156 | % components,' in Proc. 28th European Signal Process. Conf. (EUSIPCO 2020), 157 | % Amsterdam, Netherlands, Jan 2021, pp. 1-5. 158 | % 159 | % [b] https://github.com/tdietzen/INSTANT-PSD 160 | % 161 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 162 | 163 | [phi_s_hat,... 164 | phi_xl_hat,... 165 | H_hat_prior_STFT,... 166 | H_hat_post_STFT,... 167 | H_update_pattern]... 168 | = estim_PSD_RETF(P_STFT, lambda_STFT, Gamma, H_init_FT,... 169 | 'method', 'square-root MP',... 170 | 'itmax', 20,... 171 | 'alpha', alpha_SQRT_PSD_RETF,... 172 | 'beta', 20*lap_div.^2,... 173 | 'xiThresh', xi_thresh... 174 | ); 175 | 176 | % plot 177 | figure('Name',['target PSD estimate, SNR = ' num2str(SNR) 'dB']); 178 | plotSpec(phi_s_hat(:,:,1), 'pow', xTickProp, yTickProp, cRange, 0); title(['phi s1 hat, SNR = ' num2str(SNR) 'dB']); ylabel('f/kHz'); 179 | drawnow; 180 | 181 | 182 | %% ISCLP KALMAN FILTER [1] 183 | 184 | fprintf(' * run ISCLP Kalman filer [1]...\n'); 185 | 186 | % number of frames 187 | numFrames = size(y_STFT,2); 188 | 189 | % init outputs 190 | q_STFT = zeros(N_STFT_half, numFrames); 191 | e_prio_STFT = zeros(N_STFT_half, numFrames); 192 | e_post_STFT = zeros(N_STFT_half, numFrames); 193 | e_post_smooth_STFT = zeros(N_STFT_half, numFrames); 194 | 195 | 196 | for k = 2:N_STFT_half 197 | 198 | % reorganize data 199 | y_stack = shiftdim(squeeze(y_STFT(k,:,:)),1); % from (1 x numFrames x M) to (M x numFrames) 200 | h_stack = shiftdim(squeeze(H_hat_post_STFT(k,:,:,1)),1); % from (1 x numFrames x M x 1) to (M x numFrames) 201 | psi_sT_stack = shiftdim(squeeze(phi_s_hat(k,:,1)),1); % from (1 x numFrames x 1) to (1 x numFrames) 202 | 203 | % run ISCLP Kalman filter and spectral post processor 204 | [ q_stack, ... 205 | e_prio_stack,... 206 | e_post_stack,... 207 | e_post_smooth_stack ] = ... 208 | ISCLP(... 209 | A, Psi_w_delta{k},... 210 | y_stack,... 211 | psi_sT_stack,... 212 | h_stack,... 213 | Psi_w_tilde_init{k},... 214 | beta_SCLP_KF ); 215 | 216 | % save output 217 | q_STFT(k,:) = q_stack; 218 | e_prio_STFT(k,:) = e_prio_stack; 219 | e_post_STFT(k,:) = e_post_stack; 220 | e_post_smooth_STFT(k,:) = e_post_smooth_stack; 221 | 222 | end 223 | 224 | % plot 225 | figure('Name',['enhanced signal, beta = 0, SNR = ' num2str(SNR) 'dB']); 226 | plotSpec(e_post_STFT(:,:,1), 'mag', xTickProp, yTickProp, cRange, 0); title(['e post, beta = 0, SNR = ' num2str(SNR) 'dB']); ylabel('f/kHz'); 227 | figure('Name',['enhanced signal, beta = ' num2str(round(beta_SCLP_KF,2)) ', SNR = ' num2str(SNR) 'dB']); 228 | plotSpec(e_post_smooth_STFT(:,:,1), 'mag', xTickProp, yTickProp, cRange, 0); title(['e post, beta = ' num2str(round(beta_SCLP_KF,2)) ', SNR = ' num2str(SNR) 'dB']); ylabel('f/kHz'); 229 | drawnow; 230 | 231 | 232 | %% ISTFT PROCESSING 233 | 234 | e_post_TD = calc_ISTFT(e_post_STFT, win, N_STFT, R_STFT, 'onesided'); 235 | e_post_smooth_TD = calc_ISTFT(e_post_smooth_STFT, win, N_STFT, R_STFT, 'onesided'); 236 | 237 | 238 | %% WRITE AUDIO AND PLAY BACK 239 | 240 | audiowrite(['.' filesep 'audio' filesep 'v_SNR_' num2str(SNR) 'dB.wav'], v_TD_SNR_scaled, fs); 241 | audiowrite(['.' filesep 'audio' filesep 'y_SNR_' num2str(SNR) 'dB.wav'], y_TD, fs); 242 | audiowrite(['.' filesep 'audio' filesep 'e_post_SNR_' num2str(SNR) 'dB.wav'],e_post_TD,fs); 243 | audiowrite(['.' filesep 'audio' filesep 'e_post_smooth_SNR_' num2str(SNR) 'dB.wav'],e_post_smooth_TD,fs); 244 | 245 | fprintf(' * play microphone signal...\n'); 246 | sound(y_TD(:,1), fs); pause(length(y_TD(:,1))/fs); 247 | fprintf(' * play enhanced signal...\n'); 248 | sound(e_post_smooth_TD(:,1), fs); 249 | 250 | fprintf('\nDONE.\n'); 251 | -------------------------------------------------------------------------------- /param/SQRT_PSD_RETF/alpha_sqrtMP.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tdietzen/ISCLP-KF/ccfb8bb92f79a1593a62ff7c512b9cc7cfcf0310/param/SQRT_PSD_RETF/alpha_sqrtMP.mat -------------------------------------------------------------------------------- /param/SQRT_PSD_RETF/lap_div.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tdietzen/ISCLP-KF/ccfb8bb92f79a1593a62ff7c512b9cc7cfcf0310/param/SQRT_PSD_RETF/lap_div.mat -------------------------------------------------------------------------------- /tools/STFT/calc_ISTFT.m: -------------------------------------------------------------------------------- 1 | function x = calc_ISTFT(X, win, N_STFT, R_STFT, sides) 2 | % x = calc_ISTFT(X, win, N_STFT, R_STFT, sides) 3 | % performs the inverse STFT. 4 | % 5 | % IN: 6 | % X STFT tensor - freqbins x frames x channels 7 | % win window function 8 | % N_STFT frame length 9 | % R_STFT frame shift 10 | % sides {'onesided', 'twosided'}, return either onesided or twosided STFT 11 | % 12 | % OUT: 13 | % x signal - samples x channels 14 | 15 | [~, L, M] = size(X); 16 | if strcmp(sides, 'onesided') 17 | X = [X; conj(X(end-1:-1:2,:,:))]; 18 | end 19 | x_frames = ifft(X, [], 1, 'symmetric'); 20 | 21 | % apply synthesis window 22 | win = repmat(win, [1, L, M]); 23 | x_frames = x_frames.*win; 24 | x_frames = x_frames(1:N_STFT,:,:); 25 | 26 | % init output 27 | x = zeros(R_STFT*(L-1)+N_STFT, M); 28 | 29 | % OLA processing 30 | for l = 1:L 31 | sampIdx = (l-1)*R_STFT+1:(l-1)*R_STFT+N_STFT; 32 | x(sampIdx,:) = x(sampIdx,:) + squeeze(x_frames(:,l,:)); 33 | end -------------------------------------------------------------------------------- /tools/STFT/calc_STFT.m: -------------------------------------------------------------------------------- 1 | function [X, f] = calc_STFT(x, fs, win, N_STFT, R_STFT, sides) 2 | % [X, f] = calc_STFT(x, fs, win, N_STFT, R_STFT, sides) 3 | % performs the STFT. 4 | % 5 | % IN: 6 | % x signal - samples x channels 7 | % fs sampling frequency 8 | % win window function 9 | % N_STFT frame length 10 | % R_STFT frame shift 11 | % sides {'onesided', 'twosided'}, return either onesided or twosided STFT 12 | % 13 | % OUT: 14 | % X STFT tensor - freqbins x frames x channels 15 | % f frequency vector 16 | 17 | % use only half FFT spectrum 18 | N_STFT_half = N_STFT/2 + 1; 19 | 20 | % get frequency vector 21 | f = linspace(0,fs/2,N_STFT_half); 22 | if strcmp(sides, 'twosided') 23 | f = [f, -f(end-1:-1:2)]; 24 | end 25 | 26 | % init 27 | L = floor((length(x) - N_STFT + R_STFT)/R_STFT); 28 | M = size(x,2); 29 | switch sides 30 | case 'onesided' 31 | X = zeros(N_STFT_half, L, size(x,2)); 32 | case 'twosided' 33 | X = zeros(N_STFT, L, M); 34 | end 35 | 36 | for m = 1:M 37 | for l = 1:L % Frame index 38 | x_frame = x((l-1)*R_STFT+1:(l-1)*R_STFT+N_STFT, m); 39 | X_frame = fft(win.*x_frame); 40 | switch sides 41 | case 'onesided' 42 | X(:,l,m) = X_frame(1:N_STFT_half, :); 43 | case 'twosided' 44 | X(:,l,m) = X_frame; 45 | end 46 | end 47 | end 48 | 49 | end -------------------------------------------------------------------------------- /tools/estim/ISCLP_KF/ISCLP.m: -------------------------------------------------------------------------------- 1 | function [ q_stack, e_prio_stack, e_post_stack, e_post_smooth_stack ] = ISCLP( A, Psi_w_delta, y_stack, psi_s_stack, H_stack, Psi_w_tilde_init, beta ) 2 | % [ q_stack, e_prio_stack, e_post_stack, e_post_smooth_stack ] = ISCLP( A, Psi_w_delta, y_stack, psi_s_stack, H_stack, Psi_w_tilde_init, beta ) 3 | % runs the ISCLP Kalman filter in one frequency bin. 4 | % 5 | % IN: 6 | % A state transition matrix - statedim x statedim 7 | % Psi_w_delta process noise correlation matrix - statedim x statedim 8 | % y_stack microphone signal - channels x frames 9 | % psi_s_stack target signal PSD - frames 10 | % H_stack RETF - channels (x sources) x frames (if more than one source) 11 | % Psi_w_tilde_init initial state estimation error correlation matrix - statedim x statedim 12 | % beta gain decay limitation 13 | % 14 | % OUT: 15 | % q_stack MF output - frames 16 | % e_prio_stack ISCLP output, prior - frames 17 | % e_post_stack ISCLP output, posterior - frames 18 | % e_post_smooth_stack ISCLP output, smooth posterior - frames 19 | 20 | 21 | % get dimensions 22 | if ismatrix(H_stack) 23 | N = 1; 24 | [M, numFrames] = size(H_stack); 25 | else 26 | [M, N, numFrames] = size(H_stack); 27 | end 28 | stateDim = size(Psi_w_tilde_init,1); 29 | 30 | % initKF 31 | % 32 | w_hat = zeros(stateDim,1); 33 | Psi_w_tile = Psi_w_tilde_init; 34 | % 35 | y_old = zeros(M,1); 36 | % 37 | u_LP = zeros(stateDim-M+1,1); 38 | % 39 | smooth_gain = 1; 40 | 41 | % init output 42 | q_stack = zeros(1, numFrames); 43 | e_prio_stack = zeros(1, numFrames); 44 | e_post_stack = zeros(1, numFrames); 45 | e_post_smooth_stack = zeros(1, numFrames); 46 | 47 | 48 | for i_frame = 1:numFrames 49 | 50 | %%% Load Data 51 | % 52 | y = y_stack(:,i_frame); 53 | psi_s = psi_s_stack(i_frame); 54 | if N == 1 55 | H = H_stack(:,i_frame); 56 | else 57 | H = H_stack(:,:,i_frame); 58 | end 59 | 60 | 61 | %%% Spatio-Temporal Pre-Processing 62 | % 63 | % compute g, B 64 | g = sum(H/(H'*H),2); 65 | Btmp = eye(M)-(H/(H'*H))*H'; 66 | B = Btmp(:,1:M-N); 67 | % update q, u 68 | q = g'*y; 69 | u_SC = B'*y; 70 | u_LP = [y_old; u_LP(1:end-M)]; 71 | u = [u_SC; u_LP]; 72 | 73 | 74 | %%% Kalman Filter 75 | % 76 | % time update: state estimate 77 | w_hat = A*w_hat; 78 | % time update: state estimation error correlation matrix 79 | Psi_w_tile = A'*Psi_w_tile*A + Psi_w_delta; 80 | % symmetrize (just in case, avoiding accuracy issues) 81 | Psi_w_tile = 0.5*(Psi_w_tile+Psi_w_tile'); 82 | % error 83 | e_prio_conj = conj(q) - u'*w_hat; 84 | % error PSD 85 | psi_e = real(u'*Psi_w_tile*u) + psi_s + eps; 86 | % Kalman gain 87 | k = Psi_w_tile*u/psi_e; 88 | % measurement update: state estimation error correlation matrix 89 | w_hat = w_hat + k*e_prio_conj; 90 | % measurement update: state estimation error correlation matrix 91 | Psi_w_tile = Psi_w_tile - k*(u'*Psi_w_tile); 92 | 93 | 94 | %%% Spectral Post Processing 95 | % 96 | % compute gains 97 | gain = psi_s/psi_e; 98 | smooth_gain = max(gain, beta*smooth_gain); 99 | % 100 | % apply gains 101 | e_post_conj = gain*e_prio_conj; 102 | e_post_smooth_conj = smooth_gain*e_prio_conj; 103 | 104 | 105 | %%% Save Data 106 | % 107 | % save 108 | q_stack(:,i_frame) = q; 109 | e_prio_stack(:,i_frame) = conj(e_prio_conj); 110 | e_post_stack(:,i_frame) = conj(e_post_conj); 111 | e_post_smooth_stack(:,i_frame) = conj(e_post_smooth_conj); 112 | % save previous mic. signal 113 | y_old = y; 114 | 115 | end 116 | 117 | end 118 | -------------------------------------------------------------------------------- /tools/estim/ISCLP_KF/ISCLP.m~: -------------------------------------------------------------------------------- 1 | function [ q_stack, e_prio_stack, e_post_stack, e_post_smooth_stack ] = ISCLP( A, Psi_w_delta, y_stack, psi_s_stack, H_stack, Psi_w_tilde_init, beta ) 2 | % [ q_stack, e_prio_stack, e_post_stack, e_post_smooth_stack ] = ISCLP( A, Psi_w_delta, y_stack, psi_s_stack, H_stack, Psi_w_tilde_init, beta ) 3 | % runs the ISCLP Kalman filter in one frequency bin 4 | % 5 | % IN: 6 | % A state transition matrix 7 | % Psi_w_delta process noise correlation matrix 8 | % y_stack microphone signal - channels x frames 9 | % psi_s_stack target signal PSD - frames 10 | % H_stack RETF - channels (x sources) x frames (if more than one source) 11 | % Psi_w_tilde_init initial state estimation error correlation matrix 12 | % beta gain decay limitation 13 | % 14 | % OUT: 15 | % q_stack square root PSD estimate - sources x 1 16 | % Omega_hat estimate of unitary matrix - sources x sources 17 | % eps_phi_s_rel estimation error 18 | % eps_phi_s_rel_it estimation error per iteration 19 | 20 | % get dimensions 21 | if ismatrix(H_stack) 22 | N = 1; 23 | [M, numFrames] = size(H_stack); 24 | else 25 | [M, N, numFrames] = size(H_stack); 26 | end 27 | stateDim = size(Psi_w_tilde_init,1); 28 | 29 | % initKF 30 | % 31 | w_hat = zeros(stateDim,1); 32 | Psi_w_tile = Psi_w_tilde_init; 33 | % 34 | y_old = zeros(M,1); 35 | % 36 | u_LP = zeros(stateDim-M+1,1); 37 | % 38 | smooth_gain = 1; 39 | 40 | % init output 41 | q_stack = zeros(1, numFrames); 42 | e_prio_stack = zeros(1, numFrames); 43 | e_post_stack = zeros(1, numFrames); 44 | e_post_smooth_stack = zeros(1, numFrames); 45 | 46 | 47 | for i_frame = 1:numFrames 48 | 49 | %%% Load Data 50 | % 51 | y = y_stack(:,i_frame); 52 | psi_s = psi_s_stack(i_frame); 53 | if N == 1 54 | H = H_stack(:,i_frame); 55 | else 56 | H = H_stack(:,:,i_frame); 57 | end 58 | 59 | 60 | %%% Spatio-Temporal Pre-Processing 61 | % 62 | % compute g, B 63 | g = sum(H/(H'*H),2); 64 | Btmp = eye(M)-(H/(H'*H))*H'; 65 | B = Btmp(:,1:M-N); 66 | % update q, u 67 | q = g'*y; 68 | u_SC = B'*y; 69 | u_LP = [y_old; u_LP(1:end-M)]; 70 | u = [u_SC; u_LP]; 71 | 72 | 73 | %%% Kalman Filter 74 | % 75 | % time update: state estimate 76 | w_hat = A*w_hat; 77 | % time update: state estimation error correlation matrix 78 | Psi_w_tile = A'*Psi_w_tile*A + Psi_w_delta; 79 | % symmetrize (just in case, avoiding accuracy issues) 80 | Psi_w_tile = 0.5*(Psi_w_tile+Psi_w_tile'); 81 | % error 82 | e_prio_conj = conj(q) - u'*w_hat; 83 | % error PSD 84 | psi_e = real(u'*Psi_w_tile*u) + psi_s + eps; 85 | % Kalman gain 86 | k = Psi_w_tile*u/psi_e; 87 | % measurement update: state estimation error correlation matrix 88 | w_hat = w_hat + k*e_prio_conj; 89 | % measurement update: state estimation error correlation matrix 90 | Psi_w_tile = Psi_w_tile - k*(u'*Psi_w_tile); 91 | 92 | 93 | %%% Spectral Post Processing 94 | % 95 | % compute gains 96 | gain = psi_s/psi_e; 97 | smooth_gain = max(gain, beta*smooth_gain); 98 | % 99 | % apply gains 100 | e_post_conj = gain*e_prio_conj; 101 | e_post_smooth_conj = smooth_gain*e_prio_conj; 102 | 103 | 104 | %%% Save Data 105 | % 106 | % save 107 | q_stack(:,i_frame) = q; 108 | e_prio_stack(:,i_frame) = conj(e_prio_conj); 109 | e_post_stack(:,i_frame) = conj(e_post_conj); 110 | e_post_smooth_stack(:,i_frame) = conj(e_post_smooth_conj); 111 | % save previous mic. signal 112 | y_old = y; 113 | 114 | end 115 | 116 | end 117 | -------------------------------------------------------------------------------- /tools/estim/SQRT_PSD_RETF/corrmat/estim_corrmat.m: -------------------------------------------------------------------------------- 1 | function [Psi_x_smth, Psi_x_mean] = estim_corrmat(X, alpha) 2 | % [Psi_x_smth, Psi_x_mean] = estim_corrmat(X, alpha) 3 | % estimates correlation matrix. 4 | % 5 | % IN: 6 | % X STFT data - freqbins x frames x channels 7 | % alpha forgetting factor 8 | % 9 | % OUT 10 | % Psi_x_smth smooth correlation matrix estimate - freqbins x frames x channels x channels 11 | % Psi_x_mean mean correlation matrix estimate - freqbins x 1 x channels x channels 12 | 13 | % dimensions 14 | [N_half, L, M, ~] = size(X); 15 | 16 | %%% compute average PSD matrix 17 | if alpha ~= 1 18 | R_inst = zeros(N_half, L, M, M); 19 | for l = 1:L 20 | for k = 1:N_half 21 | x = squeeze(X(k,l,:)); 22 | R_inst(k,l,:,:) = shiftdim(x*x', -2); 23 | end 24 | end 25 | Psi_x_mean = sum(R_inst, 2)/L; 26 | else 27 | Psi_x_mean = zeros(N_half, 1, M, M); 28 | for l = 1:L 29 | for k = 1:N_half 30 | x = squeeze(X(k,l,:)); 31 | Psi_x_mean(k,1,:,:) = Psi_x_mean(k,1,:,:) + shiftdim(x*x', -2); 32 | end 33 | end 34 | Psi_x_mean = Psi_x_mean/L; 35 | end 36 | 37 | %%% compute smooth PSD matrix 38 | 39 | R_smth_tmp = eps*ones(N_half, 1, M, M); 40 | if alpha ~= 1 41 | Psi_x_smth = zeros(N_half, L, M, M); 42 | for l = 1:L 43 | R_smth_tmp = alpha*R_smth_tmp + (1-alpha)*R_inst(:,l,:,:); 44 | Psi_x_smth(:,l,:,:) = R_smth_tmp; 45 | end 46 | else 47 | Psi_x_smth = Psi_x_mean; 48 | end 49 | -------------------------------------------------------------------------------- /tools/estim/SQRT_PSD_RETF/corrmat/forget2tau.m: -------------------------------------------------------------------------------- 1 | function [ tau ] = forget2tau( forget, R_STFT, fs ) 2 | % [ tau ] = forget2tau( forget, R_STFT, fs ) 3 | % converts forgeting factor to time constant. 4 | % 5 | % IN: 6 | % forget forgetting factor 7 | % R_STFT frame shift 8 | % fs sampling frequency 9 | % 10 | % OUT: 11 | % tau time constant 12 | 13 | tau = -R_STFT./(fs*log(forget)); 14 | 15 | end 16 | 17 | -------------------------------------------------------------------------------- /tools/estim/SQRT_PSD_RETF/corrmat/tau2forget.m: -------------------------------------------------------------------------------- 1 | function [ forget ] = tau2forget( tau, R_STFT, fs ) 2 | % [ forget ] = tau2forget( tau, R_STFT, fs ) 3 | % converts forgeting factor to time constant. 4 | % 5 | % IN: 6 | % tau time constant 7 | % R_STFT frame shift 8 | % fs sampling frequency 9 | % 10 | % OUT: 11 | % forget forgetting factor 12 | 13 | forget = exp(-R_STFT./(fs*tau)); 14 | 15 | end 16 | 17 | -------------------------------------------------------------------------------- /tools/estim/SQRT_PSD_RETF/desmooth_GEVD.m: -------------------------------------------------------------------------------- 1 | function [P_STFT, lambda_STFT, lambda_LP_STFT] = desmooth_GEVD(Psi_x_STFT, Gamma_FT, varargin) 2 | % [P_STFT, lambda_STFT, lambda_LP_STFT] = desmooth_GEVD(Psi_x_STFT, Gamma_FT, varargin) 3 | % performs GEVD and subspace-based desmoothing. 4 | % 5 | % IN: 6 | % Psi_x_STFT correlation matrix - freqbins x frames x channels x channels 7 | % Gamma_FT diffuse coherence matrix - freqbins x 1 x channels x channels 8 | % 'lambdaMin', lambdaMin eigenvalue threshold 9 | % 'forgetPSD', forgetPSD forgetting factor for desmoothing 10 | % 11 | % OUT: 12 | % P_STFT eingevectors - freqbins x frames x channels x channels 13 | % lambda_STFT desmoothed eigenvalues - freqbins x frames x channels 14 | % lambda_LP_STFT smooth eigenvalues - freqbins x frames x channels 15 | 16 | 17 | % dimensions 18 | [N_FT_half, L, M, ~] = size(Psi_x_STFT); % number of frequency bins, frames, microphones 19 | 20 | % default options 21 | forgetPSD = 0; % PSD forgetting factor 22 | lambdaMin = 0; % minimum power 23 | 24 | % read options from input 25 | for i = 1:2:length(varargin) 26 | if ischar(varargin{i}) 27 | switch varargin{i} 28 | case 'forgetPSD' 29 | forgetPSD = varargin{i+1}; 30 | case 'lambdaMin' 31 | lambdaMin = varargin{i+1}; 32 | end 33 | end 34 | end 35 | 36 | % init 37 | P_STFT = zeros(N_FT_half,L,M,M); 38 | lambda_STFT = zeros(N_FT_half,L,M); 39 | lambda_LP_STFT = zeros(N_FT_half,L,M); 40 | 41 | for k=2:N_FT_half 42 | R_v = 0; 43 | 44 | % apply regularization 45 | Gamma = squeeze(Gamma_FT(k,1,:,:)); 46 | for l = 1:L 47 | 48 | %%% GEVD %%% 49 | % 50 | Psi_x = squeeze(Psi_x_STFT(k,l,:,:)); 51 | % generalized eigenvalue decomposition 52 | [P, Lambda_LP] = eig(Psi_x, Gamma); 53 | % ignore residual imaginary component and set negative values to zero 54 | lambda_LP = real(diag(Lambda_LP)); 55 | % rescaling W such that P'*Rv_k*P = I and P'*Ry_kp*P = Lambda 56 | P = P/diag(sqrt(real(diag(P'*Gamma*P)))); 57 | 58 | 59 | %%% Sorting eigenvalues/eigenvectors %%% 60 | % 61 | if l > 1 62 | % map current eigenvector order to previous one, start with principal eigenvector 63 | [~, maxIdx] = sort(lambda_LP, 'descend'); 64 | % if ordered correctly, Q approximates I 65 | Q = abs(P_old'*Gamma*P); 66 | % temporary variables 67 | P_tmp = P; 68 | lambda_LP_tmp = lambda_LP; 69 | % order such that Q approximates I 70 | for m = transpose(maxIdx) 71 | % find index 72 | [~, maxIdx] = max(Q(:,m)); 73 | % set corresponding row to zero (guarantees that maxIdx is unique in each iteration) 74 | Q(maxIdx,:) = 0; 75 | % sort P and Lambda 76 | P(:, maxIdx) = P_tmp(:,m); 77 | lambda_LP(maxIdx) = lambda_LP_tmp(m); 78 | end 79 | end 80 | P_old = P; 81 | % 82 | % save 83 | P_STFT(k,l,:,:) = shiftdim(P, -2); 84 | lambda_LP_STFT(k,l,:) = shiftdim(lambda_LP, -2); 85 | 86 | 87 | %%% Filter eigenvalues to compensate for recursive avaeraging %%% 88 | % 89 | if l > 1 90 | % apply HP to Lambda 91 | lambda_LP_old = squeeze(lambda_LP_STFT(k,l-1,:)); 92 | lambda = 1/(1-forgetPSD)*lambda_LP - forgetPSD/(1-forgetPSD)*lambda_LP_old; 93 | lambda(lambda < lambdaMin) = lambdaMin; 94 | else 95 | % use LP version 96 | lambda = lambda_LP; 97 | lambda(lambda < lambdaMin) = lambdaMin; 98 | end 99 | % 100 | % save 101 | lambda_STFT(k,l,:) = shiftdim(lambda, -2); 102 | 103 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 104 | % 105 | % NOTE: instead of desmoothing eigenvalues as in the implementation 106 | % above, one might prefer to use instantaneous eigenvalues, see 107 | % also [a] and the desmooth_GEVD() function in [b]. 108 | % 109 | % [a] T. Dietzen, M. Moonen, and T. van Waterschoot, 'Instantaneous 110 | % PSD estimation for speech enhancement based on generalized 111 | % principal components,' in Proc. 28th European Signal Process. 112 | % Conf. (EUSIPCO 2020), Amsterdam, Netherlands, Jan 2021, pp. 1-5. 113 | % 114 | % [b] https://github.com/tdietzen/INSTANT-PSD 115 | % 116 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 117 | 118 | end 119 | end 120 | 121 | end 122 | -------------------------------------------------------------------------------- /tools/estim/SQRT_PSD_RETF/estim_PSD_RETF.m: -------------------------------------------------------------------------------- 1 | function [phi_s_hat_STFT, phi_d_STFT, H_hat_prior_STFT, H_hat_post_STFT, update_RETF] = estim_PSD_RETF(P_STFT, lambda_STFT, Gamma_FT, H_init_FT, varargin) 2 | % function [phi_s_hat_STFT, phi_d_STFT, H_hat_prior_STFT, H_hat_post_STFT, update_RETF] = estim_PSD_RETF(P_STFT, lambda_STFT, Gamma_FT, H_init_FT, varargin) 3 | % estimates PSDs and updates RETFs. 4 | % 5 | % IN: 6 | % P_STFT eigenvectors - freqbins x frames x channels x channels 7 | % lambda_STFT eigenvalues - freqbins x frames x channels 8 | % Gamma_FT diffuse coherence matrix - freqbins x 1 x frames x channels 9 | % H_init_FT initial RETF estimate - freqbins x frames x channels x sources 10 | % 'itmax', itmax maximum number of iterations 11 | % 'phiMin', phiMin power threshold 12 | % 'method', method {'conventional MP', 'square-root MP'}, defines estimation method 13 | % 'alpha', alpha penalty factor conventional and square-root MP 14 | % 'beta', beta penalty factor in RETF update MP 15 | % 'xiThresh', xi_thresh threshold for RETF update 16 | % 17 | % OUT: 18 | % phi_s_hat_STFT early PSD estimates - freqbins x frames x sources 19 | % phi_d_STFT diffuse PSD estimate - freqbins x frames 20 | % H_hat_prior_STFT prior RETF estimate - freqbins x frames x channels x sources 21 | % H_hat_post_STFT posterior RETF estimate - freqbins x frames x channels x sources 22 | % update_RETF RETF update pattern - freqbins x frames x sources 23 | 24 | % dimensions 25 | [N_FT_half, L, M] = size(lambda_STFT); % number of frequency bins, frames, microphones 26 | N = size(H_init_FT, 4); % number of sources 27 | 28 | % default values 29 | method = 'square-root MP'; 30 | itmax = 20; % max iterations 31 | phiMin = db2pow(-80); % minimum power threshold 32 | beta = 1e3*ones(N_FT_half,1); % beta 33 | alpha = 1e3*ones(N_FT_half,1); % alpha 34 | 35 | % parse name-value pairs 36 | for i = 1:2:length(varargin) 37 | if ischar(varargin{i}) 38 | switch varargin{i} 39 | case 'itmax' 40 | itmax = varargin{i+1}; 41 | case 'phiMin' 42 | phiMin = varargin{i+1}; 43 | case 'method' 44 | method = varargin{i+1}; 45 | case 'alpha' 46 | alpha = varargin{i+1}; 47 | case 'beta' 48 | beta = varargin{i+1}; 49 | case 'xiThresh' 50 | xi_thresh = varargin{i+1}; 51 | end 52 | end 53 | end 54 | 55 | % init 56 | phi_s_hat_STFT = zeros(N_FT_half,L,N); 57 | phi_d_STFT = zeros(N_FT_half,L); 58 | 59 | if strcmp('method','square-root MP') 60 | H_hat_prior_STFT = zeros(N_FT_half,L,M,N); 61 | H_hat_post_STFT = zeros(N_FT_half,L,M,N); 62 | update_RETF = zeros(N_FT_half,L,N); 63 | else 64 | H_hat_prior_STFT = []; 65 | H_hat_post_STFT = []; 66 | update_RETF = []; 67 | end 68 | 69 | 70 | for k=2:N_FT_half 71 | 72 | % get H 73 | H_hat = squeeze(H_init_FT(k,1,:,:)); 74 | H_hat_post = H_hat; 75 | 76 | % get Gamma 77 | Gamma = squeeze(Gamma_FT(k,1,:,:)); 78 | 79 | for l = 1:L 80 | 81 | % get P and lambda 82 | P = squeeze(P_STFT(k,l,:,:)); 83 | lambda = squeeze(lambda_STFT(k,l,:)); 84 | 85 | %%% Decompose into early and late part %%% 86 | % 87 | % find index of maximum eigenvalues and principal eigenvectors 88 | [~, maxIdx] = sort(lambda, 'descend'); 89 | Pmax = P(:,maxIdx(1:N)); 90 | lambdaMax = lambda(maxIdx(1:N)); 91 | lambdaMin = lambda(maxIdx(N+1:end)); 92 | % 93 | % compute late reverberant PSD 94 | phi_d = mean(lambdaMin); % using min or mean doesn't make much of a difference 95 | % 96 | % save 97 | phi_d_STFT(k,l) = phi_d; 98 | % 99 | % compute lambda_xe 100 | lambda_xe = lambdaMax - phi_d; 101 | lambda_xe(lambda_xe < phiMin) = phiMin; 102 | 103 | 104 | %%% early correlation matrix %%% 105 | % 106 | sqrtlambda_xe = sqrt(lambda_xe); 107 | sqrtPsi_xe = Gamma*Pmax*diag(sqrtlambda_xe); 108 | Psi_xe = sqrtPsi_xe*sqrtPsi_xe'; 109 | 110 | 111 | switch method 112 | 113 | case 'conventional MP' 114 | 115 | % solve 116 | phi_s_hat = solve_convMP(H_hat, Psi_xe, phiMin, alpha(k), itmax); 117 | 118 | case 'square-root MP' 119 | 120 | % propagate H_hat_post 121 | H_hat_prior = H_hat_post; 122 | 123 | % initial value 124 | sqrtphi_s_init = sqrt(solve_convMP_simple(H_hat_prior, Psi_xe, phiMin)); 125 | 126 | % solve 127 | [sqrtphi_s_hat, Omega_hat] = solve_sqrtMP(sqrtPsi_xe, H_hat_prior, sqrtphi_s_init, alpha(k), itmax); 128 | phi_s_hat = sqrtphi_s_hat.*conj(sqrtphi_s_hat); 129 | 130 | %%% update RETF H %%% 131 | if l > 16 132 | % update H 133 | phi_reg = phi_d + 1e-3; 134 | [H_hat_post, up] = solve_RETFup(sqrtPsi_xe, sqrtphi_s_hat, Omega_hat, xi_thresh, phi_reg, beta(k), H_hat_prior); 135 | 136 | else 137 | H_hat_post = H_hat_prior; 138 | up = zeros(N,1); 139 | end 140 | 141 | % save 142 | H_hat_prior_STFT(k,l,:,:) = shiftdim(H_hat_prior, -2); 143 | H_hat_post_STFT(k,l,:,:) = shiftdim(H_hat_post, -2); 144 | update_RETF(k,l,:) = up; 145 | end 146 | 147 | % save 148 | phi_s_hat_STFT(k,l,:) = phi_s_hat; 149 | 150 | end 151 | end 152 | 153 | 154 | end -------------------------------------------------------------------------------- /tools/estim/SQRT_PSD_RETF/minprob/solve_RETFup.m: -------------------------------------------------------------------------------- 1 | function [H_hat_post, up, rho_H_prior_rel, rho_H_post_rel] = solve_RETFup( sqrtPsi_xe, sqrtphi_s_hat, Omega_hat, xiThresh, reg, beta, H_hat_prior, H) 2 | % [H_hat_post, up, rho_H_prior_rel, rho_H_post_rel] = solve_RETFup( sqrtPsi_xe, sqrtphi_s_hat, Omega_hat, xiThresh, reg, beta, H_hat_prior, H) 3 | % solves RETF update MP. 4 | % 5 | % IN: 6 | % sqrtPsi_xe square root of correlation matrix - channels x sources 7 | % sqrtphi_s_hat square root PSD estimate - sources x 1 8 | % Omega_hat estimate of unitary matrix - sources x sources 9 | % xiThresh update threshold 10 | % reg threshold regularization 11 | % beta penalty factor 12 | % H_hat_prior prior RETFs - channels x sources 13 | % H ground truth RETF (optional) 14 | % 15 | % OUT: 16 | % H_hat_post posterior RETFs - channels x sources 17 | % up RETF update pattern - channels x sources 18 | % rho_H_prior_rel prior estimation error 19 | % rho_H_post_rel posterior estimation error 20 | 21 | [M, N] = size(H_hat_prior); 22 | 23 | % init 24 | H_hat_post = ones(M,N); 25 | 26 | % update 27 | phi_s_hat = real(diag(sqrtphi_s_hat')*sqrtphi_s_hat); 28 | xi = phi_s_hat/(sum(phi_s_hat) + reg); 29 | 30 | up = zeros(N,1); 31 | for n = 1:N 32 | if xi(n) > xiThresh 33 | up(n) = 1; 34 | H_hat_post(2:M,n) = (sqrtPsi_xe(2:M,:)*Omega_hat(:,n)*conj(sqrtphi_s_hat(n)) + beta*H_hat_prior(2:M,n))/(phi_s_hat(n) + beta); 35 | else 36 | up(n) = 0; 37 | H_hat_post(2:M,n) = H_hat_prior(2:M,n); 38 | end 39 | end 40 | 41 | 42 | 43 | 44 | 45 | %if nargin < 6 46 | if nargin < 8 47 | rho_H_prior_rel = []; 48 | rho_H_post_rel = []; 49 | else 50 | % relative error 51 | H_e_prior = H - H_hat_prior; 52 | H_e_post = H - H_hat_post; 53 | 54 | rho_H_prior_rel = trace(H_e_prior'*H_e_prior)/trace(H(2:M,:)'*H(2:M,:)); 55 | rho_H_post_rel = trace(H_e_post'*H_e_post)/trace(H(2:M,:)'*H(2:M,:)); 56 | end 57 | 58 | end 59 | -------------------------------------------------------------------------------- /tools/estim/SQRT_PSD_RETF/minprob/solve_convMP.m: -------------------------------------------------------------------------------- 1 | function [phi_s_hat, eps_phi_s_rel] = solve_convMP(H_hat, Psi_xe, phiMin, alpha, itMax, phi_s) 2 | % function [phi_s_hat, eps_phi_s_rel] = solve_convMP(H_hat, Psi_xe, phiMin, alpha, itMax, phi_s) 3 | % solves conventional MP. 4 | % 5 | % IN: 6 | % H_hat RETFs - channels x sources 7 | % Psi_xe correlation matrix - channels x channels 8 | % phiMin PSD threshold 9 | % alpha penalty factor 10 | % itMax max number of iterations 11 | % phi_s ground truth PSD estimates (optional) - sources x 1 12 | % 13 | % OUT: 14 | % phi_s_hat PSD estimate - sources x 1 15 | % eps_phi_s_rel estimation error 16 | 17 | 18 | N = size(H_hat,2); 19 | 20 | % cost function terms 21 | A1 = abs(H_hat'*H_hat).^2; 22 | A2 = alpha*ones(N); 23 | A = A1 + A2; 24 | b = -real(diag(H_hat'*Psi_xe*H_hat) + alpha*Psi_xe(1,1)*ones(N,1)); 25 | 26 | % initial value 27 | R = 1e-8*(trace(A1)/N)*eye(N); 28 | x0 = -(A + R)\b; 29 | 30 | % gradient 31 | G = @(x) (x'*A)'+b; 32 | 33 | % step size 34 | mu = 1/norm(A); 35 | 36 | for it = 1:itMax 37 | 38 | xOld = x0; 39 | % compute gradient 40 | Gtemp = G(x0); 41 | % take gradient step 42 | xstep = x0-mu*Gtemp; 43 | % apply proximal opetor 44 | xProj = max(xstep,phiMin); 45 | % overwrite 46 | x0 = xProj; 47 | 48 | if norm(x0-xOld)/norm(xOld)<1e-6 49 | break; 50 | end 51 | 52 | end 53 | 54 | phi_s_hat = x0; 55 | 56 | 57 | if nargin < 6 58 | eps_phi_s_rel = []; 59 | else 60 | % error 61 | e_phi_s = sqrt(phi_s) - sqrt(phi_s_hat); 62 | eps_phi_s_rel = e_phi_s'*e_phi_s/sum(phi_s); 63 | end 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | end -------------------------------------------------------------------------------- /tools/estim/SQRT_PSD_RETF/minprob/solve_convMP_simple.m: -------------------------------------------------------------------------------- 1 | function [phi_s_hat, eps_phi_s_rel] = solve_convMP_simple(H_hat, Psi_xe, phiMin, phi_s) 2 | % function [phi_s_hat, eps_phi_s_rel] = solve_convMP_simple(H_hat, Psi_xe, phiMin, phi_s) 3 | % solves simple conventional MP. 4 | % 5 | % IN: 6 | % H_hat RETFs - channels x sources 7 | % Psi_xe correlation matrix - channels x channels 8 | % phiMin PSD threshold 9 | % phi_s ground truth PSD estimates (optional) - sources x 1 10 | % 11 | % OUT: 12 | % phi_s_hat PSD estimate - sources x 1 13 | % eps_phi_s_rel estimation error 14 | 15 | N = size(H_hat, 2); 16 | 17 | % cost function terms 18 | A = abs(H_hat'*H_hat).^2; 19 | b = -real(diag(H_hat'*Psi_xe*H_hat)); 20 | 21 | R = 1e-8*(trace(A)/N)*eye(N); 22 | phi_s_hat = max(-(A + R)\b, phiMin); 23 | 24 | if nargin < 4 25 | eps_phi_s_rel = []; 26 | else 27 | % error 28 | e_phi_s = sqrt(phi_s) - sqrt(phi_s_hat); 29 | eps_phi_s_rel = e_phi_s'*e_phi_s/sum(phi_s); 30 | end 31 | 32 | end 33 | 34 | -------------------------------------------------------------------------------- /tools/estim/SQRT_PSD_RETF/minprob/solve_sqrtMP.m: -------------------------------------------------------------------------------- 1 | function [sqrtphi_s_hat, Omega_hat, eps_phi_s_rel, eps_phi_s_rel_it] = solve_sqrtMP(sqrtPsi_xe, H_hat, sqrtphi_s_init, alpha, itMax, phi_s) 2 | % [sqrtphi_s_hat, Omega_hat, eps_phi_s_rel, eps_phi_s_rel_it] = solve_sqrtMP(sqrtPsi_xe, H_hat, sqrtphi_s_init, alpha, itMax, phi_s) 3 | % solves square-root MP. 4 | % 5 | % IN: 6 | % sqrtPsi_xe square root of correlation matrix - channels x sources 7 | % H_hat RETFs - channels x sources 8 | % sqrtphi_s_init intital square root PSD estimate - sources x 1 9 | % alpha penalty factor 10 | % itMax max number of iterations 11 | % phi_s ground truth PSD estimates (optional) - sources x 1 12 | % 13 | % OUT: 14 | % phi_s_hat square root PSD estimate - sources x 1 15 | % Omega_hat estimate of unitary matrix - sources x sources 16 | % eps_phi_s_rel estimation error 17 | % eps_phi_s_rel_it estimation error per iteration 18 | 19 | % initial value 20 | sqrtphi_s_hat = sqrtphi_s_init; 21 | 22 | % number of sources 23 | [M, N] = size(H_hat); 24 | 25 | 26 | if nargin < 6 27 | compute_eps = 0; 28 | else 29 | compute_eps = 1; 30 | end 31 | 32 | if compute_eps 33 | eps_phi_s_rel_it = zeros(itMax, 1); 34 | else 35 | eps_phi_s_rel_it = []; 36 | eps_phi_s_rel = []; 37 | end 38 | 39 | for i_it = 1:itMax 40 | 41 | % save convergence plot 42 | if compute_eps 43 | e_phi_s = sqrt(phi_s) - abs(sqrtphi_s_hat); 44 | eps_phi_s_rel_it(i_it) = e_phi_s'*e_phi_s/sum(phi_s); 45 | end 46 | 47 | % get Sigma 48 | [U, ~, V] = svd(sqrtPsi_xe'*H_hat*diag(sqrtphi_s_hat)); 49 | 50 | Omega_hat = U*V'; 51 | 52 | % get sqrtphi_xe_hat 53 | sqrtphi_s_hat_old = sqrtphi_s_hat; 54 | 55 | sqrtphi_s_hat = (diag(H_hat'*sqrtPsi_xe*Omega_hat) + alpha*Omega_hat'*sqrtPsi_xe(1,:)')./... 56 | (diag((H_hat'*H_hat)) + alpha*ones(N,1)); 57 | 58 | % break condition 59 | delta_sqrtphi_s_hat = abs(sqrtphi_s_hat) - abs(sqrtphi_s_hat_old); 60 | if delta_sqrtphi_s_hat'*delta_sqrtphi_s_hat/(sqrtphi_s_hat_old'*sqrtphi_s_hat_old) < 1e-6 61 | % save convergence plot and break 62 | if compute_eps 63 | eps_phi_s_rel_it(i_it+1:end) = eps_phi_s_rel_it(i_it); 64 | end 65 | break; 66 | end 67 | end 68 | 69 | if compute_eps 70 | eps_phi_s_rel = eps_phi_s_rel_it(end); 71 | end 72 | 73 | end 74 | 75 | -------------------------------------------------------------------------------- /tools/plot/plotSpec.m: -------------------------------------------------------------------------------- 1 | function plotSpec(spec, magPowLin, xTickProp, yTickProp, cRange, plotColorbar) 2 | % plotSpec(spec, magPowLin, xTickProp, yTickProp, cRange, plotColorbar) 3 | % plots spectrogram. 4 | % 5 | % IN: 6 | % spec spectrogram data - freqbins x frames 7 | % magPowLin {'mag', 'pow', 'lin'}, scaling option, plot logarithmic magnitudes, logarithmic powers, or linear 8 | % xTickProp [xTick start, xTick resolution, xTick distance] 9 | % xTickProp [yTick start, yTick resolution, yTick distance] 10 | % cRange range of colorbar 11 | % plotColorbar plot colorbar if 1 12 | 13 | % plot spectrogram 14 | switch magPowLin 15 | case 'mag' 16 | imagesc(mag2db(abs(spec)), cRange); 17 | case 'pow' 18 | imagesc(pow2db(abs(spec)), cRange); 19 | case 'lin' 20 | imagesc(spec, cRange); 21 | otherwise 22 | error('undefined scaling option.') 23 | end 24 | set(gca,'Ydir','normal'); 25 | set(gca,'TickLength',[0 0]); 26 | 27 | % add colorbar 28 | if plotColorbar 29 | colorbar; 30 | end 31 | 32 | % add ticks 33 | if ~isempty(xTickProp) 34 | xStartValue = xTickProp(1); 35 | xRes = xTickProp(2); 36 | xTickDist = xTickProp(3); 37 | xTicks = 1:xTickDist:size(spec,2); 38 | xTickLabels = cellstr(num2str((xStartValue + (xTicks-1)*xRes).')); 39 | set(gca,'xTick',xTicks) 40 | set(gca,'xTickLabel',xTickLabels); 41 | else 42 | set(gca,'xTick',[]) 43 | end 44 | 45 | if ~isempty(yTickProp) 46 | yStartValue = yTickProp(1); 47 | yRes = yTickProp(2); 48 | ytickDist = yTickProp(3); 49 | yTicks = 1:ytickDist:size(spec,1); 50 | yTickLabels = cellstr(num2str((yStartValue + (yTicks-1)*yRes).')); 51 | set(gca,'yTick',yTicks); 52 | set(gca,'yTickLabel',yTickLabels); 53 | else 54 | set(gca,'yTick',[]); 55 | end 56 | 57 | end -------------------------------------------------------------------------------- /tools/spatial/calc_diffcoherence.m: -------------------------------------------------------------------------------- 1 | function Gamma = calc_diffcoherence(micPos,N_STFT,fs,c,reg,type) 2 | % Gamma = calc_diffcoherence(micPos,N_STFT,fs,c,reg,type) 3 | % calculates diffuse coherence matrix. 4 | % 5 | % IN: 6 | % micPos microphone positions - channels x coordinates 7 | % N_STFT STFT frame length 8 | % fs sampling rate 9 | % c speed of sound 10 | % reg regularization (avoids ill-conditioned matrices at very low frequencies) 11 | % type {'spherical', 'cylindrical'}, coherence type 12 | % 13 | % OUT: 14 | % Gamma diffuse coherence matrix - freqbins x 1 x channels x channels 15 | 16 | if nargin<6 17 | type = 'spherical'; 18 | end 19 | 20 | N_STFT_half = N_STFT/2 + 1; 21 | f = linspace(0,fs/2,N_STFT_half); 22 | 23 | M = size(micPos,1); 24 | Gamma = (1+reg)*ones(N_STFT_half,1,M,M); 25 | 26 | for m_out = 1:M-1 27 | for m_in = m_out+1:M 28 | d = norm(micPos(m_out,:)-micPos(m_in,:)); 29 | switch type 30 | case 'spherical' 31 | Gamma(:,1,m_out,m_in) = sinc(2*f*d/c); 32 | case 'cylindrical' 33 | Gamma(:,1,m_out,m_in) = besselj(0, 2*pi*f*d/c); 34 | end 35 | Gamma(:,1,m_in,m_out) = Gamma(:,1,m_out,m_in); 36 | end 37 | end 38 | -------------------------------------------------------------------------------- /tools/spatial/doa2steervec.m: -------------------------------------------------------------------------------- 1 | function H = doa2steervec(micPos, sourceAng, N_FT_half, fs, c) 2 | % H = doa2steervec(micPos, sourceAng, N_FT_half, fs, c) 3 | % converts DoAs in steering vectors. 4 | % 5 | % IN: 6 | % micPos microphone positions - channels x coordinates 7 | % sourceAng DoA angles of sources 8 | % fs sampling frequency 9 | % c speed of sound 10 | % 11 | % OUT: 12 | % H steering vectors - freqbins x 1 x channels x sources 13 | 14 | 15 | M = size(micPos, 1); 16 | N_src = length(sourceAng); 17 | 18 | H = zeros(N_FT_half, 1, M, N_src); 19 | f = (0:N_FT_half-1)*fs/(2*(N_FT_half-1)); 20 | 21 | d = sqrt(sum((micPos-repmat(micPos(1,:), [M, 1])).^2, 2)); 22 | 23 | 24 | for n = 1:N_src 25 | for k = 1:N_FT_half 26 | delay = sin(deg2rad(sourceAng(n)))*d/c; 27 | % h(k,1,:,n) = exp(1i*2*pi*f(k)*delay); 28 | H(k,1,:,n) = exp(-1i*2*pi*f(k)*delay); 29 | end 30 | end --------------------------------------------------------------------------------