estnoisem.m

语音处理工具箱

function [x,zo,xs]=estnoisem(yf,tz,pp)
%ESTNOISEM - estimate noise spectrum using minimum statistics
% Inputs:
%   yf      input power spectra (one row per frame)
%   tz      frame increment in seconds
%           Alternatively, the input state from a previous call (see below)
%   pp      algorithm parameters [optional]
%
% Outputs:
%   x       estimated noise power spectra (one row per frame)
%   zo      output state
%   xs      estimated std error of x (one row per frame)
%           xs seems often to be an underestimate by a factor of 2 or 3
%
% The algorithm parameters are defined in reference [1] from which equation
% numbers are given in parentheses. They are as follows:
%
%        pp.taca      % (11): smoothing time constant for alpha_c [0.0449 seconds]
%        pp.tamax     % (3): max smoothing time constant [0.392 seconds]
%        pp.taminh    % (3): min smoothing time constant (upper limit) [0.0133 seconds]
%        pp.tpfall    % (12): time constant for P to fall [0.064 seconds]
%        pp.tbmax     % (20): max smoothing time constant [0.0717 seconds]
%        pp.qeqmin    % (23): minimum value of Qeq [2]
%        pp.qeqmax    % max value of Qeq per frame [14]
%        pp.av        % (23)+13 lines: fudge factor for bc calculation  [2.12]
%        pp.td        % time to take minimum over [1.536 seconds]
%        pp.nu        % number of subwindows to use [3]
%        pp.qith      % Q-inverse thresholds to select maximum noise slope [0.03 0.05 0.06 Inf ]
%        pp.nsmdb     % corresponding noise slope thresholds in dB/second   [47 31.4 15.7 4.1]
%
% Example use:      y=enframe(s,w,ni);                  % divide speech signal s(n) into
%                                                       % overlapping frames using window w(n)
%                   yf=rfft(y,nf,2);                    % take fourier transform
%                   dp=estnoisem(yf.*conj(yf),tinc);    % estimate the noise
%
% If convenient, you can call estnoisem in chunks of arbitrary size. Thus the following are equivalent:
%
%                   (a) dp=estnoisem(yp(1:300),tinc);
%
%                   (b) [dp(1:100),z]=estnoisem(yp(1:100),tinc);
%                       [dp(101:200),z]=estnoisem(yp(101:200),z);
%                       [dp(201:300),z]=estnoisem(yp(201:300),z);


% This is intended to be a precise implementation of [1] with Table III
% replaced by the updated table 5 from [2]. The only deliberate algorithm
% change is the introduction of a minimum value for 1/Qeq in equation (23).
% This change only affects the first few frames and improves the
% convergence of the algorithm. A minor improveemnt was reported in [3] but
% this has not yet been included.
%
% Refs:
%    [1] Rainer Martin.
%        Noise power spectral density estimation based on optimal smoothing and minimum statistics.
%        IEEE Trans. Speech and Audio Processing, 9(5):504-512, July 2001.
%    [2] Rainer Martin.
%        Bias compensation methods for minimum statistics noise power spectral density estimation
%        Signal Processing, 2006, 86, 1215-1229
%    [3] Dirk Mauler and Rainer Martin
%        Noise power spectral density estimation on highly correlated data
%        Proc IWAENC, 2006

%	   Copyright (C) Mike Brookes 2008
%      Version: $Id: estnoisem.m,v 1.1 2008/05/22 17:17:02 dmb Exp $
%
%   VOICEBOX is a MATLAB toolbox for speech processing.
%   Home page: http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%   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 can obtain a copy of the GNU General Public License from
%   http://www.gnu.org/copyleft/gpl.html or by writing to
%   Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[nr,nrf]=size(yf);          % number of frames and freq bins
x=zeros(nr,nrf);            % initialize output arrays
xs=zeros(nr,nrf);           % will hold std error in the future
if isempty(yf) && isstruct(tz)             % no real data
    zo=tz;              % just keep the same state
else
    if isstruct(tz)       % take parameters from a previous call
        nrcum=tz.nrcum;
        p=tz.p;          % smoothed power spectrum
        ac=tz.ac;               % correction factor (9)
        sn2=tz.sn2;              % estimated noise power
        pb=tz.pb;               % smoothed noisy speech power (20)
        pb2=tz.pb2;
        pminu=tz.pminu;
        actmin=tz.actmin;   % Running minimum estimate
        actminsub=tz.actminsub;           % sub-window minimum estimate
        subwc=tz.subwc;                   % force a buffer switch on first loop
        actbuf=tz.actbuf;  % buffer to store subwindow minima
        ibuf=tz.ibuf;
        lminflag=tz.lminflag;      % flag to remember local minimum
        tinc=tz.tinc;     % frame increment
        qq=tz.qq;         % parameter structure
    else
        tinc = tz;          % second argument is frame increment
        nrcum=0;            % no frames so far
        % default algorithm constants

        qq.taca=0.0449;    % smoothing time constant for alpha_c = -tinc/log(0.7) in equ (11)
        qq.tamax=0.392;    % max smoothing time constant in (3) = -tinc/log(0.96)
        qq.taminh=0.0133;    % min smoothing time constant (upper limit) in (3) = -tinc/log(0.3)
        qq.tpfall=0.064;   % time constant for P to fall (12)
        qq.tbmax=0.0717;   % max smoothing time constant in (20) = -tinc/log(0.8)
        qq.qeqmin=2;       % minimum value of Qeq (23)
        qq.qeqmax=14;      % max value of Qeq per frame
        qq.av=2.12;             % fudge factor for bc calculation (23 + 13 lines)
        qq.td=1.536;       % time to take minimum over
        qq.nu=8;           % number of subwindows
        qq.qith=[0.03 0.05 0.06 Inf]; % noise slope thresholds in dB/s
        qq.nsmdb=[47 31.4 15.7 4.1];

        if nargin>=3 && ~isempty(pp)
            qqn=fieldnames(qq);
            for i=1:length(qqn)
                if isfield(pp,qqn{i})
                    qq.(qqn{i})=pp.(qqn{i});
                end
            end
        end
    end

    % unpack parameter structure

    taca=qq.taca;    % smoothing time constant for alpha_c = -tinc/log(0.7) in equ (11)
    tamax=qq.tamax;    % max smoothing time constant in (3) = -tinc/log(0.96)
    taminh=qq.taminh;    % min smoothing time constant (upper limit) in (3) = -tinc/log(0.3)
    tpfall=qq.tpfall;   % time constant for P to fall (12)
    tbmax=qq.tbmax;   % max smoothing time constant in (20) = -tinc/log(0.8)
    qeqmin=qq.qeqmin;       % minimum value of Qeq (23)
    qeqmax=qq.qeqmax;      % max value of Qeq per frame
    av=qq.av;             % fudge factor for bc calculation (23 + 13 lines)
    td=qq.td;       % time to take minimum over
    nu=qq.nu;           % number of subwindows
    qith=qq.qith; % noise slope thresholds in dB/s
    nsmdb=qq.nsmdb;   % maximum permitted +ve noise slope in dB/s

    % derived algorithm constants

    aca=exp(-tinc/taca); % smoothing constant for alpha_c in equ (11) = 0.7
    acmax=aca;          % min value of alpha_c = 0.7 in equ (11) also = 0.7
    amax=exp(-tinc/tamax); % max smoothing constant in (3) = 0.96
    aminh=exp(-tinc/taminh); % min smoothing constant (upper limit) in (3) = 0.3
    bmax=exp(-tinc/tbmax); % max smoothing constant in (20) = 0.8
    snrexp = -tinc/tpfall;
    nv=round(td/(tinc*nu));    % length of each subwindow in frames
    if nv<4            % algorithm doesn't work for miniscule frames
        nv=4;
        nu=max(round(td/(tinc*nv)),1);
    end
    nd=nu*nv;           % length of total window in frames
    [md,hd]=mhvals(nd); % calculate the constants M(D) and H(D) from Table III
    [mv,hv]=mhvals(nv); % calculate the constants M(D) and H(D) from Table III