Matlab v_specsub函数（谱减法）参数详解（英文附例）

Matlab v_specsub函数参数详解（英文附例）

需下载voicebox安装至matlab路径下

v_specsubm.m更新为v_specsub.m，详情参见函数源文件。

函数解释

function [ss,gg,tt,ff,zo]=v_specsub(si,fsz,pp)

%V_SPECSUB performs speech enhancement using spectral subtraction [SS,ZO]=(S,FSZ,P)
%
% Usage: (1) y=v_specsub(x,fs); % enhance the speech using default parameters
%
% Inputs:
% si: input speech signal
% fsz: sample frequency in Hz
% Alternatively, the input state from a previous call (see below)
% pp: algorithm parameters [optional]
%
% Outputs:
% ss: output enhanced speech
% gg(t,f,i): selected time-frequency values (see pp.tf below)
% tt: centre of frames (in seconds)
% ff: centre of frequency bins (in Hz)
% zo: output state (or the 2nd argument if gg,tt,ff are omitted)
%
% The algorithm operation is controlled by a small number of parameters:
%
% pp.of % overlap factor = (fft length)/(frame increment) [2]
% pp.ti % desired frame increment [0.016 seconds]
% pp.ri % set to 1 to round ti to the nearest power of 2 samples [0]
% pp.g % subtraction domain: 1=magnitude, 2=power [1]
% pp.e % gain exponent [1]
% pp.am % max oversubtraction factor [3]
% pp.b % max noise attenutaion in power domain [0.01]
% pp.al % SNR for oversubtraction=am (set this to Inf for fixed a) [-5 dB]
% pp.ah % SNR for oversubtraction=1 [20 dB]
% pp.ne % noise estimation: 0=min statistics, 1=MMSE [0]
% pp.bt % threshold for binary gain or -1 for continuous gain [-1]
% pp.mx % input mixture gain [0]
% pp.gh % maximum gain for noise floor [1]
% pp.rf % round output signal to an exact number of frames [0]
% pp.tf % selects time-frequency planes to output in the gg() variable [‘g’]
% ‘i’ = input power spectrum
% ‘I’ = input complex spectrum
% ‘n’ = noise power spectrum
% ‘g’ = gain
% ‘o’ = output power spectrum
% ‘O’ = output complex spectrum
%
% Following [1], the magnitude-domain gain in each time-frequency bin is given by
% gain=mx+(1-mx)max((1-(aN/X)^(g/2))(e/g),min(gh,(b*N/X)^(e/2)))
% where N and X are the powers of the noise and noisy speech respectively.
% The oversubtraction factor varies linearly between a=am for a frame SNR of al down to
% a=1 for a frame SNR of ah. To obtain a fixed value of a for all values of SNR, set al=Inf.
% Common exponent combinations are:
% g=1 e=1 Magnitude Domain spectral subtraction
% g=2 e=1 Power Domain spectral subtraction
% g=2 e=2 Wiener filtering
% Many authors use the parameters alpha=a^(g/2), beta=b^(g/2) and gamma2=e/g instead of a, b and e
% but this increases interdependence amongst the parameters.
% If bt>=0 then the max(…) expression above is thresholded to become 0 or 1.
%
% In addition it is possible to specify parameters for the noise estimation algorithm
% which implements reference [2] or [3] according to the setting of pp.ne
%
% Minimum statistics noise estimate [2]: pp.ne=0
% 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]
%
% MMSE noise estimate [3]: pp.ne=1
% pp.tax % smoothing time constant for noise power estimate 0.0717 seconds
% pp.tap % smoothing time constant for smoothed speech prob 0.152 seconds
% pp.psthr % threshold for smoothed speech probability [0.99] (24)
% pp.pnsaf % noise probability safety value [0.01] (24)
% pp.pspri % prior speech probability [0.5] (18)
% pp.asnr % active SNR in dB [15] (18)
% pp.psini % initial speech probability [0.5] (23)
% pp.tavini % assumed speech absent time at start [0.064 seconds]
%
% If convenient, you can call v_specsub in chunks of arbitrary size. Thus the following are equivalent:

    (a) y=v_specsub(s,fs);
%
%   (b) [y1,z]=v_specsub(s(1:1000),fs);
%       [y2,z]=v_specsub(s(1001:2000),z);
%        y3=v_specsub(s(2001:end),z);
%        y=[y1; y2; y3];

%
% If the number of output arguments is either 2 or 5, the last partial frame of samples will
% be retained for overlap adding with the output from the next call to v_specsub().
%
% See also v_ssubmmse() for an alternative gain function

Refs: [1] M. Berouti, R. Schwartz and J. Makhoul Enhancement of speech
corrupted by acoustic noise Proc IEEE ICASSP, 1979, 4, 208-211 [2]
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. [3] Gerkmann, T. & Hendriks, R.
C.Unbiased MMSE-Based Noise Power Estimation With Low Complexity and
Low Tracking Delay IEEE Trans Audio, Speech, Language Processing,2012, 20, 1383-1393

Copyright © Mike Brookes 2004
Version: $Id: v_specsub.m 10865 2018-09-21 17:22:45Z dmb $
VOICEBOX is a MATLAB toolbox for speech processing.
Home page: http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html