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frequency warping matlab code

Introduction

WarpTB is a MATLAB toolbox for frequency-warped signal processing. Practically any signal processing algorithm can be warped by replacing all the unit delay elements by first order allpass blocks. Frequency-warping changes the frequency resolution of the system. Using a suitable value for a warping coefficient LAMBDA, the frequency-resolution of the system approximates closely the frequency resolution of human auditory system. This makes frequency-warped signal processing techniques beneficial in many speech and audio signal processing applications.
If you need more information about frequency warping browse to our publications page http://acoustics.hut.fi/publications/papers.html, where you can find several papers about the topic. In particular, many of the design examples in this toolbox are related to experiments reported in [1] and [2].

WarpTB consists of a basic toolkit and a set of optional examples and sub-toolkits for specific applications. The toolbox is free and it is available at http://acoustics.hut.fi/software/warp.

Copyrights: Aki Härmä and Matti Karjalainen, Helsinki University of Technology, Department of Signal Processing and Acoustics (former Laboratory of Acoustics and Audio Signal Processing), Espoo, Finland.

function [ CC, FBE, frames ] = mfcc( speech, fs, Tw, Ts, alpha, window, R, M, N, L )
% MFCC Mel frequency cepstral coefficient feature extraction.
%
% MFCC(S,FS,TW,TS,ALPHA,WINDOW,R,M,N,L) returns mel frequency
% cepstral coefficients (MFCCs) computed from speech signal given
% in vector S and sampled at FS (Hz). The speech signal is first
% preemphasised using a first order FIR filter with preemphasis
% coefficient ALPHA. The preemphasised speech signal is subjected
% to the short-time Fourier transform analysis with frame durations
% of TW (ms), frame shifts of TS (ms) and analysis window function
% given as a function handle in WINDOW. This is followed by magnitude
% spectrum computation followed by filterbank design with M triangular
% filters uniformly spaced on the mel scale between lower and upper
% frequency limits given in R (Hz). The filterbank is applied to
% the magnitude spectrum values to produce filterbank energies (FBEs)
% (M per frame). Log-compressed FBEs are then decorrelated using the
% discrete cosine transform to produce cepstral coefficients. Final
% step applies sinusoidal lifter to produce liftered MFCCs that
% closely match those produced by HTK [1].
%
% [CC,FBE,FRAMES]=MFCC(...) also returns FBEs and windowed frames,
% with feature vectors and frames as columns.
%
% This framework is based on Dan Ellis' rastamat routines [2]. The
% emphasis is placed on closely matching MFCCs produced by HTK [1]
% (refer to p.337 of [1] for HTK's defaults) with simplicity and
% compactness as main considerations, but at a cost of reduced
% flexibility. This routine is meant to be easy to extend, and as
% a starting point for work with cepstral coefficients in MATLAB.
% The triangular filterbank equations are given in [3].
%
% Inputs
% S is the input speech signal (as vector)
%
% FS is the sampling frequency (Hz)
%
% TW is the analysis frame duration (ms)
%
% TS is the analysis frame shift (ms)
%
% ALPHA is the preemphasis coefficient
%
% WINDOW is a analysis window function handle
%
% R is the frequency range (Hz) for filterbank analysis
%
% M is the number of filterbank channels
%
% N is the number of cepstral coefficients
% (including the 0th coefficient)
%
% L is the liftering parameter
%
% Outputs
% CC is a matrix of mel frequency cepstral coefficients
% (MFCCs) with feature vectors as columns
%
% FBE is a matrix of filterbank energies
% with feature vectors as columns
%
% FRAMES is a matrix of windowed frames
% (one frame per column)
%
% Example
% Tw = 25; % analysis frame duration (ms)
% Ts = 10; % analysis frame shift (ms)
% alpha = 0.97; % preemphasis coefficient
% R = [ 300 3700 ]; % frequency range to consider
% M = 20; % number of filterbank channels
% C = 13; % number of cepstral coefficients
% L = 22; % cepstral sine lifter parameter
%
% % hamming window (see Eq. (5.2) on p.73 of [1])
% hamming = @(N)(0.54-0.46*cos(2*pi*[0:N-1].'/(N-1)));
%
% % Read speech samples, sampling rate and precision from file
% [ speech, fs, nbits ] = wavread( 'sp10.wav' );
%
% % Feature extraction (feature vectors as columns)
% [ MFCCs, FBEs, frames ] = ...
% mfcc( speech, fs, Tw, Ts, alpha, hamming, R, M, C, L );
%
% % Plot cepstrum over time
% figure('Position', [30 100 800 200], 'PaperPositionMode', 'auto', ...
% 'color', 'w', 'PaperOrientation', 'landscape', 'Visible', 'on' );
%
% imagesc( [1ize(MFCCs,2)], [0:C-1], MFCCs );
% axis( 'xy' );
% xlabel( 'Frame index' );
% ylabel( 'Cepstrum index' );
% title( 'Mel frequency cepstrum' );
%
% References
%
% [1] Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D.,
% Liu, X., Moore, G., Odell, J., Ollason, D., Povey, D.,
% Valtchev, V., Woodland, P., 2006. The HTK Book (for HTK
% Version 3.4.1). Engineering Department, Cambridge University.
% (see also: http://htk.eng.cam.ac.uk)
%
% [2] Ellis, D., 2005. Reproducing the feature outputs of
% common programs using Matlab and melfcc.m. url:
% http://labrosa.ee.columbia.edu/matlab/ra...mfccs.html
%
% [3] Huang, X., Acero, A., Hon, H., 2001. Spoken Language
% Processing: A guide to theory, algorithm, and system
% development. Prentice Hall, Upper Saddle River, NJ,
% USA (pp. 314-315).
%
% See also EXAMPLE, COMPARE, FRAMES2VEC, TRIFBANK.

% Author: Kamil Wojcicki, September 2011


%% PRELIMINARIES

% Ensure correct number of inputs
if( nargin~= 10 ), help mfcc; return; end;

% Explode samples to the range of 16 bit shorts
if( max(abs(speech))<=1 ), speech = speech * 2^15; end;

Nw = round( 1E-3*Tw*fs ); % frame duration (samples)
Ns = round( 1E-3*Ts*fs ); % frame shift (samples)

nfft = 2^nextpow2( Nw ); % length of FFT analysis
K = nfft/2+1; % length of the unique part of the FFT


%% HANDY INLINE FUNCTION HANDLES

% Forward and backward mel frequency warping (see Eq. (5.13) on p.76 of [1])
% Note that base 10 is used in [1], while base e is used here and in HTK code
hz2mel = @( hz )( 1127*log(1+hz/700) ); % Hertz to mel warping function
mel2hz = @( mel )( 700*exp(mel/1127)-700 ); % mel to Hertz warping function

% Type III DCT matrix routine (see Eq. (5.14) on p.77 of [1])
dctm = @( N, M )( sqrt(2.0/M) * cos( repmat([0:N-1].',1,M) ...
.* repmat(pi*([1:M]-0.5)/M,N,1) ) );

% Cepstral lifter routine (see Eq. (5.12) on p.75 of [1])
ceplifter = @( N, L )( 1+0.5*L*sin(pi*[0:N-1]/L) );


%% FEATURE EXTRACTION

% Preemphasis filtering (see Eq. (5.1) on p.73 of [1])
speech = filter( [1 -alpha], 1, speech ); % fvtool( [1 -alpha], 1 );

% Framing and windowing (frames as columns)
frames = vec2frames( speech, Nw, Ns, 'cols', window, false );

% Magnitude spectrum computation (as column vectors)
MAG = abs( fft(frames,nfft,1) );

% Triangular filterbank with uniformly spaced filters on mel scale
H = trifbank( M, K, R, fs, hz2mel, mel2hz ); % size of H is M x K

% Filterbank application to unique part of the magnitude spectrum
FBE = H * MAG(1:K,; % FBE( FBE<1.0 ) = 1.0; % apply mel floor

% DCT matrix computation
DCT = dctm( N, M );

% Conversion of logFBEs to cepstral coefficients through DCT
CC = DCT * log( FBE );

% Cepstral lifter computation
lifter = ceplifter( N, L );

% Cepstral liftering gives liftered cepstral coefficients
CC = diag( lifter ) * CC; % ~ HTK's MFCCs


% EOF