Speech Signal Analysis using IIR filters

If i want to analyse a speech signal using IIR filters like chebyshev, butterworth and elliptic, how do I decide the parameters like passband and stopband frequency, maximum and minimum passband and stopband ripples?

Pl help me how to proceed ahead with this code.

% Speech Signal Analysis
clc;
clear all;
close all;

[y,fs] = audioread('a010Shoe.wav');
ydft = fft(y);
% I'll assume y has even length
ydft = ydft(1:length(y)/2+1);
% create a frequency vector
freq = 0:fs/length(y):fs/2;
% plot magnitude
subplot(211);
plot(freq,abs(ydft));
% plot phase
subplot(212);
plot(freq,unwrap(angle(ydft))); 
xlabel('Hz');

Answer 1

[Mathieu NOE]

hello

your code is about frequency analysis (FFT) and not filtering time series. ... so what is the purpose of bandpass filtering here ?

what info are you looking at ?

maybe a time frequency analysis can be of some help to understand energy distribution vs time and vs frequency. Then maybe after that first analysis you can filter the signal for another use.

If it can help you, below some code to do basic spectral and time / frequency analysis. I can further help you on the digital filters once we understand what you are looking for.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% dummy signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Fs = 5000;
samples = 255001;
dt = 1/Fs;

t = (0:dt:(samples-1)*dt);
omega = 2*pi*(25+20*t);

sensordata = randn(size(t)) + 5*sin(omega.*t);  % signal = linear chirp + noise
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


% FFT parameters
samples = length(sensordata);
NFFT = 512;    % 
NOVERLAP = round(0.75*NFFT);
w = hanning(NFFT);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% option 1 : averaged FFT spectrum
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[sensor_spectrum, freq] = pwelch(sensordata,w,NOVERLAP,NFFT,Fs);

figure(1),plot(freq,20*log10(sensor_spectrum));
title(['Averaged FFT Spectrum  / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(freq(2)-freq(1)) ' Hz ']);
xlabel('Time (s)');ylabel('Amplitude (dB)');


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% option 2 : time / frequency analysis : spectrogram demo
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

saturation_dB = 80;  % dB range scale (means , the lowest displayed level is 80 dB below the max level)
% fmin = 1;
% fmax = Fs/2;

[sg,fsg,tsg] = specgram(sensordata,NFFT,Fs,w,NOVERLAP);  

% FFT normalisation and conversion amplitude from linear to dB peak
sg_dBpeak = 20*log10(abs(sg))+20*log10(2/length(fsg));     % NB : X=fft(x.*hanning(N))*4/N; % hanning only

% saturation to given dB range
mini_dB = round(max(max(sg_dBpeak))) - saturation_dB;
sg_dBpeak(sg_dBpeak<mini_dB) = mini_dB;

% plots spectrogram
figure(2);
imagesc(tsg,fsg,sg_dBpeak);axis('xy');colorbar('vert');
title(['Spectrogram  / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(fsg(2)-fsg(1)) ' Hz ']);
xlabel('Time (s)');ylabel('Frequency (Hz)');