# Lecture 1: Matlab DSP Review

## Contents

## Simple signals

Generate a pure tone.

% Pick a sampling rate. sr = 8000; % Define the time axis. dur = 1; % sec t = linspace(0, dur, dur * sr); freq = 440; % Hz x = sin(2*pi*freq*t);

Playing sounds.

soundsc(x, sr)

Warning: The playback thread did not start within one second. (Type "warning off MATLAB:audioplayer:playthread" to suppress this warning.)

Making plots.

```
plot(t, x)
xlabel('Time (sec)')
```

That looks pretty terrible. Lets only plot the first few cycles.

period = 1.0 / freq; % sec samples_per_period = period * sr; idx = 1:3*samples_per_period; plot(t(idx) * 1e3, x(idx)); xlabel('Time (msec)')

Make some noise.

% rand generates uniformly distributed random values between 0 and 1. n = rand(1, length(t)) - 0.5; plot(t(idx) * 1e3, n(idx)) xlabel('Time (msec)')

## Audio I/O

filename = 'flute-C4.wav'; [x2 sr2] = wavread(filename); % Get the time axis right. t2 = linspace(0, length(x2) / sr2, length(x2)); plot(t2, x2) xlabel('Time (sec)')

## Resampling

sr3 = 4000; % Hz x3 = resample(x2, 8000, sr3); subplot(211) title('Flute') plot(x2) subplot(212) plot(x3) title('Resampled flute') xlabel('Time (samples)')

Write the sound file to disk.

```
wavwrite(x3, sr3, 'flute-resampled.wav')
```

## Fourier Transform

X = fft(x);

Whats the frequency axis?

f = linspace(0, sr, length(X)); subplot(211) plot(f, abs(X)) title('Magnitude') subplot(212) plot(f, angle(X)) title('Phase') xlabel('Frequency (Hz)')

Recall that for real-valued signals the DFT is always symmetric around sr/2 (or 0), so we only need to plot the first half. This time for x2.

X2 = fft(x2(1:1024)); f2 = linspace(0, sr2, length(X2)); subplot(211) plot(f2(1:end/2+1), abs(X2(1:end/2+1))) title('Magnitude') subplot(212) plot(f2(1:end/2+1), angle(X2(1:end/2+1))) title('Phase') xlabel('Frequency (Hz)')

Whats the (approximate) fundamental frequency of the flute note (C4)? Just find the bin corresponding to the first peak in the magnitude spectrum.

mag = abs(X2); % Ignore redundant second half. mag = mag(1:end/2+1); % Find all local maxima. peaks = (mag(1:end-2) < mag(2:end-1)) & mag(2:end-1) > mag(3:end); length(peaks)

ans = 511

But we only want the peaks above a threshold to ensure that we ignore the noise.

peaks = peaks & mag(2:end-1) > 0.5; f2(peaks)

ans = 247.87

## Filters

Simple FIR filter: y[n] = 0.5*x[n] + 0.5*x[n-1]

filt = [0.5, 0.5];

Lets filter some noise buy convolving it with the impulse response.

% Recall that the convolution of two signals with length N1 and N2 is % N1 + N2 - 1. The 'same' argument just tells conv to truncate the % convolved signal at N1 samples. y = conv(n, filt, 'same'); N = fft(n); Y = fft(y); idx = 1:length(n) / 2 + 1; subplot(211); plot(f(idx), abs(N(idx))) subplot(212); plot(f(idx), abs(Y(idx))) xlabel('Frequency (Hz)')

What about IIR filters?

```
[b a] = butter(2, [400 1500]/sr)
y = filter(b, a, n);
Y = fft(y);
subplot(212); plot(f(idx), abs(Y(idx)))
xlabel('Frequency (Hz)')
```

b = 0.035438 0 -0.070875 0 0.035438 a = 1 -3.2428 4.0778 -2.3717 0.54317

(You can use filter for FIR filters too, just be sure that the second argument is a scalar).

Matlab has a special function to plot a filter's frequency response.

clf freqz(b, a)

Pole-zero plots too:

zplane(b, a)

Lets make an unstable filter.

b = 1; a = [1 2]; zplane(b, a)

That pole is definitely outside of the unit circle. What is its impulse response?

[h, t] = impz(b, a, 100); % Get first 100 samples of impulse response plot(t, h) title('h[n]') xlabel('Time (samples)')

## Spectrograms

nwin = 512; % samples noverlap = 256; %samples nfft = 512; %samples spectrogram(x2, nwin, noverlap, nfft, sr2, 'yaxis'); colorbar

## Getting help

```
lookfor spectrogram
```

specgram - Spectrogram using a Short-Time Fourier Transform (STFT). spectrogram - Spectrogram using a Short-Time Fourier Transform (STFT). specgramdemo - Spectrogram Display.

```
help spectrogram
```

SPECTROGRAM Spectrogram using a Short-Time Fourier Transform (STFT). S = SPECTROGRAM(X) returns the spectrogram of the signal specified by vector X in the matrix S. By default, X is divided into eight segments with 50% overlap, each segment is windowed with a Hamming window. The number of frequency points used to calculate the discrete Fourier transforms is equal to the maximum of 256 or the next power of two greater than the length of each segment of X. If X cannot be divided exactly into eight segments, X will be truncated accordingly. S = SPECTROGRAM(X,WINDOW) when WINDOW is a vector, divides X into segments of length equal to the length of WINDOW, and then windows each segment with the vector specified in WINDOW. If WINDOW is an integer, X is divided into segments of length equal to that integer value, and a Hamming window of equal length is used. If WINDOW is not specified, the default is used. S = SPECTROGRAM(X,WINDOW,NOVERLAP) NOVERLAP is the number of samples each segment of X overlaps. NOVERLAP must be an integer smaller than WINDOW if WINDOW is an integer. NOVERLAP must be an integer smaller than the length of WINDOW if WINDOW is a vector. If NOVERLAP is not specified, the default value is used to obtain a 50% overlap. S = SPECTROGRAM(X,WINDOW,NOVERLAP,NFFT) specifies the number of frequency points used to calculate the discrete Fourier transforms. If NFFT is not specified, the default NFFT is used. S = SPECTROGRAM(X,WINDOW,NOVERLAP,NFFT,Fs) Fs is the sampling frequency specified in Hz. If Fs is specified as empty, it defaults to 1 Hz. If it is not specified, normalized frequency is used. Each column of S contains an estimate of the short-term, time-localized frequency content of the signal X. Time increases across the columns of S, from left to right. Frequency increases down the rows, starting at 0. If X is a length NX complex signal, S is a complex matrix with NFFT rows and k = fix((NX-NOVERLAP)/(length(WINDOW)-NOVERLAP)) columns. For real X, S has (NFFT/2+1) rows if NFFT is even, and (NFFT+1)/2 rows if NFFT is odd. [S,F,T] = SPECTROGRAM(...) returns a vector of frequencies F and a vector of times T at which the spectrogram is computed. F has length equal to the number of rows of S. T has length k (defined above) and its value corresponds to the center of each segment. [S,F,T] = SPECTROGRAM(X,WINDOW,NOVERLAP,F,Fs) where F is a vector of frequencies in Hz (with 2 or more elements) computes the spectrogram at those frequencies using the Goertzel algorithm. The specified frequencies in F are rounded to the nearest DFT bin commensurate with the signal's resolution. [S,F,T,P] = SPECTROGRAM(...) P is a matrix representing the Power Spectral Density (PSD) of each segment. For real signals, SPECTROGRAM returns the one-sided modified periodogram estimate of the PSD of each segment; for complex signals and in the case when a vector of frequencies is specified, it returns the two-sided PSD. SPECTROGRAM(...) with no output arguments plots the PSD estimate for each segment on a surface in the current figure. It uses SURF(f,t,10*log10(abs(P)) where P is the fourth output argument. A trailing input string, FREQLOCATION, controls where MATLAB displays the frequency axis. This string can be either 'xaxis' or 'yaxis'. Setting this FREQLOCATION to 'yaxis' displays frequency on the y-axis and time on the x-axis. The default is 'xaxis' which displays the frequency on the x-axis. If FREQLOCATION is specified when output arguments are requested, it is ignored. EXAMPLE 1: Display the PSD of each segment of a quadratic chirp. t=0:0.001:2; % 2 secs @ 1kHz sample rate y=chirp(t,100,1,200,'q'); % Start @ 100Hz, cross 200Hz at t=1sec spectrogram(y,128,120,128,1E3); % Display the spectrogram title('Quadratic Chirp: start at 100Hz and cross 200Hz at t=1sec'); EXAMPLE 2: Display the PSD of each segment of a linear chirp. t=0:0.001:2; % 2 secs @ 1kHz sample rate x=chirp(t,0,1,150); % Start @ DC, cross 150Hz at t=1sec F = 0:.1:100; [y,f,t,p] = spectrogram(x,256,250,F,1E3,'yaxis'); % NOTE: This is the same as calling SPECTROGRAM with no outputs. surf(t,f,10*log10(abs(p)),'EdgeColor','none'); axis xy; axis tight; colormap(jet); view(0,90); xlabel('Time'); ylabel('Frequency (Hz)'); See also PERIODOGRAM, PWELCH, SPECTRUM, GOERTZEL. Reference page in Help browser doc spectrogram

Graphical documentation

```
doc spectrogram
```

Advanced Matlab audio processing resources: http://www.ee.columbia.edu/~dpwe/resources/matlab/

author = 'Ron Weiss <ronw@nyu.edu>'; fprintf('%s\nLast updated %s\n', author, datestr(now))

Ron Weiss <ronw@nyu.edu> Last updated 21-Jan-2010 17:30:18