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# enbw() - Signal Processing

```bw = enbw(window)
returns the two-sided equivalent noise bandwidth, bw,
for a uniformly sampled window, window. The equivalent
noise bandwidth is normalized by the noise power per frequency bin.examplebw = enbw(window,fs) returns
the two-sided equivalent noise bandwidth, bw,
in Hz.```

### Syntax

`bw = enbw(window) examplebw = enbw(window,fs) example`

### Example

```Equivalent Noise Bandwidth of Hamming WindowOpen This ExampleDetermine the equivalent noise bandwidth of a Hamming window 1000 samples in length.bw = enbw(hamming(1000))

bw =

1.3638

Equivalent Noise Bandwidth of Flat Top WindowOpen This ExampleDetermine the equivalent noise bandwidth in Hz of a flat top window 10000 samples in length. The sample rate is 44.1 kHz.bw = enbw(flattopwin(10000),44.1e3)

bw =

16.6285

Equivalent Rectangular Noise BandwidthOpen This Example
Obtain the equivalent rectangular noise bandwidth of a Von Hann window and overlay the equivalent rectangular bandwidth on the window's magnitude spectrum. The window is 1000 samples in length and the sampling frequency is 10 kHz.
Set the sampling frequency, create the window, and obtain the discrete Fourier transform of the window with 0 frequency in the center of the spectrum.Fs = 10000;
win = hann(1000);
windft = fftshift(fft(win));
Obtain the equivalent (rectangular) noise bandwidth of the Von Hann window.bw = enbw(hann(1000),Fs)

bw =

15.0150

Plot the squared-magnitude DFT of the window and use the equivalent noise bandwidth to overlay the equivalent rectangle.freq = -(Fs/2):Fs/length(win):Fs/2-(Fs/length(win));
maxgain = 20*log10(abs(windft(length(win)/2+1)));

plot(freq,20*log10(abs(windft)))
hold on
plot(bw*[-1 -1 1 1],[-40 maxgain maxgain -40],'--')
hold off

xlabel('Hz')
ylabel('dB')
axis([-60 60 -40 60])```