R – “Winamp style” spectrum analyzer

audiofftsignal processingspectrum

I have a program that plots the spectrum analysis (Amp/Freq) of a signal, which is preety much the DFT converted to polar. However, this is not exactly the sort of graph that, say, winamp (right at the top-left corner), or effectively any other audio software plots. I am not really sure what is this sort of graph called (if it has a distinct name at all), so I am not sure what to look for.

I am preety positive about the frequency axis being base two exponential, the amplitude axis puzzles me though.

Any pointers?

Best Answer

Actually an interesting question. I know what you are saying; the frequency axis is certainly logarithmic. But what about the amplitude? In response to another poster, the amplitude can't simply be in units of dB alone, because dB has no concept of zero. This introduces the idea of quantization error, SNR, and dynamic range.

Assume that the received digitized (i.e., discrete time and discrete amplitude) time-domain signal, x[n], is equal to s[n] + e[n], where s[n] is the transmitted discrete-time signal (i.e., continuous amplitude) and e[n] is the quantization error. Suppose x[n] is represented with b bits, and for simplicity, takes values in [0,1). Then the maximum peak-to-peak amplitude of e[n] is one quantization level, i.e., 2^{-b}.

The dynamic range is the defined to be, in decibels, 20 log10 (max peak-to-peak |s[n]|)/(max peak-to-peak |e[n]|) = 20 log10 1/(2^{-b}) = 20b log10 2 = 6.02b dB. For 16-bit audio, the dynamic range is 96 dB. For 8-bit audio, the dynamic range is 48 dB.

So how might Winamp plot amplitude? My guesses:

The minimum amplitude is assumed to be -6.02b dB, and the maximum amplitude is 0 dB. Visually, Winamp draws the window with these thresholds in mind.
Another nonlinear map, such as log(1+X), is used. This function is always nonnegative, and when X is large, it approximates log(X).

Any other experts out there who know? Let me know what you think. I'm interested, too, exactly how this is implemented.

Related Solutions

R – Implementing Matlab’s avgpower in Octave

Apparently I'm talking to myself, but here is some proposed Octave code for those who wander this way.

function[avgp] = oavgpower(signal, sampling_freq, lowfreq, highfreq, window)

[spectra, freq]=pwelch(signal, window, [], [], sampling_freq);

idx1=max(find(freq = highfreq));

% Index and actual frequency of lower and upper bins
%idx1
%freq(idx1)
%idx2
%freq(idx2)

% 0: don't include the last bin
width = [diff(freq); 0];

pvec = width(idx1:idx2).*spectra(idx1:idx2);
avgp = sum(pvec);

Java – Signal amplitude against time in Java

The basic approach is this:

collect your audio data that you want to display. This may be the raw audio samples or may be the result of processing (e.g. averaging across N samples, or taking every Nth sample) to provide scaling on the x-axis. (Without scaling, you will need 48000 pixels to display 1 second of audio.)
Given this array of samples, you have to turn it into a aplitude-time plot. As a start you can create a new JPanel and override the paint method to draw the plot.

E.g.

public void paint(Graphics g)
{
   short[] samples;  // the samples to render - get this from step 1.

   int height = getHeight();
   int middle = height/2;

   for (int i=0; i<samples.length; i++)
   {
      int sample = samples[i];
      int amplitude = sample*middle;
      g.drawLine(i, middle+amplitide, i, middle-amplitude); 
   }  
}

This will draw a amplitude-time graph in your component. it will be as heigh as the component, and as wide as the number of samples.

To get animation, you regularly drop in a new short[] array into the component and call repaint() (All on the Swing Event thread, of course!).

Good luck!

Best Answer

Related Solutions

R – Implementing Matlab’s avgpower in Octave

Java – Signal amplitude against time in Java

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