JavaFx Music Spectrum
My first 2018 post, and hi to you all.
Building a circular progress indicator specifically for showing the music progress was the initial idea which was fairly simple to do with the Animation class ,with the code snippet below
My Slerp function looks like so, found on stackoverflow.
Half way the project i decided to change to the project to visualize spectrum magnitude of each band in a song.I used the default number of bands 128 and a threshold of -300 for better visibility.
The bands are drawn from the center creating 128 nodes each with an opacity that corresponds to the spectral threshold.Since the opacity value is between 0 to 1,i had to normalize the threshold values.
Building a circular progress indicator specifically for showing the music progress was the initial idea which was fairly simple to do with the Animation class ,with the code snippet below
Animation = new Transition() {
{
setCycleDuration(Duration.seconds(5));
setInterpolator(Interpolator.LINEAR);
}
@Override
protected void interpolate(double frac) {
double interpolation_angle = slerpFunc(frac);
double sinValue = Math.sin(Math.toRadians(interpolation_angle + startAngle));
double cosValue = Math.cos(Math.toRadians(interpolation_angle + startAngle));
double angleX = center2d.getX() + (radius * 0.4) * sinValue;
double angleY = center2d.getY() + (radius * 0.4) * cosValue;
gc.fillOval(angleX,angleY,1,1);
}
};
My Slerp function looks like so, found on stackoverflow.
private double slerpFunc(double frac){
return (((((360 - 0) % 360) + 540 ) % 360) + 180) * frac;
}
Half way the project i decided to change to the project to visualize spectrum magnitude of each band in a song.I used the default number of bands 128 and a threshold of -300 for better visibility.
The bands are drawn from the center creating 128 nodes each with an opacity that corresponds to the spectral threshold.Since the opacity value is between 0 to 1,i had to normalize the threshold values.
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| Andre -Fascination Javafx Music Spectrum |
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| sleep away Spectrum |





Great walkthrough on the audio spectrum visualization. The FFT approach you demonstrated here pairs well with real-time signal processing pipelines. Worth noting that HVAC load forecasting uses similar spectral analysis techniques for pattern recognition in time-series data — happy to share more on that crossover if it would help.
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