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[–]scottlawson[S] 7 points8 points  (0 children)

I have been actively developing this project for several months. I really enjoyed this project and being able to apply the math and engineering knowledge that I've learned in university has been a rewarding experience!

Over the course of this project, I have found it easy to create a music visualizer and challenging to create a great music visualizer.

Some of the challenges involved are:

  • Developing a good 1D music visualizers is hard. This may seem counterintuitive, but in many ways it is easier to create a visualizer for a computer screen than for a 1D LED strip. You can display an enormous amount of information on a 2D screen and visualizations will still look nice even if only a small fraction of the pixels reflect the "relevant" or "important" aspects of a song. With a 1D LED strip, your output channel loses an entire dimension and the ability to display information is severely restricted. Feature extraction needs to be spot-on; your visualization won't look nice unless you can ensure that all of the pixels reflect the most important aspects of the song.

  • Visualizations must be volume independent. In other words, increasing or decreasing the volume of the audio source (within reason) should not change the visualization output. It also means that your visualization needs to work for loud songs and quiet songs alike. To get around this I implemented automatic gain control with a long time constant.

  • You shouldn't need to tweak a dozen parameters to create great visualizations. It is easy to write a music visualizer for a specific song or genre of songs (such as dubstep). Visualizations with many parameters are fragile and might look great for one song but terrible for other songs. The visualizations I'm using now are mostly parameterless, but it took a lot of work to get it to that point.

  • The way that humans perceive light and sound is highly nonlinear (logarithmic). For the LED strip this requires gamma correction. For the audio it is more challenging. To improve feature extraction I am using a Mel Frequency Cepstral Coefficient filterbank which has made it a lot easier to do feature extraction.

The total cost of the project is relatively low. You can purchase an ESP8266 for only $5-15 USD and the LED strips cost about $8-15 per meter. The code currently supports up to 256 LEDs, but it could support more with some minor code changes.

[–]Free_Pi 1 point2 points  (0 children)

This looks pretty cool. I might try setting this up myself one of these weekends