years back yours truly have made a contraption to convey the concept of fourier transform http://shin-ajaran.blogspot.sg/2011/07/diy-arduino-vu-spectrum-analyzer.html using Arduino, LOL shield and the FFT library. The piece of code is still hanging on the Internet, but the hardware has been re-purposed for the better of humanity.
Not long ago, yours truly come across the FHT (Fast Hartley Transform) by Open Music Lab http://wiki.openmusiclabs.com/wiki/ArduinoFHT while browsing the Internet for inspirations to continue with the current working life. This algorithm claims to be more efficient in terms of CPU cycles and memory footprint; well, true to speak because the premise is: FHT works on the "real" portion of the data whereas FFT works on both the "real" and "complex" portion of the data. Really excited by this discovery of the code library, yours truly can't wait to get his hand dirty on making a contraption that uses the above FHT. Following the instructions in the FHT wiki for installing the library and sample data output using processing is a breeze. This wiki also comes complete with a 128 channel spectrum visualizer written using processing; Scroll down until you hit "FHT_128_channel_analyser.zip" http://wiki.openmusiclabs.com/wiki/ArduinoFHT?action=AttachFile&do=view&target=FHT_128_channel_analyser.zip . A visualizer is very handy when it comes to deciding the "strategy" for the music to light algorithm.
Check out the video below for a demo of this make
This make assumes the following parts come in handy
1. 1x Arduino
2. 1x RGB LED (common anode)
3. 1x 3D printed LED diffuser
4. 1x electret microphone & LM386 audio amplifier.
Prelude: making the amplifier for electret microphone.
An electret microphone http://en.wikipedia.org/wiki/Electret_microphone is a cheapo microphone that reads in analog signal generated by sound frequency. This analog signal has to be amplified, and then passed into a microprocessor based system (e.g Arduino) for ADC (Analog to Digital Conversion). Once data is digitized, humans can manipulate the signal with code, hence the terminology : digital signal processing.
This make assumes an LM386 as the audio amplifier http://www.ti.com/lit/ds/symlink/lm386.pdf for the electret microphone is available.
There are many manufacturers of LM386, one of the is TI. Refer to the link above for spec sheet and then scroll down to the diagram "amplifier with minimum parts". If you need help on making a LM386 based audio amplifier, this instructable http://www.instructables.com/id/Know-Your-IC-LM386/ is helpful on getting started.
Prelude2: For those that are still clueless what is happening, check out this very thoroughly written article on sound analysis https://bochovj.wordpress.com/2013/06/23/sound-analysis-in-arduino/.
Step1: The wiring
Connect output from electret & LM386 audio amplifier to A0 of arduino, and VCC and GND to arduino's VCC and GND. Connect common anode RGB LED to pin 3,4,5,6 of Arduino;with pin4 dedicated as the common anode, pin3,5,6 dedicated as PWM pin. It is good to add some 220ohm resistors across the pin3,5,6 for current limiting. Yours truly has none available, hence the omission in the picture below. These 4pins can be used as the input to a transistor switched 12V load to control LED light strips. The following picture describes the wiring, and LED diffuser with Fibre Optic cable
Step2: the code and the strategy of choosing which frequency is for what colour
There are several ways to map the frequency spectrum to the RGB colour spectrum. Using AnalogWrite() on R, G,B; each PWM pin is capable of a value from 0-255 to drive the individual LED in the RGB LED. Thus, the total combination of colours possible (in code) are 256*256*256 = 16777216; thats a whopping 16M worth of variations.
For the visually inclined, the RGB chart below is a good guide for giving an idea what is the final colour blend at the RGB LED output corresponding to the R, G, B value written by AnalogWrite().
Processing has a useful article on colour https://processing.org/tutorials/color/ which yours truly think it helps with visualizing colour using code.
The questions come begging: How to map audio frequency to the colour spectrum??
Years ago, yours truly did an attempt at mapping colour to frequency using the self made LOLs shield http://shin-ajaran.blogspot.sg/2012/03/arduino-music-to-rgb-with-lolss.html, but it is not visually appealing.
Drawing inspiration from yours truly secondary school physics: human voice ranges from 85Hz to 255Hz; male voice is at lower frequency bands 85Hz-180Hz whereas female voice is at higher frequency bands 165Hz to 255Hz. As for human hearing, it is from 20Hz to 20K Hz. Futhermore, each musical instruments has it's own frequency range, and as we know, music composes of a variety of frequency stemming for human voice and/or musical instruments. Hence, the choice of strategy will be reflected in the colour observed while a piece of music is played.
Strategy: mapping audio frequency to colour spectrum
1. Mapping of human hearing e.g 20Hz to 20K Hz to 16777216 of possible RGB colours
1a. Mapping of whole audio frequency bands to 6777216 of possible RGB colours.
2. Choosing 3 channels deliberately; one each from the low, mid, and high frequency bands as observed using the spectrum visualizer mentioned earlier. The 3 channels of low, mid, and high corresponds to Blue, Green, and Red; with the intensity of the colour corresponds to the amplitude of that chosen channel. The output of RGB LED will then be "blended".
3. Similar to 2, but instead of choosing the channels deliberately, this algo is to group frequency bands into larger low, mid, and high frequency bands; within each of this group of larger frequency bands , the amplitude that is used to turn on the corresponding LED is the result of averaging all the amplitude from the frequency bands.
4. Similar to 2,3, but first apply a Low Pass Filter at the frequency bands.
5. LED activated by predefined threshold on frequency band
It seems to yours truly, finding an ideal mapping of music genre to colour is going to be an never ending story.
Cut the chase, let's go to the code.
No matter what are the music played during experimentation, it seems that the genre of the music maps to the corresponding biased group of frequency bands. Assuming the frequency bands are colour mapped eg blue for low, green for mid, red for high, definitely techno is going to appear more blue than red, and the counterexample eg opera is going to appear more red than blue. So the big Q: Which recipe for mapping of frequency to colour is "the best"? For this, yours truly don't have an answer, yet. It seems appreciating changing colour visually may differ from human to human.
Nonetheless, FHT is a really responsive algorithm implemented on Arduino. Check out the demo video below reacting to human voice.
Interested to pick up where I left? ping me!~~~~