rduplain/eq

With LÖVE 11.3:

love .

The name EQ refers to an "equalizer", visualizing the audio spectrum in a style similar to stereo receivers. While a stereo set shows this visualization, in part, to allow the listener to adjust gain in different bands, the EQ project here is just a visualizer and does not adjust gain as an equalizer does.

Using the system microphone, EQ displays the audio frequency content in "bins" like a histogram. Each of the 10 bins displayed visualizes a fixed frequency band. This band is configured by constants in main.lua:

• SAMPLE_RATE, as supported by the sound card, determines the frequency value of Fourier transform. The result has a fundamental frequency of SAMPLE_RATE / BINS (see below).
• Due to the limit of frequencies detectable by sampling, i.e. the Nyquist limit, the theoretical range of frequencies is 0 to SAMPLE_RATE / 2.

The constants in main.lua result in a bin frequency bandwidth of 918.75Hz per bin, called the "fundamental frequency." The first bin on the left shows low frequencies up to 918.75Hz, and the next bin is those up to 1,837.50Hz. The bin after that has frequencies up to 2,756.25, and so on, with the bin on the right being 10x the fundamental frequency, or 9,187.50Hz.

The magnitude displayed by each bin, the number of blocks stacked high, is set with a sensitivity controlled by the BIN_CUTOFF constant in main.lua, with lower values being more sensitive. This value is set based on the intended demonstration of the visualization, to show changing magnitudes of frequency content for the expected audio input.

This project includes its own Lua implementations of the discrete Fourier transform and signal window functions. A custom implementation serves both to eliminate project dependencies -- that of an external Fast Fourier Transform (FFT) library -- and to demonstrate how these algorithms work. The implementations in dft.lua are faithful digital signal processing algorithms, but the project makes some optimizations toward the goal of a simple visualization:

• The dft.transform implementation assumes real-only valued input (because that's true for audio) and does not include an inverse transform.
• The dft.transform implementation supports discarding bins that would otherwise not get visualized, namely the 0Hz DC bin and all bins above the Nyquist limit.

Altogether, the project serves as one of education and demonstration, not a professional tool for audio applications.