A whimsically named music clustering program.

Running

Calls the external program sox; could eventually use cgo to bind to libsox, but this would actually complicate compilation a bit. This clean separation makes compiling as easy as pure Go libs/exe's always are while still calling sox for reading sound files. It turns out that reading mp3's is not easy.

Spectral features

Currently uses the following features:

• spectral cutoff frequency
• variation in spectral energy over time
• frequency bin with most energy and variance
• log-spaced raw frequency energies

For now, only uses a 4s sample starting at 0:30 in the song. Ultimately should sample several 4s samples and average the results.

Clustering

The clustering is a straightforward k-means on the normalized features using a Euclidean distance metrics. K-means is susceptible to outliers and random initialization, which may be a problem for music. To help counteract these effects I run k-means several times and select the one with the best 'cost', a combination of sum of Euclidean distances and a measure of the spread of clusters, to discourage lumping everything in one big cluster.