Using exemplar fingerprints, a representative sample of an artist's music, is a good approach, but success would require detailed fingerprints, a varied dataset, and a well-chosen algorithm.

For artists who change styles, time-series analysis can capture their evolving sound.

The solution will likely need machine learning.

The current solution doesn't use feature hashing or clusterable feature vectors. Instead, it relies on audio fingerprinting, which breaks down short audio samples into unique patterns or "fingerprints" for quick comparison with a large database of known songs.