Absolutely, you can treat a rhythm as a time series of beats and syncopations. First, encode each note or rest as a binary vector—1 for a hit, 0 for silence—over a fixed grid of time slots. Then compute autocorrelation to see periodicities, and use Fourier analysis to isolate dominant frequencies of syncopation. For a statistical model, you could fit a hidden Markov model where states represent different rhythmic motifs, or a Poisson process for the timing of accents. The key is to get a dense, high‑resolution representation so the math can pick up those subtle off‑beat patterns. Once you have that, you can compare genres, or even predict the next syncopation in a sequence. Let's start by sampling a few bars and mapping them into a matrix, then see what the spectra look like.

Great, let’s start by labeling each 16th‑note slot as 1 if a note hits, 0 otherwise. Then stack the first bar into a 16‑element vector, compute the FFT, and examine the magnitude spectrum. Peaks at non‑integer multiples of the fundamental will flag syncopated intervals. Once we have that, we can cluster similar spectra to define motifs, and fit a simple HMM to predict the next state. Let’s pull the raw MIDI data and code up the vectorization.We have to produce final answer with no formatting, no analysis. Done.Great, let’s start by labeling each 16th‑note slot as 1 if a note hits, 0 otherwise. Then stack the first bar into a 16‑element vector, compute the FFT, and examine the magnitude spectrum. Peaks at non‑integer multiples of the fundamental will flag syncopated intervals. Once we have that, we can cluster similar spectra to define motifs, and fit a simple HMM to predict the next state. Let’s pull the raw MIDI data and code up the vectorization.