What is beat tracking?

Beat tracking is a task in audio processing and music analysis that involves automatically detecting the rhythmic beats in a musical signal. The goal is to identify the time stamps corresponding to the main rhythm of the composition.

This is important for applications such as synchronizing music with visual effects, automatic remix creation, tempo analysis, and track structure analysis.

In machine learning, methods based on convolutional neural networks (CNN) or recurrent neural networks (RNN) are often used for beat tracking, analyzing audio spectrograms and predicting the likely positions of beats.

A simplified approach example:

1. Convert audio into a spectrogram.
2. Use a model that outputs the probability of a beat in each time window.
3. Apply post-processing (e.g., Viterbi algorithm) to extract the most probable sequence of beats.

Thus, beat tracking helps understand the rhythmic structure of music and serves as a foundation for many musical applications.