An orchestra plays a complex piece, and each group of instruments enters on its own schedule. The violins might slow down while the woodwinds keep the same tempo, and the percussion might suddenly burst into a roll. The conductor hears everything at once, but pinpointing the exact moment when the flutes, not the cellos, changed rhythm is nearly impossible. That is what signals from the brain during sleep or from the ocean surface during El Niño years look like: many voices, each living its own life.

Previously, researchers could either fix common breakpoints for all channels or smooth over changes, losing sharp jumps. The new approach works like an attentive listener who divides the entire recording into short segments where the music sounds roughly the same and builds a frequency picture for each segment. The number of segments is not known in advance, and the method decides where to draw the boundaries by exploring options through random jumps and smart search.

For El Niño data, the method suggested that the character of oscillations changed in the 1980s, assigning this event a probability of 65 percent. This is not a final verdict but rather a hint for climatologists about where to look for causes of change. In tests on synthetic signals with known breakpoints, the method made fewer errors than its competitors and ran faster than one of the most accurate previous algorithms.

Such a tool will be useful wherever the dynamics of connections between multiple signals matter: in sleep medicine for diagnosing disorders, in neuroscience for analyzing activity across different brain regions, and in climatology for monitoring ocean and atmosphere indices. The orchestra is complex, but now we have a way to hear when each group of instruments changes its tempo.