AI has learned to objectively measure the 'groove' and funkiness of music, outperforming traditional human-designed formulas.
March 31, 2026
Original Paper
Can pre-trained Deep Learning models predict groove ratings?
arXiv · 2603.27237
The Takeaway
Groove is usually considered a subjective human 'feeling' that is impossible to define mathematically. This research proves that deep learning models can actually extract and quantify 'funk' directly from audio signals, demonstrating that the human sensation of rhythm follows complex, style-dependent patterns that computers can now see.
From the abstract
This study explores the extent to which deep learning models can predict groove and its related perceptual dimensions directly from audio signals. We critically examine the effectiveness of seven state-of-the-art deep learning models in predicting groove ratings and responses to groove-related queries through the extraction of audio embeddings. Additionally, we compare these predictions with traditional handcrafted audio features. To better understand the underlying mechanics, we extend this met