Can pre-trained Deep Learning models predict groove ratings?
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 methodology to analyze predictions based on source-separated instruments, thereby isolating the contributions of individual musical elements. Our analysis reveals a clear separation of groove characte...
| Authors | Axel Marmoret, Nicolas Farrugia, Jan Alexander Stupacher |
|---|---|
| Primary category | cs.SD |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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