Can Pre-trained Deep Learning Models Predict Groove Ratings?
Summary: arXiv:2603.27237v1 Announce Type: cross
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.
Introduction
Understanding groove in music has long been a focus within musicology and psychology. However, the advent of deep learning offers new opportunities to analyze and predict groove ratings directly from audio data. This research investigates the capabilities of advanced deep learning techniques in the realm of music information retrieval (MIR).
Methodology
To achieve our goals, we applied a series of deep learning models to a dataset comprising various musical genres, specifically focusing on:
- Audio signal processing
- Extraction of audio embeddings using deep learning architectures
- Comparative analysis with traditional handcrafted audio features
Furthermore, we extended our methodology to analyze predictions based on source-separated instruments, allowing us to isolate the contributions of individual musical elements.
Results
Our analysis revealed a clear separation of groove characteristics driven by the underlying musical style of the tracks, which included:
- Funk
- Pop
- Rock
These findings indicate that deep audio representations can successfully encode complex, style-dependent groove components that traditional features often miss. The models demonstrated significant predictive power, particularly when trained on genre-specific data.
Discussion
This study underscores the importance of utilizing deep learning in the field of music analysis. The ability of these models to capture the nuanced characteristics of groove suggests that representation learning can play a pivotal role in enhancing predictive Music Information Retrieval methodologies. Our work opens avenues for further exploration of how different musical elements contribute to the overall perception of groove.
Conclusion
In conclusion, our research demonstrates the strong potential of advanced deep learning models in capturing the multifaceted concept of groove. By leveraging deep audio embeddings, we can achieve improved accuracy in predicting groove ratings, paving the way for more sophisticated music analysis tools that could benefit artists, producers, and researchers alike.
Future Work
Future studies could expand upon this foundation by exploring additional genres and incorporating larger datasets to validate our findings. Moreover, investigating the impact of different audio processing techniques on model performance may yield further insights into the complex relationship between musical elements and groove perception.