Freesound Loop Generator

This project investigates the synthesis of new audio loops using neural networks, focusing on creative sound generation through latent space manipulation. Using the FreeSound Loop Dataset, audio samples were preprocessed through tempo normalization as well as beat and downbeat alignment to ensure rhythmic consistency and structural coherence, enabling musically relevant synthesis. The system is built around a neural autoencoder, specifically the RAVE model, which compresses audio loops into compact latent representations and reconstructs them with high-fidelity. New loops are generated by interpolating between two encoded examples, producing smooth transitions and hybrid sounds that blend characteristics from both sources. Genre classification guides latent space traversal, supporting stylistic selection and controlled blending of sounds. Furthermore, realtime manipulation of individual latent dimensions expands the system’s potential for interactive audio applications, such as live performances or dynamic sound design. Subjective evaluation demonstrated that interpolated loops are perceptually coherent and musically meaningful. Listeners reported that latent space trajectories provide expressive and controllable tools for creative composition, and, that varying the number of latent dimensions influences musical richness without compromising perceptual continuity.By combining deep generative models, musically informed preprocessing, and usercontrollable synthesis techniques, this work presents a flexible framework for creative loop-based audio generation, bridging high-quality synthesis with practical usability.