A Lightweight Music Texture Transfer System
This addresses the gap in practical neural network methods for music feature transfer, though it appears incremental as it builds on existing transformation techniques from image and text domains.
The paper tackles the problem of practical music texture transfer by developing a lightweight system that converts sounds to texture spectra and uses a feed-forward transfer network, achieving convincing results in both sound effects and computational performance.
Deep learning researches on the transformation problems for image and text have raised great attention. However, present methods for music feature transfer using neural networks are far from practical application. In this paper, we initiate a novel system for transferring the texture of music, and release it as an open source project. Its core algorithm is composed of a converter which represents sounds as texture spectra, a corresponding reconstructor and a feed-forward transfer network. We evaluate this system from multiple perspectives, and experimental results reveal that it achieves convincing results in both sound effects and computational performance.