A Lightweight Music Texture Transfer System

TL;DR

This paper introduces a lightweight, practical system for transferring music textures using neural networks, combining texture spectra representation, a reconstructor, and a transfer network, with promising results in sound quality and efficiency.

Contribution

It presents a novel, open-source music texture transfer system that is efficient and practical, addressing limitations of previous neural network approaches.

Findings

Achieves convincing sound texture transfer results

Demonstrates good computational performance

Provides an open-source implementation

Abstract

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.