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Deep Learning for Style Transfer and Experimentation with Audio Effects and Music Creation
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A short AAAI 2024 statement proposing a planned Music+AI toolkit for style transfer, timbre transfer, natural-language-driven audio effects, and music generation, using MusicGen and related models, with no experiments reported.
Problem
Making music writing, production, and audio effect design accessible, since existing music tools are costly and require long study; current deep learning approaches to audio effects, style transfer, and timbre transfer are limited and not unified.
Approach
Proposes an all-encompassing framework combining MusicGen (for natural-language control of audio effects) with VQ-VAEs and conditional TCNs for analyzing audio, with human and model-based evaluation planned, without implementation or results.
Key results
- No quantitative results are reported; the paper is a research statement proposing future work.
Limitations
- No implementation or experiments are presented.
- Evaluation plan relies on subjective human surveys and musician feedback.
- Lyric-generation prior work could not be published due to copyright of lyric data.
Experimental setup
Builds on: MusicGen AudioCraft VQ-VAE conditional temporal convolutional networks
Closest papers by meaning
- MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models (CVPR 2024 · similarity 0.87)
- Responding to the Call: Exploring Automatic Music Composition Using a Knowledge-Enhanced Model (AAAI 2024 · similarity 0.86)
- Music Style Transfer with Time-Varying Inversion of Diffusion Models (AAAI 2024 · similarity 0.85)
- MusER: Musical Element-Based Regularization for Generating Symbolic Music with Emotion (AAAI 2024 · similarity 0.85)
- Creative Text-to-Audio Generation via Synthesizer Programming (ICML 2024 · similarity 0.85)
- Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models (ICLR 2024 · similarity 0.85)
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