Device-Guided Music Transfer
This addresses device-specific music adaptation for users lacking certain hardware, though it appears incremental as it builds on existing transfer methods.
The paper tackles the problem of adapting music playback for unseen devices by processing speaker frequency response curves with a vision-language model to extract device embeddings, achieving effective speaker-style transfer and robust few-shot adaptation.
Device-guided music transfer adapts playback across unseen devices for users who lack them. Existing methods mainly focus on modifying the timbre, rhythm, harmony, or instrumentation to mimic genres or artists, overlooking the diverse hardware properties of the playback device (i.e., speaker). Therefore, we propose DeMT, which processes a speaker's frequency response curve as a line graph using a vision-language model to extract device embeddings. These embeddings then condition a hybrid transformer via feature-wise linear modulation. Fine-tuned on a self-collected dataset, DeMT enables effective speaker-style transfer and robust few-shot adaptation for unseen devices, supporting applications like device-style augmentation and quality enhancement.