Improving Inference-Time Optimisation for Vocal Effects Style Transfer
  with a Gaussian Prior
Style Transfer with Inference-Time Optimisation (ST-ITO) is a recent approach
for transferring the applied effects of a reference audio to a raw audio track.
It optimises the effect parameters to minimise the distance between the style
embeddings of the processed audio and the reference. However, this method
treats all possible configurations equally and relies solely on the embedding
space, which can lead to unrealistic or biased results. We address this pitfall
by introducing a Gaussian prior derived from a vocal preset dataset, DiffVox,
over the parameter space. The resulting optimisation is equivalent to
maximum-a-posteriori estimation. Evaluations on vocal effects transfer on the
MedleyDB dataset show significant improvements across metrics compared to
baselines, including a blind audio effects estimator, nearest-neighbour
approaches, and uncalibrated ST-ITO. The proposed calibration reduces parameter
mean squared error by up to 33% and matches the reference style better.
Subjective evaluations with 16 participants confirm our method's superiority,
especially in limited data regimes. This work demonstrates how incorporating
prior knowledge in inference time enhances audio effects transfer, paving the
way for more effective and realistic audio processing systems.
682b539908b49a9e5088330f
Chin-Yun Yu
claimed_verified
2025-05-20T07:22:12.615Z
621d85a10e35b2fbbf3e6196
https://cdn-avatars.huggingface.co/v1/production/uploads/621d85a10e35b2fbbf3e6196/D6DMntYh-eV5fVef_Mx4Z.png
Chin-Yun Yu
user
yoyolicoris
682b539908b49a9e50883310
Marco A. Martínez-Ramírez
682b539908b49a9e50883311
Junghyun Koo
claimed_verified
2025-07-22T08:00:52.926Z
63f5c72fb607296857b14de7
/avatars/c4ae2f44e2f925ab9812ba9c046e53dd.svg
Junghyun (Tony) Koo
user
jhtonyKoo
682b539908b49a9e50883312
Wei-Hsiang Liao
682b539908b49a9e50883313
Yuki Mitsufuji
682b539908b49a9e50883314
György Fazekas
Improving Inference-Time Optimisation for Vocal Effects Style Transfer
  with a Gaussian Prior
Style Transfer with Inference-Time Optimisation (ST-ITO) is a recent approach
for transferring the applied effects of a reference audio to a raw audio track.
It optimises the effect parameters to minimise the distance between the style
embeddings of the processed audio and the reference. However, this method
treats all possible configurations equally and relies solely on the embedding
space, which can lead to unrealistic or biased results. We address this pitfall
by introducing a Gaussian prior derived from a vocal preset dataset, DiffVox,
over the parameter space. The resulting optimisation is equivalent to
maximum-a-posteriori estimation. Evaluations on vocal effects transfer on the
MedleyDB dataset show significant improvements across metrics compared to
baselines, including a blind audio effects estimator, nearest-neighbour
approaches, and uncalibrated ST-ITO. The proposed calibration reduces parameter
mean squared error by up to 33% and matches the reference style better.
Subjective evaluations with 16 participants confirm our method's superiority,
especially in limited data regimes. This work demonstrates how incorporating
prior knowledge in inference time enhances audio effects transfer, paving the
way for more effective and realistic audio processing systems.
Incorporating Gaussian prior knowledge derived from a vocal preset dataset enhances audio effects transfer by improving parameter accuracy and matching reference styles more effectively than existing methods.
Gaussian prior DiffVox maximum-a-posteriori estimation MedleyDB audio effects transfer parameter mean squared error