PitchSTAR: Self-Supervised Pitch Style Transfer with Complementary Temporal and Time-Agnostic Style Representations
This is an accompanying page for the paper “PitchSTAR: Self-Supervised Pitch Style Transfer with Complementary Temporal and Time-Agnostic Style Representations”, currently under review. PitchSTAR is a self-supervised framework for arbitrary pitch style transfer (PST). The PST task is defined as generating a pitch curve given a reference ornamented pitch (style) and notes (content), represented in the Figure 1.
PitchSTAR is a flow-matching framework for pitch style transfer. It models a full style reference through note-relative pitch modulation and temporal and time-agnostic branches, which capture phrase-level organization and ornament details and limit trivial copying in a self-supervised training setting, as shown on Figure 2.
Sound Samples
In-Domain
Sample 1
Sample 2
Sample 3
Sample 4
Out-of-Domain
Sample 1
Sample 2
Sample 3
Sample 4
Effect of CFG
In this section we show the effect of the guidance CFG scale, which balances guided by style and unguided generation.
Sample 1
Sample 2
Pitch Style Classifier
In this section we plot the results of the trained Pitch Ornament Classifier, through its confusion matrices.
Test Data
This confusion matrix is obtained on the test set of the GTSinger.
Pitch Style Transfer Models
These are the matrices obtained applying on the transfer of each of the models.
Synthetic Dataset Samples
Four examples of the synthetic ornament dataset are shown. Two with ornament and two without ornament.
With Ornament 1
With Ornament 2
Without Ornament 1
Without Ornament 2