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Found 9 papers, 0 papers with code
Towards Context-Aware Neural Performance-Score Synchronisation
This PhD furthers the development of performance-score synchronisation research by proposing data-driven, context-aware alignment approaches, on three fronts: Firstly, I replace the handcrafted features by employing a metric learning based approach that is adaptable to different acoustic settings and performs well in data-scarce conditions.
A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization
Our method is also robust to structural differences between the performance and score sequences, which is a common limitation of standard alignment approaches.
Structure-Aware Audio-to-Score Alignment using Progressively Dilated Convolutional Neural Networks
The identification of structural differences between a music performance and the score is a challenging yet integral step of audio-to-score alignment, an important subtask of music information retrieval.
Learning Frame Similarity using Siamese networks for Audio-to-Score Alignment
Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece.
A Hybrid Approach to Audio-to-Score Alignment
Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece.
Multi-source transformer with combined losses for automatic post editing
In the first subtask, our system improves over the baseline up to -5. 3 TER and +8. 23 BLEU points ranking second out of 11 submitted runs.
