3 citations · 4 across the 6 of their papers we have counts for
6 papers
Exploring System Adaptations For Minimum Latency Real-Time Piano Transcription
Patricia Hu, Silvan David Peter, Jan Schlüter +1
Advances in neural network design and the availability of large-scale labeled datasets have driven major improvements in piano transcription. Existing approaches target either offl…
TheGlueNote: Learned Representations for Robust and Flexible Note Alignment
Silvan David Peter, Gerhard Widmer
Note alignment refers to the task of matching individual notes of two versions of the same symbolically encoded piece. Methods addressing this task commonly rely on sequence alignm…
DExter: Learning and Controlling Performance Expression with Diffusion Models
Huan Zhang, Shreyan Chowdhury, Carlos Eduardo Cancino-Chacón +3
In the pursuit of developing expressive music performance models using artificial intelligence, this paper introduces DExter, a new approach leveraging diffusion probabilistic mode…
Are we describing the same sound? An analysis of word embedding spaces of expressive piano performance
Silvan David Peter, Shreyan Chowdhury, Carlos Eduardo Cancino-Chacón +1
Semantic embeddings play a crucial role in natural language-based information retrieval. Embedding models represent words and contexts as vectors whose spatial configuration is der…
Sounding Out Reconstruction Error-Based Evaluation of Generative Models of Expressive Performance
Silvan David Peter, Carlos Eduardo Cancino-Chacón, Emmanouil Karystinaios +1
Generative models of expressive piano performance are usually assessed by comparing their predictions to a reference human performance. A generative algorithm is taken to be better…
Symbolic Music Representations for Classification Tasks: A Systematic Evaluation
Huan Zhang, Emmanouil Karystinaios, Simon Dixon +2
Music Information Retrieval (MIR) has seen a recent surge in deep learning-based approaches, which often involve encoding symbolic music (i.e., music represented in terms of discre…