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Patricia Hu

dblp:331/1633 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Audio and music processing · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing › music generation
automatic accompaniment
0.712023
The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist · IJCAI 2023
Audio and music processing › music generation
expressive performance generation
0.712023
The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist · IJCAI 2023
Audio and music processing
music performance
0.712023
The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist · IJCAI 2023
Audio and music processing › music information retrieval › music alignment
score following
0.712023
The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist · IJCAI 2023

Methods — techniques the papers use, named apart from their topics

real-time prediction · 0.7MIDI · 0.7
YearPublicationVenuePosition
2023 The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist
abstract
This paper introduces the ACCompanion, an expressive accompaniment system. Similarly to a musician who accompanies a soloist playing a given musical piece, our system can produce a human-like rendition of the accompaniment part that follows the soloist's choices in terms of tempo, dynamics, and articulation. The ACCompanion works in the symbolic domain, i.e., it needs a musical instrument capable of producing and playing MIDI data, with explicitly encoded onset, offset, and pitch for each played note. We describe the components that go into such a system, from real-time score following and prediction to expressive performance generation and online adaptation to the expressive choices of the human player. Based on our experience with repeated live demonstrations in front of various audiences, we offer an analysis of the challenges of combining these components into a system that is highly reactive and precise, while still a reliable musical partner, robust to possible performance errors and responsive to expressive variations.
Carlos Eduardo Cancino-Chacón, Silvan Peter, Patricia Hu, Emmanouil Karystinaios, Florian Henkel, Francesco Foscarin, Gerhard Widmer
IJCAI3