most citedLearning Audio - Sheet Music Correspondences for Score Identification and Offline Alignment

17 citations · 45 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SD20194 cited

Learning Complex Basis Functions for Invariant Representations of Audio

Stefan Lattner, Monika Dörfler, Andreas Arzt

Learning features from data has shown to be more successful than using hand-crafted features for many machine learning tasks. In music information retrieval (MIR), features learned…

cs.IR20194 cited

Learning Soft-Attention Models for Tempo-invariant Audio-Sheet Music Retrieval

Stefan Balke, Matthias Dorfer, Luis Carvalho +2

Connecting large libraries of digitized audio recordings to their corresponding sheet music images has long been a motivation for researchers to develop new cross-modal retrieval s…

cs.SD20175 cited

The ACCompanion v0.1: An Expressive Accompaniment System

Carlos Cancino-Chacón, Martin Bonev, Amaury Durand +5

In this paper we present a preliminary version of the ACCompanion, an expressive accompaniment system for MIDI input. The system uses a probabilistic monophonic score follower to t…

cs.IR20171 cited

Piece Identification in Classical Piano Music Without Reference Scores

Andreas Arzt, Gerhard Widmer

In this paper we describe an approach to identify the name of a piece of piano music, based on a short audio excerpt of a performance. Given only a description of the pieces in tex…

cs.IR201717 cited

Learning Audio - Sheet Music Correspondences for Score Identification and Offline Alignment

Matthias Dorfer, Andreas Arzt, Gerhard Widmer

This work addresses the problem of matching short excerpts of audio with their respective counterparts in sheet music images. We show how to employ neural network-based cross-modal…

cs.IR201714 cited

Modeling Harmony with Skip-Grams

David R. W. Sears, Andreas Arzt, Harald Frostel +2

String-based (or viewpoint) models of tonal harmony often struggle with data sparsity in pattern discovery and prediction tasks, particularly when modeling composite events like tr…