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

17 citations · 58 across the 11 of their papers we have counts for

collaborators

13 papers

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.SD20173 cited

What were you expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music

Carlos Cancino-Chacón, Maarten Grachten, David R. W. Sears +1

In this paper we present preliminary work examining the relationship between the formation of expectations and the realization of musical performances, paying particular attention…

cs.SD2017

Learning Musical Relations using Gated Autoencoders

Stefan Lattner, Maarten Grachten, Gerhard Widmer

Music is usually highly structured and it is still an open question how to design models which can successfully learn to recognize and represent musical structure. A fundamental pr…

cs.LG20175 cited

Probabilistic Generative Adversarial Networks

Hamid Eghbal-zadeh, Gerhard Widmer

We introduce the Probabilistic Generative Adversarial Network (PGAN), a new GAN variant based on a new kind of objective function. The central idea is to integrate a probabilistic…

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…