arXiv subjects

Carlos Cancino-Chacón

Publications and source records attributed to Carlos Cancino-Chacón.

16 recordsLinked to original sources

arXiv2025

Matchmaker: An Open-source Library for Real-time Piano Score Following and Systematic Evaluation

Real-time music alignment, also known as score following, is a fundamental MIR task with a long history and is essential for many interactive applications. Despite its importance, there has not been a unified open framework for comparing models, largely due to the inherent complexity of real-time processing and the language- or system-dependent implementations. In addition, low compatibility with the existing MIR environment has made it difficult to develop benchmarks using large datasets available in recent years. While new studies based on established methods (e.g., dynamic programming, probabilistic models) have emerged, most evaluations compare models only within the same family or on small sets of test data. This paper introduces Matchmaker, an open-source Python library for real-time music alignment that is easy to use and compatible with modern MIR libraries. Using this, we systematically compare methods along two dimensions: music representations and alignment methods. We evaluated our approach on a large test set of solo piano music from the (n)ASAP, Batik, and Vienna4x22 datasets with a comprehensive set of metrics to ensure robust assessment. Our work aims to establish a benchmark framework for score-following research while providing a practical tool that developers can easily integrate into their applications.

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arXiv2024

Towards Musically Informed Evaluation of Piano Transcription Models

Automatic piano transcription models are typically evaluated using simple frame- or note-wise information retrieval (IR) metrics. Such benchmark metrics do not provide insights into the transcription quality of specific musical aspects such as articulation, dynamics, or rhythmic precision of the output, which are essential in the context of expressive performance analysis. Furthermore, in recent years, MAESTRO has become the de-facto training and evaluation dataset for such models. However, inference performance has been observed to deteriorate substantially when applied on out-of-distribution data, thereby questioning the suitability and reliability of transcribed outputs from such models for specific MIR tasks. In this work, we investigate the performance of three state-of-the-art piano transcription models in two experiments. In the first one, we propose a variety of musically informed evaluation metrics which, in contrast to the IR metrics, offer more detailed insight into the musical quality of the transcriptions. In the second experiment, we compare inference performance on real-world and perturbed audio recordings, and highlight musical dimensions which our metrics can help explain. Our experimental results highlight the weaknesses of existing piano transcription metrics and contribute to a more musically sound error analysis of transcription outputs.

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arXiv2023

The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist

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.

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arXiv2022

Partitura: A Python Package for Symbolic Music Processing

Partitura is a lightweight Python package for handling symbolic musical information. It provides easy access to features commonly used in music information retrieval tasks, like note arrays (lists of timed pitched events) and 2D piano roll matrices, as well as other score elements such as time and key signatures, performance directives, and repeat structures. Partitura can load musical scores (in MEI, MusicXML, Kern, and MIDI formats), MIDI performances, and score-to-performance alignments. The package includes some tools for music analysis, such as automatic pitch spelling, key signature identification, and voice separation. Partitura is an open-source project and is available at https://github.com/CPJKU/partitura/.

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arXiv2022

The match file format: Encoding Alignments between Scores and Performances

This paper presents the specifications of match: a file format that extends a MIDI human performance with note-, beat-, and downbeat-level alignments to a corresponding musical score. This enables advanced analyses of the performance that are relevant for various tasks, such as expressive performance modeling, score following, music transcription, and performer classification. The match file includes a set of score-related descriptors that makes it usable also as a bare-bones score representation. For applications that require the use of structural score elements (e.g., voices, parts, beams, slurs), the match file can be easily combined with the symbolic score. To support the practical application of our work, we release a corrected and upgraded version of the Vienna4x22 dataset of scores and performances aligned with match files.

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arXiv2022

partitura: A Python Package for Handling Symbolic Musical Data

This demo paper introduces partitura, a Python package for handling symbolic musical information. The principal aim of this package is to handle richly structured musical information as conveyed by modern staff music notation. It provides a much wider range of possibilities to deal with music than the more reductive (but very common) piano roll-oriented approach inspired by the MIDI standard. The package is an open source project and is available on GitHub.

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arXiv2022

Beyond synchronization: Body gestures and gaze direction in duo performance

In this chapter, we focus on two main categories of visual interaction: body gestures and gaze direction. Our focus on body gestures is motivated by research showing that gesture patterns often change during joint action tasks to become more predictable (van der Wel et al., 2016). Moreover, coordination sometimes emerges between musicians at the level of body sway (Chang et al., 2017). Our focus on gaze direction was motivated by the fact that gaze can serve simultaneously as a means of obtaining information about the world and as a means of communicating one's own attention and intent.

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arXiv2020

On the Characterization of Expressive Performance in Classical Music: First Results of the Con Espressione Game

A piece of music can be expressively performed, or interpreted, in a variety of ways. With the help of an online questionnaire, the Con Espressione Game, we collected some 1,500 descriptions of expressive character relating to 45 performances of 9 excerpts from classical piano pieces, played by different famous pianists. More specifically, listeners were asked to describe, using freely chosen words (preferably: adjectives), how they perceive the expressive character of the different performances. In this paper, we offer a first account of this new data resource for expressive performance research, and provide an exploratory analysis, addressing three main questions: (1) how similarly do different listeners describe a performance of a piece? (2) what are the main dimensions (or axes) for expressive character emerging from this?; and (3) how do measurable parameters of a performance (e.g., tempo, dynamics) and mid- and high-level features that can be predicted by machine learning models (e.g., articulation, arousal) relate to these expressive dimensions? The dataset that we publish along with this paper was enriched by adding hand-corrected score-to-performance alignments, as well as descriptive audio features such as tempo and dynamics curves.

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arXiv2020

Real-time visualisation of fugue played by a string quartet

We present a new system for real-time visualisation of music performance, focused for the moment on a fugue played by a string quartet. The basic principle is to offer a visual guide to better understand music using strategies that should be as engaging, accessible and effective as possible. The pitch curves related to the separate voices are drawn on a space whose temporal axis is normalised with respect to metrical positions, and aligned vertically with respect to their thematic and motivic classification. Aspects related to tonality are represented as well. We describe the underlying technologies we have developed and the technical setting. In particular, the rhythmical and structural representation of the piece relies on real-time polyphonic audio-to-score alignment using online dynamic time warping. The visualisation will be presented at a concert of the Danish String Quartet, performing the last piece of The Art of Fugue by Johann Sebastian Bach.

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arXiv2019

Moving to Communicate, Moving to Interact: Patterns of Body Motion in Musical Duo Performance

Skilled ensemble musicians coordinate with high precision, even when improvising or interpreting loosely-defined notation. Successful coordination is supported primarily through shared attention to the musical output; however, musicians also interact visually, particularly when the musical timing is irregular. This study investigated the performance conditions that encourage visual signalling and interaction between ensemble members. Piano and clarinet duos rehearsed a new piece as their body motion was recorded. Analyses of head movement showed that performers communicated gesturally following held notes. Gesture patterns became more consistent as duos rehearsed, though consistency dropped again during a final performance given under no-visual-contact conditions. Movements were smoother and interperformer coordination was stronger during irregularly-timed passages than elsewhere in the piece, suggesting heightened visual interaction. Performers moved more after rehearsing than before, and more when they could see each other than when visual contact was occluded. Periods of temporal instability and increased familiarity with the music and co-performer seem to encourage visual interaction, while specific communicative gestures are integrated into performance routines through rehearsal. We propose that visual interaction may support successful ensemble performance by affirming coordination throughout periods of temporal instability and serving as a social motivator to promote creative risk-taking.

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arXiv2019

A Convolutional Approach to Melody Line Identification in Symbolic Scores

In many musical traditions, the melody line is of primary significance in a piece. Human listeners can readily distinguish melodies from accompaniment; however, making this distinction given only the written score -- i.e. without listening to the music performed -- can be a difficult task. Solving this task is of great importance for both Music Information Retrieval and musicological applications. In this paper, we propose an automated approach to identifying the most salient melody line in a symbolic score. The backbone of the method consists of a convolutional neural network (CNN) estimating the probability that each note in the score (more precisely: each pixel in a piano roll encoding of the score) belongs to the melody line. We train and evaluate the method on various datasets, using manual annotations where available and solo instrument parts where not. We also propose a method to inspect the CNN and to analyze the influence exerted by notes on the prediction of other notes; this method can be applied whenever the output of a neural network has the same size as the input.

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arXiv2019

User Curated Shaping of Expressive Performances

Musicians produce individualized, expressive performances by manipulating parameters such as dynamics, tempo and articulation. This manipulation of expressive parameters is informed by elements of score information such as pitch, meter, and tempo and dynamics markings (among others). In this paper we present an interactive interface that gives users the opportunity to explore the relationship between structural elements of a score and expressive parameters. This interface draws on the basis function models, a data-driven framework for expressive performance. In this framework, expressive parameters are modeled as a function of score features, i.e., numerical encodings of specific aspects of a musical score, using neural networks. With the proposed interface, users are able to weight the contribution of individual score features and understand how an expressive performance is constructed.

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arXiv2018

A Computational Study of the Role of Tonal Tension in Expressive Piano Performance

Expressive variations of tempo and dynamics are an important aspect of music performances, involving a variety of underlying factors. Previous work has showed a relation between such expressive variations (in particular expressive tempo) and perceptual characteristics derived from the musical score, such as musical expectations, and perceived tension. In this work we use a computational approach to study the role of three measures of tonal tension proposed by Herremans and Chew (2016) in the prediction of expressive performances of classical piano music. These features capture tonal relationships of the music represented in Chew's spiral array model, a three dimensional representation of pitch classes, chords and keys constructed in such a way that spatial proximity represents close tonal relationships. We use non-linear sequential models (recurrent neural networks) to assess the contribution of these features to the prediction of expressive dynamics and expressive tempo using a dataset of Mozart piano sonatas performed by a professional concert pianist. Experiments of models trained with and without tonal tension features show that tonal tension helps predict change of tempo and dynamics more than absolute tempo and dynamics values. Furthermore, the improvement is stronger for dynamics than for tempo.

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arXiv2017

The ACCompanion v0.1: An Expressive Accompaniment System

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 track the position of the soloist in the score, and a linear Gaussian model to compute tempo updates. The expressiveness of the system is powered by the Basis-Mixer, a state-of-the-art computational model of expressive music performance. The system allows for expressive dynamics, timing and articulation.

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arXiv2017

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

In this paper we present preliminary work examining the relationship between the formation of expectations and the realization of musical performances, paying particular attention to expressive tempo and dynamics. To compute features that reflect what a listener is expecting to hear, we employ a computational model of auditory expectation called the Information Dynamics of Music model (IDyOM). We then explore how well these expectancy features -- when combined with score descriptors using the Basis-Function modeling approach -- can predict expressive tempo and dynamics in a dataset of Mozart piano sonata performances. Our results suggest that using expectancy features significantly improves the predictions for tempo.

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arXiv2017

From Bach to the Beatles: The simulation of human tonal expectation using ecologically-trained predictive models

Tonal structure is in part conveyed by statistical regularities between musical events, and research has shown that computational models reflect tonal structure in music by capturing these regularities in schematic constructs like pitch histograms. Of the few studies that model the acquisition of perceptual learning from musical data, most have employed self-organizing models that learn a topology of static descriptions of musical contexts. Also, the stimuli used to train these models are often symbolic rather than acoustically faithful representations of musical material. In this work we investigate whether sequential predictive models of musical memory (specifically, recurrent neural networks), trained on audio from commercial CD recordings, induce tonal knowledge in a similar manner to listeners (as shown in behavioral studies in music perception). Our experiments indicate that various types of recurrent neural networks produce musical expectations that clearly convey tonal structure. Furthermore, the results imply that although implicit knowledge of tonal structure is a necessary condition for accurate musical expectation, the most accurate predictive models also use other cues beyond the tonal structure of the musical context.

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