Expressive Music Performance Research Articles

Anti-Pastoral and the Prophetic Mode in Moddi’s Climate Songs

This article explores aspects of apocalypse and pastoral in the climate themed song lyrics of Norwegian indie folk artist Moddi. Through close listening and contextualizing of the songs “Noens ark” (“Someone’s ark”, 2013) and “En sang om fly” (“A song about planes”, 2013), apocalypse and pastoral emerge as key motifs. These are navigated in an exploration of a conflicted Norwegian response to global warming. The songs express critique of unrelenting pursuits of economic growth and personal consumption even as the future imposes itself on the present. In two different ways, elements of indie folk music are tailored to express an ontologically unsettling encounter with a global environmental risk scenario. “Noens ark” employs forceful instrumentation and polyrhythms to evoke a sense of alarm and urgency called for by the projection of future disaster. “En sang om fly” is a soft composition projecting nostalgia for the material conditions of the present. The desire for these conditions ultimately locks the singer in a cycle of repetition that destabilizes the very conditions he desires. The nuances afforded by this expression in musical performance allows song lyrics to sustain contradictory attitudes towards globalization. In their pursuit of these contradictions, Moddi’s climate songs can be seen as part of an anti-pastoral tradition. Features of musical genre and a prophetic lyric mode are employed to evoke and subvert a pastoral response to apocalypticism. By musically sustaining contradictions between pastoral and apocalypse, Moddi’s songs reflect a conflict between Norwegian identity and a global cultural imaginary in the face of global warming.

A STUDY ON ARTISTIC STYLES AND ARTISTISM IN MUSICAL PERFORMANCE

Musical performance represents a secondary creation of musical works, necessitating performers to possess a high level of artistic cultivation and a deep understanding of the artistic style inherent in the music. Performers must bring unique aesthetic characteristics to their performances. The performer's personality, temperament, and experience, along with the local cultural background, influence the artistic style in musical performance. Therefore, performers must specifically analyze these influencing factors and explore methods of artistic expression in musical performance. By accurately understanding and interpreting musical works, performers can achieve a perfect rendition.

Multimodal robotic music performance art based on GRU-GoogLeNet model fusing audiovisual perception.

The field of multimodal robotic musical performing arts has garnered significant interest due to its innovative potential. Conventional robots face limitations in understanding emotions and artistic expression in musical performances. Therefore, this paper explores the application of multimodal robots that integrate visual and auditory perception to enhance the quality and artistic expression in music performance. Our approach involves integrating GRU (Gated Recurrent Unit) and GoogLeNet models for sentiment analysis. The GRU model processes audio data and captures the temporal dynamics of musical elements, including long-term dependencies, to extract emotional information. The GoogLeNet model excels in image processing, extracting complex visual details and aesthetic features. This synergy deepens the understanding of musical and visual elements, aiming to produce more emotionally resonant and interactive robot performances. Experimental results demonstrate the effectiveness of our approach, showing significant improvements in music performance by multimodal robots. These robots, equipped with our method, deliver high-quality, artistic performances that effectively evoke emotional engagement from the audience. Multimodal robots that merge audio-visual perception in music performance enrich the art form and offer diverse human-machine interactions. This research demonstrates the potential of multimodal robots in music performance, promoting the integration of technology and art. It opens new realms in performing arts and human-robot interactions, offering a unique and innovative experience. Our findings provide valuable insights for the development of multimodal robots in the performing arts sector.

Music performance style transfer for learning expressive musical performance

Music performance style transfer for learning expressive musical performance

Research in Computational Expressive Music Performance and Popular Music Production: A Potential Field of Application?

In music, the interpreter manipulates the performance parameters in order to offer a sonic rendition of the piece that is capable of conveying specific expressive intentions. Since the 1980s, there has been growing interest in expressive music performance (EMP) and its computational modeling. This research field has two fundamental objectives: understanding the phenomenon of human musical interpretation and the automatic generation of expressive performances. Rule-based, statistical, machine, and deep learning approaches have been proposed, most of them devoted to the classical repertoire, in particular to piano pieces. On the contrary, we introduce the role of expressive performance within popular music and the contemporary ecology of pop music production based on the use of digital audio workstations (DAWs) and virtual instruments. After an analysis of the tools related to expressiveness commonly available to modern producers, we propose a detailed survey of research into the computational EMP field, highlighting the potential and limits of what is present in the literature with respect to the context of popular music, which by its nature cannot be completely superimposed to the classical one. In the concluding discussion, we suggest possible lines of future research in the field of computational expressiveness applied to pop music.

Musical Expression in Music Performance and Its Enhancement Pathway

Musical Expression in Music Performance and Its Enhancement Pathway

Movement Experiences in Preschool Music Classes

Active bodily involvement in music is a key component of musical understanding. Rudolf von Laban developed a movement framework introducing the “effort elements” of time, space, weight, and flow. He proposed that by experiencing combinations of these elements, individuals internalize a movement vocabulary that fosters expressive music performance. In this article, I discuss findings that have confirmed the importance of active bodily engagement in enhancing musical comprehension, and share practical considerations drawn from my experience in conducting preschool music classes based on Laban’s movement framework. The suggested playful activities provide music educators with a new perspective on incorporating movement in preschool music classes.

Capture and express, question and understand: Gloves in gestural electronic music performance.

Gesture-based musical performance with on-body sensing represents a particular case of wearable connection. Gloves and hand-sensing interfaces connected to real-time digital sound production and transformation processes enable empty-handed, expressive musical performance styles. In this article, the origins and developments of this practice as well as a specific use case are investigated. By taking the technical, cognitive, and cultural dimensions of this media performance as foundation, a reflection on the value, limitations, and opportunities of computational approaches to movement translation and analysis is carried out. The insights uncover how the multilayered, complex artistic situations produced by these performances are rich in intersections and represent potent amplifiers for investigating corporeal presence and affective engagement. This allows to identify problems and opportunities of existing research approaches and core issues to be solved in the domain of movement, music, interaction technology, and performance research.

Theoretical Framework for Facilitating Young Musicians' Learning of Expressive Performance.

Since communication and expression are central aspects of music performance it is important to develop a systematic pedagogy of teaching children and teenagers expressiveness. Although research has been growing in this area a comprehensive literature review that unifies the different approaches to teaching young musicians expressiveness has been lacking. Therefore, the aim of this article is to provide an overview of literature related to teaching and learning of expressiveness from music psychology and music education research in order to build a new theoretical framework for teaching and learning expressive music performance in instrumental music lessons with children and teenagers. The article will start with a brief discussion of interpretation and expression in music performance, before providing an overview of studies that investigated teaching and learning of performance expression in instrumental music education with adults and children. On the foundation of this research a theoretical framework for dialogic teaching and learning of expressive music performance will be proposed and the rationale explained. Dialogic teaching can be useful for scaffolding young musicians’ learning of expressivity as open questions can stimulate thinking about the interpretation and may serve to connect musical ideas to the embodied experience of the learner. A “toolkit” for teaching and learning of expressiveness will be presented for practical application in music lessons. In addition, a theoretical model will be proposed to further our understanding of teaching and learning of expressive music performance as a multifaceted and interactive process that is embedded in the context of tutors’ and learners’ experiences and environment. Finally, implications of this framework and suggestions for future research will be discussed.

Method Development for Multimodal Data Corpus Analysis of Expressive Instrumental Music Performance.

Musical performance is a multimodal experience, for performers and listeners alike. This paper reports on a pilot study which constitutes the first step toward a comprehensive approach to the experience of music as performed. We aim at bridging the gap between qualitative and quantitative approaches, by combining methods for data collection. The purpose is to build a data corpus containing multimodal measures linked to high-level subjective observations. This will allow for a systematic inclusion of the knowledge of music professionals in an analytic framework, which synthesizes methods across established research disciplines. We outline the methods we are currently developing for the creation of a multimodal data corpus dedicated to the analysis and exploration of instrumental music performance from the perspective of embodied music cognition. This will enable the study of the multiple facets of instrumental music performance in great detail, as well as lead to the development of music creation techniques that take advantage of the cross-modal relationships and higher-level qualities emerging from the analysis of this multi-layered, multimodal corpus. The results of the pilot project suggest that qualitative analysis through stimulated recall is an efficient method for generating higher-level understandings of musical performance. Furthermore, the results indicate several directions for further development, regarding observational movement analysis, and computational analysis of coarticulation, chunking, and movement qualities in musical performance. We argue that the development of methods for combining qualitative and quantitative data are required to fully understand expressive musical performance, especially in a broader scenario in which arts, humanities, and science are increasingly entangled. The future work in the project will therefore entail an increasingly multimodal analysis, aiming to become as holistic as is music in performance.

Brain-Computer Music Interface for Music Expression

Active music listening is a way of listening to music through active interactions. In this paper we present an expressive brain-computer interactive music system for active music listening, which allows listeners to manipulate expressive parameters in music performances using their emotional state, as detected by a brain-computer interface. The proposed system is divided in two parts: a real-time system able to detect listeners’ emotional state from their EEG data, and a real-time expressive music performance system capable of adapting the expressive parameters of music based on the detected listeners’ emotion. We comment on an application of our system as a music neurofeedback system to alleviate depression in elderly people.

The Limits of Traverso; Exploring the sound possibilities of traverso through contemporary music

In ‘early music’ performance today ΄sound΄ does not get as much attention as other expressive devices, even though the sound was an inseparable part of expression in music performance in 18th century. This research attempts to explore the traverso and its expressive sound possibilities when placed in the field of contemporary music. The tonal capabilities of the traverso will be viewed from the perspectives of both 18th century sources and modern-day ΄early΄ and ΄classical music practice΄. The research considers what have sometimes been seen as the instrument´s “limitations” and "imperfections", asking how they could be positively exploited in contemporary music. At the same time, so-called ‘extended techniques ’for the modern flute are explored on the baroque flute, by a study and performance of two contemporary compositions for traverso solo. At the end, I will be looking at how this untraditional perception of traverso sound could open up our expressive imagination in performance of the traditional 18th century traverso repertoire. The research hopes to bring some new inspirations for traverso players as well as other ΄early music΄ performers, and to clarify the distinctive role of sound as an expressive device in early instruments. It also hopes to inspire composers to write more contemporary acoustic music using the specific sonority of this instrument. The presentation will be given in the form of performance-lecture.

Young Musicians' Learning of Expressive Performance: The Importance of Dialogic Teaching and Modeling

Until recently little was known through systematic research about effective teaching methods to enhance children's expressiveness in music performance. A previous experimental study indicated that a dialogic teaching approach, consisting of questions and dialogue, improves pupils' expressive performance. Developing from this, a participatory action research study was conducted with the following objectives: (1) To explore how dialogic teaching and learning of expressiveness can be used in weekly individual instrumental lessons; (2) to investigate whether instrumental tutors find a dialogic teaching approach useful for facilitating pupils' learning of expressiveness; and (3) to explore what other complementing instructional modes tutors would like to employ. (4) To investigate pupils' views on their learning of expressiveness; and (5) pupils' views on the instructional strategies used for teaching expressiveness. Five instrumental music tutors participated in this research with two or three of their pupils (11 girls in total, aged 8–15, playing various instruments) for 4 months. Pupils played in informal performance sessions at the start, middle, and end of the project. Lessons and performances were video-recorded. Music diaries, questionnaires and video-stimulated recall interviews were used to collect information about participants' views. Participating tutors used mainly dialogic teaching, modeling, and playing along with pupils. Tutors thought that teaching and learning expressiveness is a complex process wherein “everything is intertwined”; several methods can be used within a dialogic teaching approach for working on various teaching aims. Aural modeling combined with dialogic teaching was seen as especially useful. Pupils' accounts indicate that they had learned to think about the musical character and how to convey this in performance. Tutors' questions had stimulated pupils' reflection and raised their awareness of the musical meaning, while teachers' modeling had helped to build up an aural picture of the music which had facilitated pupils' learning. The dialogic teaching approach supported by modeling had generated improved expressiveness in lessons and contributed to a growing sense of achievement, confidence, self-efficacy, and musical agency. These findings demonstrate the importance of dialogic teaching supported by modeling for meaningful instrumental music education as this can stimulate pupils' thinking, thus facilitating their learning and enhancing their expressiveness.

Understanding biomechanical constraints for modelling expressive performance: A guitar case study

Computer-generated musical performances are often criticised for being unable to match the expressivity found in performances by humans. In this paper, we discuss the role of errors when modelling expressive music performance, which concerns the physical manipulation of the instrument by the performer. In addition, a study on the motor and biomechanical processes of the guitar performance is presented. Some findings on the speed, precision, and force of a guitarist´s left-hand are presented overarching questions regarded to the way a human body performs actions when playing the guitar, and the possibility to err in such context.

Comparison of beliefs about teaching and learning of emotional expression in music performance between Spanish and English HE students of music

Despite an increase in research on emotional expression in music and its teaching and learning, little is known about the beliefs and conceptualizations that students hold regarding musical expression, and how they differ depending on educational context. To address this gap, a comparison was made between a sample of 79 UK and 117 Spanish higher education students of music, who were asked to indicate their beliefs about expressivity, most useful instructional methods to develop expressive performance, and factors that influence expressiveness and its teaching and learning. Results indicated agreement, but also several significant differences across cohorts. UK students endorsed the idea more strongly that musical context (i.e., piece and instrument) influences expressivity and the choice of teaching strategy, while Spanish students linked expressivity more strongly to particular music-emotional characteristics. Both groups considered using technical explanation as the better method for teaching expressivity, whilst modelling was considered the worst. On the other hand, they agreed that the choice of the teaching approach should depend on the age of the student with modelling being preferred for younger age groups, and technique for adults only. These results highlight differences in the understanding of musical expressivity that parallel academic debates on emotional vs. stylistic expressiveness.

A Computational Model of Immanent Accent Salience in Tonal Music.

Accents are local musical events that attract the attention of the listener, and can be either immanent (evident from the score) or performed (added by the performer). Immanent accents involve temporal grouping (phrasing), meter, melody, and harmony; performed accents involve changes in timing, dynamics, articulation, and timbre. In the past, grouping, metrical and melodic accents were investigated in the context of expressive music performance. We present a novel computational model of immanent accent salience in tonal music that automatically predicts the positions and saliences of metrical, melodic and harmonic accents. The model extends previous research by improving on preliminary formulations of metrical and melodic accents and introducing a new model for harmonic accents that combines harmonic dissonance and harmonic surprise. In an analysis-by-synthesis approach, model predictions were compared with data from two experiments, respectively involving 239 sonorities and 638 sonorities, and 16 musicians and 5 experts in music theory. Average pair-wise correlations between raters were lower for metrical (0.27) and melodic accents (0.37) than for harmonic accents (0.49). In both experiments, when combining all the raters into a single measure expressing their consensus, correlations between ratings and model predictions ranged from 0.43 to 0.62. When different accent categories of accents were combined together, correlations were higher than for separate categories (r = 0.66). This suggests that raters might use strategies different from individual metrical, melodic or harmonic accent models to mark the musical events.

This time with feeling: learning expressive musical performance

Music generation has generally been focused on either creating scores or interpreting them. We discuss differences between these two problems and propose that, in fact, it may be valuable to work in the space of direct performance generation: jointly predicting the notes and also their expressive timing and dynamics. We consider the significance and qualities of the dataset needed for this. Having identified both a problem domain and characteristics of an appropriate dataset, we show an LSTM-based recurrent network model that subjectively performs quite well on this task. Critically, we provide generated examples. We also include feedback from professional composers and musicians about some of these examples.

Decoding emotions in expressive music performances: A multi-lab replication and extension study

ABSTRACTWith over 560 citations reported on Google Scholar by April 2018, a publication by Juslin and Gabrielsson (1996) presented evidence supporting performers’ abilities to communicate, with high accuracy, their intended emotional expressions in music to listeners. Though there have been related studies published on this topic, there has yet to be a direct replication of this paper. A replication is warranted given the paper’s influence in the field and the implications of its results. The present experiment joins the recent replication effort by producing a five-lab replication using the original methodology. Expressive performances of seven emotions (e.g. happy, sad, angry, etc.) by professional musicians were recorded using the same three melodies from the original study. Participants (N = 319) were presented with recordings and rated how well each emotion matched the emotional quality using a 0–10 scale. The same instruments from the original study (i.e. violin, voice, and flute) were used, with the addition of piano. In an effort to increase the accessibility of the experiment and allow for a more ecologically-valid environment, the recordings were presented using an internet-based survey platform. As an extension to the original study, this experiment investigated how musicality, emotional intelligence, and emotional contagion might explain individual differences in the decoding process. Results found overall high decoding accuracy (57%) when using emotion ratings aggregated for the sample of participants, similar to the method of analysis from the original study. However, when decoding accuracy was scored for each participant individually the average accuracy was much lower (31%). Unlike in the original study, the voice was found to be the most expressive instrument. Generalised Linear Mixed Effects Regression modelling revealed that musical training and emotional engagement with music positively influences emotion decoding accuracy.

Computational Models of Expressive Music Performance: A Comprehensive and Critical Review

Expressive performance is an indispensable part of music making. When playing a piece, expert performers shape various parameters (tempo, timing, dynamics, intonation, articulation, etc.) in ways that are not prescribed by the notated score, in this way producing an expressive rendition that brings out dramatic, affective, and emotional qualities that may engage and affect the listeners. Given the central importance of this skill for many kinds of music, expressive performance has become an important research topic for disciplines like musicology, music psychology, etc. This paper focuses on a specific thread of research: work on computational music performance models. Computational models are attempts at codifying hypotheses about expressive performance in terms of mathematical formulas or computer programs, so that they can be evaluated in systematic and quantitative ways. Such models can serve at least two main purposes: they permit us to systematically study certain hypotheses regarding performance; and they can be used as tools to generate automated or semi-automated performances, in artistic or educational contexts. The present article presents an up-to-date overview of the state of the art in this domain. We explore recent trends in the field, such as a strong focus on data-driven (machine learning); a growing interest in interactive expressive systems, such as conductor simulators and automatic accompaniment systems; and an increased interest in exploring cognitively plausible features and models. We provide an in-depth discussion of several important design choices in such computer models, and discuss a crucial (and still largely unsolved) problem that is hindering systematic progress: the question of how to evaluate such models in scientifically and musically meaningful ways. From all this, we finally derive some research directions that should be pursued with priority, in order to advance the field and our understanding of expressive music performance.

Simple mappings, expressive movement: a qualitative investigation into the end-user mapping design of experienced mid-air musicians

ABSTRACTIn a New Interface for Musical Expression (NIME), the design of the relationship between a musician’s actions and the instrument’s sound response is critical in creating instruments that facilitate expressive music performance. A growing body of NIMEs expose this design task to the end performer themselves, leading to the possibility of new insights into NIME mapping design: what can be learned from the mapping design strategies of practicing musicians? This research contributes a qualitative study of four highly experienced users of an end-user mapping instrument to examine their mapping practice. The study reveals that the musicians focus on designing simple, robust mappings that minimize errors, embellishing these control gestures with theatrical ancillary gestures that express metaphors. However, musical expression is hindered by the unintentional triggering of musical events. From these findings, a series of heuristics are presented that can be applied in the future development of NIMEs.