The title of the thesis is ” The Gesture?s Narrative – contemporary music for percussion.”
I?m researching on the gesture?s influence on the musical perception of contemporary percussion performance.
For the moment, the first chapter is the one I?m requesting your help.
Is structured like this:
1 The Narrative – “Without action there is no tragedy” (Aristotle in Poetics)
1.2 Structuralism, Semiotics and Narratology
1.3 Narrative applied to Music
1.4 Narrative applied to Gesture
In n? 1 I intend to have an explanation of what is intended to be a Narrative in a general way. It?s very important the Aristotelian quote on the under tiltle, from his book “Poetics” because it says in a way that every action contains it self a narrative speech. (That?s very important for my thesis to show this about the gesture?s of the music al perfrmance. )
1.2 This should be a review on this three chains on the narrative studies.
1.3 Relate all of this to music
1.4 relate all of this to gesture, in which is possible to include choreographic motions, etc.
I don?t know if I should provide some references or not. Please let me know. If something is not clear (English is not my first language) please don?t hesitate in getting back to me.
For me would be very important to have a list off all references used.
For you to better understand my research theme I paste here a part of a document:
Musical Gestures Analysis and Perception
This project aims to objectively analyze the gesture of percussion performance and deeply understand its narrative content. Previous research has shown a strong correlation between gesture, sound and musical perception. Investigators concluded that the visual component in musical performance has influence in the communication and relationship between the performers and their audience (Vines, Krumhansl, Wanderley, & Levitan, 2008), interfering on musicality and expressivity (Davidson, 1993; Davidson 1995). In a communicative character and sensorial contents wise, the music becomes before gesture a complete narrative. When we listen and ?see? music, we tend to conceptualize it in terms of narrative. This narrative acts as a meta- metaphor within which everything can be understandable (Salgado Correia, 2005). These nonverbal narratives of temporal nature are the product of the continuous mimetic reaction to gestures and actions of the performers (Cox, 2001).
However, despite the consensus around this influence, it is not unanimous how to measure its impact on the musical speech of the composition itself (Schutz, Kubovy, 2006). Therefore we aim to find the meaning of the performance gestures, their narrative content and the interference on musical perception. A contemporary percussion program set including 6 works of renowned composers will be the musical basis for the study. Jo?o Pedro Oliveira, Pedro Junqueira Maia, Lu?s Antunes Pena are the chosen authors. The choice of the repertoire focus on the contrasting characteristics of the composition methods and technique used by the referred authors, so the gestures produced by the performer during interpretation shall provide us a wide palette of characteristics and consequently measurement parameters. For this research in gesture performance is a promising approach because it allows us to analyze data from many different dimensions, categorize it, and summarize the relationships identified, thus allowing to discover hidden speech narratives on music semantics perception.
Therefore, the methodology departs from recent technological developments methods of measuring, that allow an accurate analysis of gesture, by applying a multimodal approach for capturing the percussionist performance based on the extraction of features? sets specifically targeted to each performance movement. This technique combines acquisition of:
Body Posture descriptors captured by Biomechanical data extracted by sensor technology applied to muscular sections of the performer (percussionist musical gesture analysis).
1.1 – Biomedical data 1.1.1. Posture analysis
1.1.2. Contact forces measurement stroke-instrument
Body motion descriptors by software analysis of digital recording of Motion Capture (MOCAP).
Features recorded/extracted by software analysis of audience perception analysis
3.1.Measurement of applause
3.1.3. Quick to respond.
4.Survey on ?blind listening? vs. ?visual listening?.
5.Heart beat measurement (pulse), analysis of cardiac performance.
6. Motion analysis (network cameras)
7. Musical Performance features obtained by user surveys based on percussion performances.
According to the obtained results the aim is to determine narrative content of performance gesture of the percussionist and possible classification of aesthetic and musicological criteria. The outcome of the project will provide:
1- An understanding of the percussive gesture?s narrative content and how it influences musical perception. 2- An openly available database of multimodal datasets from the performance of percussionists, which complies with standards of indexation and storage formats widely used by the International Scientific Community. 3- A proof of concept application available to demonstrate the potential for automatic determination of the visual component influence in musical perception. 4- A new framework for potential application in areas such as composition, musical performance, multimedia creation, visual arts. In order to conduct this research following a multi-disciplinary approach concerning issues of musicology, technology, computer science, biomechanics behavior, cognitive psychology and performance practice. 5- A multidisciplinary approach and institutional cooperation with Porto Biomechanics Laboratory (LABIOMEP), Porto University (UP), Porto, Portugal in order to reduce costs through the use of equipment which already exist in this Laboratory and would be extremely useful for this project.
For many centuries the contact with music occurred through live performances. From the 20th century on, all the listening experience accumulated during the musical evolution ́s process was transformed by the appearance of recording and playback devices, that allowed us to be in contact with music at any time without the need of physical presence of the performer. This new ways of receiving the musical art became generally social accepted and more than that, the most common way of listening music nowadays. In the past decades investigators were interested in understanding how the musical perception was changing and mainly in understanding how we perceive music before live performances or recorded music. Investigators concluded that the visual component in musical performance has strong influence in the communication and relationship between the performers and their audience (Vines, Krumhansl, Wanderley, & Levitan, 2008), interfering on perception of musicality and expressivity (Davidson, 1993; Davidson 1995). ?Much previous research has shown gestures to be an important aspect of music perception allowing skilled performers to strategically manipulate audience experience. This suggests audiences should pay careful attention to gestures, and that musical situations which ignore visual information (e.g. CDs, radio broadcasts, blind auditions) rob the listener of an important dimension of musical communication. ( Schutz, Kubovy, 2006). Bradley W. Vines, Carol L. Krumhansl, Marcelo M. Wanderley and Daniel J. Levitin (2006) concluded that: ?The auditory and visual channels were found to convey similar experiences of phrasing but different experiences of tension through much of the performances. We found that visual information served both to augment and to reduce the experience of tension at different points in the musical piece (as revealed by functional linear modeling and functional significance testing). In addition, the musicians’ movements served to extend the sense of phrasing, to cue the beginning of new phrases, to indicate musical interpretation, and to anticipate changes in emotional content. Evidence for an interaction effect suggests that there may exist an emergent quality when musical performances are both seen and heard? In “The Sight of Sound: Music, Representation, and the History of the Body”, Richard Leppert refers the importance of the gestural images that are produced during a musical performance:”Precisely because musical sound is abstract, intangible, and ethereal […] the visual experience of its production is crucial to both musicians and audience alike for locating and communicating the place of music and musical sound within society and culture. […] Music, despite its phenomenological sonoric ethereality, is an embodied practice, like dance and theater.”(Leppert 1993: xx-xxi) This previous authors, suggest that gesture in music performs a fundamental role in the generation of meaning (Henrotte 1992; Lidov 1987). In a certain way, we have learned to understand musical sounds with the aid of the gestures that produce and represent these sounds (Lazzetta, 2000). As G. Kurtenbach and E. Hulteen have noted, the function of gesture in music is proportional to its power to express something: “Gestures increase function by virtue of their expressiveness. That is, a gesture may control multiple parameters at the same time, thus allowing a user to manipulate data in a manner not possible by modifying each parameter individually. For example, a conductor simultaneously controls both tempo and volume of the music gesture. The rhythm of the gesture controls tempo and the size of the gesture controls volume. This allows an efficient communication not possible by adjusting the tempo and volume independently .”(Kurtenbach & Hulteen 1990: 311-12)
However, despite the consensus around the influence of the gesture in musical perception, it is not unanimous how to measure its impact on the musical speech of the composition itself. For that acquisition we need to achieve a point of knowledge capable to answer to the questions: What means the gesture(s) from a semantic perspective?
How does the gesture semantics overlaps the music semantic?
In order to address these challenges, the project will start by collecting extensive multimodal datasets of precise measurements and recordings from performance gestures of a skilled percussionist , while interpreting 6 major contemporary pieces. This multimodal approach, and the variety of the chosen repertoire, goes beyond traditional case studies, since it allows the correlation of data acquired from different sources with great potential for new insights on this specific study.
We intend to analyze every bodily movement capable of conveying meaning as well as actions of touching, grasping or manipulating physical objects in order to control music or sound parameters. ?Each instrument offers a different level of control and interaction. For example, the mechanism of a pipe organ works almost automatically in a way that the performer’s gestures have very little influence over the process of sound generation and consequently on the capability of interfere with audience perception . On another hand, some acoustic instrument families, such as percussion, generally will allow the control of very subtle sound characteristics through the interaction between the player’s gestures and the structure of the instrument?(Lazzeta, 2000). “With such instruments the micro gestural movements of the performer?s body are translated into sound in ways that allow the performer to evoke a wide range of affective quality in the musical sound. That is simultaneously what makes such devices good musical instruments, what make them extremely difficult to play well, and what makes overcoming that difficulty well worthwhile to both performer and listener.” (Moore 1987: 258)
CHALLENGES ADDRESSED BY THE PROJECT As discussed in the Literature Review, several studies has shown a strong correlation between gesture, sound and music speech. This findings indicates that the visual component of a musical performance has influence on the relation between musician and his audience. This suggests the existence of a inner narrative content in gestural performance capable of interfere on the apprehension of the musical narrative it self. The perception of music before the performance action is not consonant when compared to a pure listening situation, or ?blind listening? experience. (Schutz, Kubovy, 2006) In fact, a musical performance is more than a literal reproduction of a musical score. (Repp, 1992). What makes a piece of music come alive (and what makes some performers and playing styles unique) is the art of music interpretation, in which are include the visual informations of the musical gestures. Therefore, the notated music score is but a small part of the actual music performing process. Not every intended nuance can be captured in a limited formalism such as common music notation. The performing artist is the determinant part of the musical happening system, so are his actions. Musicological research has gradually started to focus on (empirical) aspects of expressive performance in both visual and auditive components, since in the past the vast majority of music research dealt with the analysis of musical works, their structure, and its own formal theories. As a result, several aspects related to music perception are still waiting to be studied, such as: – the visual gesture meaning and contents; – measurement of several variables of expressive performance (dynamics, rythm, tension, phrasing); – the identification of the principles that govern the perception of the audience of expressive performances.
In order to address these challenges, the project will start by collecting extensive multimodal datasets of precise measurements and recordings from performance gestures of a skilled percussionist, while interpreting 6 major contemporary pieces. This multimodal approach, and the variety of the chosen repertoire, goes beyond traditional case studies, since it allows the correlation of data acquired from different sources with great potential for new insights on this specific study. This will require a setup of sophisticated audio and Video devices as well as multiple sensors such wireless and Piezotronics accelerometers, Bluetooth biomedical sensors, Impact force calculator. This devices will allow to read the different modalities involved in a live percussion performance, and the subsequent storage of the captured information into databases. In order to accurately acquire the performance data according to the project specifications, our Research Center at Catholic University already has all most of the required equipment, such as: – Music studio with a high Resolution Audio Recording. – TV Studio with high resolution Video recording Equipment. – Vicon System (MOCAP)
A multidisciplinary approach and institutional cooperation with Porto Biomechanics Laboratory (LABIOMEP), Porto University (UP), Porto, Portugal in order to reduce costs through the use of equipment which already exist in this Laboratory will be extremely useful for this project. The resulting databases will then be available to the international research community in this field (in compliance with widely used data formats, e.g. PCM audio, MIDI, MPEG Video, GDIF).
With such an acquisition setup ready, it will then be necessary to prepare live concert sessions, where a skilled percussionist will be invited to perform before an assessment panel of expert audiences. This audience will be tested through the measurement of applause in duration, volume, readiness of reaction. Analysis on the motion and facial expression (network cameras), real time heart beat rates control and inquires/tests over the expressive musical components on ?blind listening? vs. ?visual listening?, will take place, in order to collect the music/gesture perception data.
Upon the completion and recording of this data acquisition stage, the objective is to detect patterns and regularities in what regards sound and gestures, and how they are perceived by human observers. This analysis of the recorded data will be carried out with the help of machine learning and data mining methods (Widmer, 2004; Goebl, 2005). In fact, the use of Artificial Intelligence methods (AI – a field of study which includes pattern recognition, machine learning and data mining), namely inductive machine learning, has increasingly gained interest in recent years (Widmer, 2004). Therefore, the objective is to apply AI methods to perform research in empirical musicology. More precisely, the project aims at developing computational methods to study the musical perception, and to
inductively build formal models for particular aspects of musical gesture. This will allow a definition of gesture narrative.
When compared to previous works in the area of empirical musicology and expressive performance analysis (Widmer, 2004; Goebl, 2005), the proposed approach for this project is centered around the analysis of gesture and its inner narrative content interference on the perception of music semantics. The use large amounts of “real-world” and multimodal performance data as a basis for investigation, means that the resulting hypotheses and models will have a strong empirical and multimodal foundation. Dealing with the complexity of such large amounts of data, will require the implementation of intelligent data analysis methods, provided by the field of machine learning and data mining. Such methods will enable to discover interesting and possibly novel patterns and regularities in the data as well as to specify models of gestural performance in a formal way that makes possible extensive and precise testing. It will also be interesting to correlate the human assessment results with the obtained computational models, and try to map the discovered patterns in the feature space to the perception of expressive features.
Focusessays.com has been offering academic support services to students since 2002 and more than 60% of our customers are return clients. We have skilled and experienced writers in all academic levels and subjects. Entrust us with your assignment and you will get a custom essay which is 100% original within its deadline. Get value for your money, confidentiality is guaranteed and customer support services/communication with your writer are available 24/7. Place your Order Now.