Labanotation Scores Research Articles

LabanFormer: Multi-scale graph attention network and transformer with gated recurrent positional encoding for labanotation generation

Labanotation is a widely-used notation system for recording human dance movements. Automatically generating Labanotation scores from motion capture data can save significant manual effort and help the preservation of old folk dances in protecting intangible cultural heritages. Existing Labanotation generation methods have limited ability to capture the flexible limb movements as well as the rich periodic, symmetric, or repeated dance steps. In this paper, we present a novel LabanFormer model including a Multi-Scale Graph Attention network (MS-GAT) and a transformer model with Gated Recurrent Positional Encoding (GRPE) to achieve more effective Labanotation generation. First, the proposed MS-GAT can capture flexible limb movements by learning feature correlations between every two joints and aggregating features of neighboring joints over multiple scales. Second, we propose a new GRPE-based transformer to learn global temporal dependencies in the output feature sequences of MS-GAT. The novel GRPE module can encode position information with learnable parameters while handling various sequence lengths. As such, the periodic, symmetric, or repeated steps in dances can be accurately captured. Finally, the corresponding Laban symbols are generated by the decoder of the GRPE-based transformer. Extensive experiments on two real-world datasets show that the proposed LabanFormer model obtains remarkable performance compared with state-of-the-art approaches on the automatic Labanotation generation task.

Dance Movement Learning for Labanotation Generation Based on Motion-Captured Data

Labanotation is a widely used notation system for recording body movements, especially dances. It has wide applications in choreography preservation, dance archiving, and so on. However, the manual creation of Labanotation scores is rather difficult and time-consuming. Therefore, research on the generation of Labanotation scores is of great interest. In this paper, we aim to generate Labanotation scores based on the motion-captured data obtained from real-world dance performances. First, to deal with challenges such as various dance movement patterns, different dancer shapes, and noises in the motion-captured data, we propose a novel feature that is invariant to anthropometric variation and body orientation. Then, we generate the notations of both lower-limb movements and upper-limb gestures. On the one hand, we utilize the hidden Markov model (HMM) to analyze the temporal dynamic characteristics of limb movements and map each lower limb movement to a corresponding dance notation. On the other hand, for upper limbs, we train a multi-class classifier based on the extremely randomized trees (Extra-Trees) to identify the notations for arm gestures. Finally, we generate the Labanotation symbols based on the above movement analysis and thus create Labanotation scores. The proposed methods can generate the spatial symbols describing directions and levels in both the support column and arm column based on motion-captured data. The generated scores are clear and reliable. Experimental results show an average recognizing accuracy of over 92% for the generated notations, which is significantly better than previous work.

Computer-aided dance analysis in practice — with Labanatory

Hungarian folk dance has strict structure, which plays an important role in its authenticity. Besides the connections of dance elements, it is important to consider quantitative features of a dance such as the frequency of dance elements and the frequency of connections. The number of occurrences of a dance element or several consecutive dance elements in a dance performance can be counted with a computer tool if the dance is notated and its notation is digitized. Labanatory has been developed in Hungary as a tool that is capable of searching for repeating patterns in Labanotation scores, that is to identify recurring movements. The paper shows the software in use: how the researcher can use this kind of tool in practice. It shows actual examples of detecting recurring notated movements with several kinds of searches (simple, compound, wildcard, symmetric, and augmented searches). It points out that searches have to be used in an iterative way during the analysis when creating ‘good’ queries from Labanotation symbols. Notation parts can be labeled in the program in any phase of the analysis and the researcher can revise the label markers offered as a result of a search. From the labeled dance parts, the user can make basic statistics on the analyzed dance performances. The examination of original dance texts is very important in dance education. It is beneficial if a student can access fieldwork-texts directly, not only through the teacher’s filter. The student can focus on dance elements in accordance with his own interest by creating his own queries and performing the related computer searches. He can detect dance elements of any length and examine any consecutive combinations of them. Evaluating search results can allow the student to interpret dance structures in new ways.

GenLaban: A tool for generating Labanotation from motion capture data

This paper presents a computer-aided tool for automatically generating Labanotation scores from motion capture data named GenLaban. GenLaban can be implemented with a low-cost equipment but an efficient method that allows users converting body motions to scores. The key components of GenLaban are the analysis of body motions, the quantization of body postures and the determination of body parts carrying the body weight. All the processes are under supervision of a Labanotation expert to ensure the notation meaning correctly as the use for the dance composition. The experiments showed that for dancers, dance instructors and choreographers, GenLaban is a potential tool for notating dance movements into Labanotation scores enabling them to be accurately interpreted. At present the system can handle a subset of Labanotation covering many of the fundamental movements. However, Labanotation is rich in symbols and new symbols are continually being introduced and will be incorporated in the GenLaban tool as time permits.

From dance notation to human animation: The LabanDancer project

AbstractSymbolic systems such as Labanotation for notating dance and choreography provide a critical tool for the preservation of cultural heritage in what once was considered an ‘illiterate’ art form. While the goals of such notation systems are laudable, the unfortunate reality is that most dancers and choreographers cannot read or write the notation; that is, they are loath to take the considerable effort to learn a rich, but complex methodology. To make Labanotation scores more accessible the LabanDancer system has been developed to translate Labanotation scores recorded in the LabanWriter editor into 3‐d human figure animations. A major challenge in the development of this translator has been to find approaches that are general enough to create reasonable animations for a wide variety of different movements. Any translator must also take account of the context of a movement since this can affect the interpretation of the Labanotation scores. Copyright © 2005 John Wiley & Sons, Ltd.

Preface

These papers were originally given as a panel entitled What Constitutes a Dance at the 1989 Congress on Research in Dance Conference in Williamsburg, Virginia. Panelists selected Antony Tudor's 1937 Dark Elegies as a case study and basis for examining general questions regarding elements to be considered in identifying a dance work. Several issues and occurrences inspired panel members, such as recent interest in revivals of dance works from the beginning of this century and scholarly debate about issues related to directing dance from Labanotation scores. While Nelson Goodman's 1968 book Languages of Art served as a theoretical springboard for discussion, Judy Van Zile's 1985–86 article “What is the Dance? Implications for Dance Notation” proved a thought-provoking precedent for this investigation.

The Japanese Bon Dance in Hawaii by Judy Van Zile. Hawaii: Press Pacifica, 1982, vi-x + 93 pp., map, line drawings, photos, Labanotation scores, music transcriptions, notes, glossary, sources, $5.95 paperbound.

The Japanese Bon Dance in Hawaii by Judy Van Zile. Hawaii: Press Pacifica, 1982, vi-x + 93 pp., map, line drawings, photos, Labanotation scores, music transcriptions, notes, glossary, sources, $5.95 paperbound.

Issues in Labananalysis Research: Using the Humphrey Scores

The accessibility of notation scores and film provides a unique opportunity for examination of the relationship between existing music and dance in the work of Doris Humphrey. Nine of her pieces to existing musical scores have been notated in Labanotation and at least fourteen have been filmed in their entirety or in part. Nine are recorded in notation and on film. The Humphrey films and scores span her choreographic career and illustrate a wide range of musical scores from the eighteenth to the twentieth century. In this paper, I will suggest various ways in which the Labanotation scores have been useful for analysis and have contributed to my understanding of Humphrey's work.So far I have studied three pieces, each of which has been filmed and notated, Passacaglia in C minor (1938), Partita in G major (1942) the four movements that remain, and the first movement of Brandenburg Concerto No. 4 in G major (1959), created with Ruth Currier. All three employ scores and titles by Bach and all make oblique reference to the music visualization tradition from which Humphrey emerged.