2,170 research outputs found
Henry Emery
No full textHenry Emery is Managing Director of Latitude Aviation English Services (www.latitude-aes.aero), a specialist provider of aviation language training and testing services. Henry is co-author of the British Council award-winning Aviation English (Macmillan, 2008) and Check Your Aviation English (Macmillan, 2010). He was the project manager of the English Test for Aviation, the first test in the world to be endorsed by ICAO, and was project manager for the development of the ICAO/ICAEA Rated Speech Samples Training Aid (second edition). He is a board member of the International Civil Aviation English Association.https://commons.erau.edu/icaea-workshop-images/1003/thumbnail.jp
Henry Emery
No full textHenry Emery is Managing Director of Latitude Aviation English Services (www.latitude-aes.aero), a specialist provider of aviation language training and testing services. Henry is co-author of the British Council award-winning Aviation English (Macmillan, 2008) and Check Your Aviation English (Macmillan, 2010). He was the project manager of the English Test for Aviation, the first test in the world to be endorsed by ICAO, and was project manager for the development of the ICAO/ICAEA Rated Speech Samples Training Aid (second edition). He is a board member of the International Civil Aviation English Association.https://commons.erau.edu/icaea-workshop-images/1045/thumbnail.jp
Letter dated 21 April 1908 from Emery W. Ellis to his classmates
No full textLetter of Emery W. Ellis to former classmates, reporting plans to fully reopen Lintsing Station; Purchase of old merchant home for Boys\u27 School; History of old merchant home; Emary W Ellis-author; Apr 21, 0
supp_mat_804038 – Supplemental material for The definition of a musician in music psychology: A literature review and the six-year rule
No full textSupplemental material, supp_mat_804038 for The definition of a musician in music psychology: A literature review and the six-year rule by J. Diana Zhang, Marco Susino, Gary E. McPherson and Emery Schubert in Psychology of Music</p
A spiral model of musical decision-making
No full textThis paper describes a model of how musicians make decisions about performing notated music. The model builds on psychological theories of decision-making and was developed from empirical studies of Western art music performance that aimed to identify intuitive and deliberate processes of decision-making, a distinction consistent with dual-process theories of cognition. The model proposes that the proportion of intuitive (Type 1) and deliberate (Type 2) decision-making processes changes with increasing expertise and conceptualises this change as movement along a continually narrowing upward spiral where the primary axis signifies principal decision-making type and the vertical axis marks level of expertise. The model is intended to have implications for the development of expertise as described in two main phases. The first is movement from a primarily intuitive approach in the early stages of learning towards greater deliberation as analytical techniques are applied during practice. The second phase occurs as deliberate decisions gradually become automatic (procedural), increasing the role of intuitive processes. As a performer examines more issues or reconsiders decisions, the spiral motion towards the deliberate side and back to the intuitive is repeated indefinitely. With increasing expertise, the spiral tightens to signify greater control over decision type selection. The model draws on existing theories, particularly Evans’ (2011) Intervention Model of dual-process theories, Cognitive Continuum Theory (Hammond et al., 1987; Hammond, 2007), and Baylor’s (2001) U-shaped model for the development of intuition by level of expertise. By theorising how musical decision-making operates over time and with increasing expertise, this model could be used as a framework for future research in music performance studies and performance science more generally
The Role of Individual Difference in Judging Expressiveness of Computer-Assisted Music Performances by Experts
No full textComputational systems for generating expressive musical performances have been studied for several decades now. These models are generally evaluated by comparing their predictions with actual performances, both from a performance parameter and a subjective point of view, often focusing on very specific aspects of the model. However, little is known about how listeners evaluate the generated performances and what factors influence their judgement and appreciation. In this article, we present two studies, conducted during two dedicated workshops, to start understanding how the audience judges entire performances employing different approaches to generating musical expression. In the preliminary study, 40 participants completed a questionnaire in response to five different computer-generated and computer-assisted performances, rating preference and describing the expressiveness of the performances. In the second, “GATM” (Gruppo di Analisi e Teoria Musicale) study, 23 participants also completed the Music Cognitive Style questionnaire. Results indicated that music systemizers tend to describe musical expression in terms of the formal aspects of the music, and music empathizers tend to report expressiveness in terms of emotions and characters. However, high systemizers did not differ from high empathizers in their mean preference score across the five pieces. We also concluded that listeners tend not to focus on the basic technical aspects of playing when judging computer-assisted and computer-generated performances. Implications for the significance of individual differences in judging musical expression are discussed
Toward a musical Turing test for automatic music performance
No full textThis paper reports a “musical Turing test”conducted at a live concert of algorithm-generated performances, where one group of participants were invited to rank the most human-like performance while knowing that one of the performances was by a human, and another group of participant were asked to do the same, but without knowing that there was a human performer on the program. The program consisted of five pieces from the classical/romantic period, played on a Disklavier. High quality music-expression algorithms were used to generate the algorithmic renditions. Regardless of the group, musical experience and a number of other factors, the subjects were unable to identify the human performer out of the five performances. The group that did not know there was a human performer had a wider range of votes compared to the group that did know. Furthermore, subjects were less confident of their answers when they knew that they were comparing human and computer-generated performances . On the contrary, if subjects believed they were only comparing computer-generated performances the task may have been less demanding. Findings suggest that computer algorithms are able to substitute for human performance, but the role of the physical presence of the performer (who was absent in this study) could be an area for further investigation
Performing solo Bach: A case study of musical decision-making
No full textThis study explores how an expert period instrument musician makes musical decisions, focusing on the distinction between intuitive (Type 1) and deliberate (Type 2) processes as defined by dual-process theories of cognition (Evans, 2008). A case study of the cellist Daniel Yeadon was conducted over 2 years, during which extensive quasi think-aloud and performance data were collected regarding Yeadon’s interpretation of the Suites for Solo Cello by J. S. Bach (BWV 1007–1012). Analysis of this data resulted in the categorization of 134 musical decisions as intuitive, procedural, deliberate, or deliberate HIP (historically informed performance). Procedural decisions were a subset of intuitive, defined as previously deliberate decisions that had become automatic over time. The category of deliberate HIP consisted of decisions that were explained with reference to specific knowledge of historical performance practices. A large proportion of deliberate decision-making was found (65% overall), with deliberate processes dictating the majority of decisions across all performance features except for tone color and ornamentation. Musical decisions discussed in the study demonstrate that performers often manipulate several features of the music simultaneously (making coding and analysis complicated), whether consciously or otherwise. The highest number of musical decisions related to articulation and phrasing, a result that highlights important components of current HIP style. Implications for dual-process theories include the novel category of procedural that demonstrates differences within intuitive (Type 1) processes
Editorial: Preservation and exploitation of audio recordings: from archives to industries
No full textAlgorithms can Mimic Human Piano Performance: The Deep Blues of Music
No full textCan a computer play a music score, e.g. via a Disklavier, in a way that cannot be distinguished from a human performance of the same music? One hundred and seventy-two participants with a wide range of music playing backgrounds rated sound recordings of 7 performances of piano music by Kuhlau, one played by a human, and six generated by algorithms, including a ‘mechanical’ and an ‘unmusical’ rendering. Participants rated the extent to which each performance was by a human and explained their answers. The mechanical performance had the lowest mean rating, but the human performance was rated as statistically identical to the other stimuli. There were no differences between ratings made by classical piano experts and lay listeners, but despite this, the musicians were more confident with their ratings. Qualitative analysis revealed five broad themes that contribute to judging whether a piece appears to be human. The themes were labelled (in descending order of frequency) intuitive, expressive, imperfections, halo (global preference) and empathy. This paper presents new evidence systematically demonstrating that algorithm generated performances of piano music can be indistinguishable from human performances, suggesting some parallels with the 1990s victory of the Deep Blue computer of the world champion (human) chess player
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