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Books on the topic 'Ensemblier'

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Published: 3 May 2025

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1

Marcilhac, Félix. André Groult (1884-1966): Décorateur-ensemblier du XXe siècle. Paris: Les Éditions de l'Amateur, 1997.

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2

Glass, Philip. Songs from liquid days. New York, N.Y: CBS, 1986.

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3

McDonald, Donna. The odyssey of the Philip Jones Brass Ensemble. Bulle, Switzerland: Editions Bim, 1986.

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4

Ma, Yo-Yo. A playlist without borders. New York, NY: Masterworks, 2013.

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5

Vierdanck, Johann. Geistliche Konzerte: Zweiter Teil. Kassel: Bärenreiter, 1990.

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6

Vierdanck, Johann. Geistliche Konzerte: Erster Teil. Kassel: Bärenreiter, 1988.

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7

Radomski, Mikołaj. Dzieła wszystkie: Complete works. Warsaw: Dux, 2004.

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8

Górecki, Henryk Mikołaj. The essential Górecki: Orchestral & choral music. London: Olympia, 1993.

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9

Bockstal, Koen van. Radicale ensembles: Radical ensembles. Brussel: Academic & Scientific Publishers, 2018.

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10

Françoise, Siriex, ed. Leleu: Décorateurs ensembliers. Saint-Rémy-en-l'Eau: Monelle Hayot, 2007.

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11

Cosier, Tony. Ensemble. St. Catharines, Ont: Moonstone Press, 1985.

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12

Comeau, Raymond F. Ensemble. 6th ed. Fort Worth: Holt, Rinehart and Winston, 1999.

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13

Comeau, Raymond F. Ensemble. 6th ed. Fort Worth: Holt, Rinehart and Winston, 1999.

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14

Comeau, Raymond F. Ensemble. 6th ed. Fort Worth: Holt, Rinehart and Winston, 1998.

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15

Comeau, Raymond F. Ensemble. 5th ed. Fort Worth: Holt, Rinehart and Winston, 1994.

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16

Comeau, Raymond F. Ensemble. 5th ed. Fort Worth: Holt, Rinehart and Winston, 1994.

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17

Comeau, Raymond F. Ensemble. 5th ed. Fort Worth: Holt, Rinehart and Winston, 1994.

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18

Carduelis, Susan. Improve your consort skills. Littleton, CO: ARS Education Committee, 1990.

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19

Aggarwal, Charu C., and Saket Sathe. Outlier Ensembles. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7.

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20

Benoist, Emilie. Ensembles-en. Paris: Manuella, 2012.

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21

Ne Jamais Sous-Estimer un Ensemblier Qui a SurvÉcu À la PandÉmie De 2020: Carnet de Notes Cadeau Pour un Ensemblier. Independently Published, 2021.

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22

Dominique: Décorateur-ensemblier du XXe siècle : André Domin, 1883-1962, Marcel Genevrière, 1885-1967. Paris: L'Amateur, 2008.

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23

Fennell, Frederick. The wind ensemble. Arkadelphia, Ark. : Delta Publications, 1988.

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24

Morozov, Alexei. Non-Hermitian ensembles. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.18.

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This article discusses the three-fold family of Ginibre random matrix ensembles (complex, real, and quaternion real) and their elliptic deformations. It also considers eigenvalue correlations that are exactly reduced to two-point kernels in the strongly and weakly non-Hermitian limits of large matrix size. Ginibre introduced the complex, real, and quaternion real random matrix ensembles as a mathematical extension of Hermitian random matrix theory. Statistics of complex eigenvalues are now used in modelling a wide range of physical phenomena. After providing an overview of the complex Ginibre ensemble, the article describes random contractions and the complex elliptic ensemble. It then examines real and quaternion-real Ginibre ensembles, along with real and quaternion-real elliptic ensembles. In particular, it analyses the kernel in the elliptic case as well as the limits of strong and weak non-Hermiticity.
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25

Farberman, Harold. Percussion Ensemble Collection: 4 Ensembles for 6 Players (Percussion Performance). Alfred Publishing Company, 1993.

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26

Percussion Ensemble Collection: 4 Ensembles for 6 Players (Percussion Performance). Alfred Publishing Company, 1993.

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27

Majumdar, Satya N. Random growth models. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.38.

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This article discusses the connection between a particular class of growth processes and random matrices. It first provides an overview of growth model, focusing on the TASEP (totally asymmetric simple exclusion process) with parallel updating, before explaining how random matrices appear. It then describes multi-matrix models and line ensembles, noting that for curved initial data the spatial statistics for large time t is identical to the family of largest eigenvalues in a Gaussian Unitary Ensemble (GUE multi-matrix model. It also considers the link between the line ensemble and Brownian motion, and whether this persists on Gaussian Orthogonal Ensemble (GOE) matrices by comparing the line ensembles at fixed position for the flat polynuclear growth model (PNG) and at fixed time for GOE Brownian motions. Finally, it examines (directed) last passage percolation and random tiling in relation to growth models.
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28

Brezin, Edouard, and Sinobu Hikami. Beta ensembles. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.20.

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This article deals with beta ensembles. Classical random matrix ensembles contain a parameter β, taking on the values 1, 2, and 4. This parameter, which relates to the underlying symmetry, appears as a repulsion sβ between neighbouring eigenvalues for small s. β may be regarded as a continuous positive parameter on the basis of different viewpoints of the eigenvalue probability density function for the classical random matrix ensembles - as the Boltzmann factor for a log-gas or the squared ground state wave function of a quantum many-body system. The article first considers log-gas systems before discussing the Fokker-Planck equation and the Calogero-Sutherland system. It then describes the random matrix realization of the β-generalization of the circular ensemble and concludes with an analysis of stochastic differential equations resulting from the case of the bulk scaling limit of the β-generalization of the Gaussian ensemble.
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29

Ferrari, Patrik L., and Herbert Spohn. Random matrices and Laplacian growth. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.39.

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This article reviews the theory of random matrices with eigenvalues distributed in the complex plane and more general ‘beta ensembles’ (logarithmic gases in 2D). It first considers two ensembles of random matrices with complex eigenvalues: ensemble C of general complex matrices and ensemble N of normal matrices. In particular, it describes the Dyson gas picture for ensembles of matrices with general complex eigenvalues distributed on the plane. It then presents some general exact relations for correlation functions valid for any values of N and β before analysing the distribution and correlations of the eigenvalues in the large N limit. Using the technique of boundary value problems in two dimensions and elements of the potential theory, the article demonstrates that the finite-time blow-up (a cusp–like singularity) of the Laplacian growth with zero surface tension is a critical point of the normal and complex matrix models.
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30

Clendinning, Elizabeth A. American Gamelan and the Ethnomusicological Imagination. University of Illinois Press, 2020. http://dx.doi.org/10.5622/illinois/9780252043383.001.0001.

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The book seeks to answer these questions: Why are there more than 150 gamelans (Indonesian percussion ensembles) in North America, and why are more than half of them associated with American colleges and universities? How and why did gamelan ensembles spark the ethnomusicological imagination? What impact have these ensembles had on college music programs, their local communities, and transnational Indonesian performing arts scenes? How does a lifetime of teaching foreign college students shape the lives of non-American music teachers? First providing an overview of gamelan and its incorporation in education in North America, this book uses the story of the career and community of one performer-teacher, I Made Lasmawan of Bali and Colorado, as a case study to examine the formation and sustenance academic world music ensembles. It examines the way students develop musical and cultural competence by learning gamelan in traditional ethnomusicology ensemble courses and analyzes the merits of including gamelan ensembles in studies in percussion, composition, and music education. More broadly, the book argues that beyond the classroom, the presence of these ensembles shapes transnational arts education and touristic performing arts scenes in Bali. Finally, it advocates for world music ensemble courses as a powerful means for teaching musical and cultural diversity and sparking transnational exchanges, both in and outside the classroom.
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31

Farberman, Harold. Percussion Ensemble Collection: 4 Ensembles for 6 Players (Percussion Performance Series). Alfred Publishing Company, 1993.

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32

Cottrell, Stephen. The creative work of large ensembles. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199346677.003.0013.

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Preparing large ensembles for performance involves musical, social, logistical and financial challenges of a kind seldom encountered in other forms of collective music-making. The conventional approach to meeting the challenges that arise during rehearsal is to appoint a single musical overseer, usually a conductor, whose ostensible role in musical preparation is to directly influence the musicians while working towards the creation of a musical product to be delivered in later performances. Rehearsal leadership, viewed from this perspective, moves predominantly in one direction, from conductor to ensemble. But such a perspective oversimplifies the conductor’s relationship with the ensemble, the relationships between the musicians, and the strategies that the latter must employ when working in large ensembles. Conceptualizing the ensemble as a complex system of interrelated components, where leadership and creative agency are distributed and developed through rehearsal to achieve what audiences assume to be a unified whole, yields new understanding of the work of large ensembles. This chapter examines these components of the creative process in orchestral and choral rehearsal and performance, the internal and external forces shaping and constraining that process, and the approaches that individual musicians and conductors could adopt in response to the changing contexts in which such creativity might be manifested.
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33

Anderson, Greg W. Spectral statistics of orthogonal and symplectic ensembles. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.5.

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This article describes a direct approach for computing scalar and matrix kernels, respectively for the unitary ensembles on the one hand and the orthogonal and symplectic ensembles on the other hand, leading to correlation functions and gap probabilities. In the classical orthogonal polynomials (Hermite, Laguerre, and Jacobi), the matrix kernels for the orthogonal and symplectic ensemble are expressed in terms of the scalar kernel for the unitary case, using the relation between the classical orthogonal polynomials going with the unitary ensembles and the skew-orthogonal polynomials going with the orthogonal and symplectic ensembles. The article states the fundamental theorem relating the orthonormal and skew-orthonormal polynomials that enter into the Christoffel-Darboux kernels
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34

Hoover, Theresa. Empowering Ensembles with Technology: The Ensemble Director's Guide to Teaching with Tech. F-flat Books, 2022.

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35

Butterworth, Helen. Gems for Flute Ensembles: Easy Ensemble Pieces with Piano, Book and CD. Alfred Publishing Company, Incorporated, 2014.

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36

Butterworth, Helen. Gems for Cello Ensembles: Easy Ensemble Pieces with Piano, Book and CD. Alfred Publishing Company, Incorporated, 2013.

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37

Fleck, Bela. Bela Fleck and the Flecktones. Leonard Corporation, Hal, 1998.

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38

Fleck, Bela. Bela Fleck and the Flecktones. Leonard Corporation, Hal, 1998.

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39

J, Cipolla Frank, and Hunsberger Donald, eds. The wind ensemble and its repertoire: Essays on the fortieth anniversary of the Eastman Wind Ensemble. University of Rochester Press, 1994.

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40

(Editor), Frank Cipolla, Eastman Wind Ensemble (Corporate Author), and Donald Hunsberger (Editor), eds. The Wind Ensemble and Its Repertoire: Essays on the Fortieth Anniversary of the Eastman Wind Ensemble. University of Rochester Press, 1994.

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41

Fleck, Bela. Bela Fleck and the Flecktones. Hal Leonard Corporation, 1998.

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42

Cipolla, Frank, and Donald Hunsberger. Wind Ensemble and Its Repertoire: Essays on the Fortieth Anniversary of the Eastman Wind Ensemble. University of Rochester Press, 1997.

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43

Fleck, Bela. Bela Fleck and the Flecktones. Leonard Corporation, Hal, 1998.

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44

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Ensembles with hard constraints. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0005.

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This chapter introduces random graph ensembles involving hard constraints such as setting a fixed total number of links or fixed degree sequence, including properties of the partition function. It continues on from the previous chapter’s investigation of ensembles with soft-constrained numbers of two-stars (two-step paths) and soft-constrained total number of triangles, but now combined with a hard constraint on the total number of links. This illustrates phase transitions in a mixed-constrained ensemble – which in this case is shown to be a condensation transition, where the network becomes clumped. This is investigated in detail using techniques from statistical mechanics and also looking at the averaged eigenvalue spectrum of the ensemble. These phase transition phenomena have important implications for the design of graph generation algorithms. Although hard constraints can (by force) impose required values of observables, difficult-to-reconcile constraints can lead to graphs being generated with unexpected and unphysical overall topologies.
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45

Borodin, Alexei. Random matrix representations of critical statistics. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.12.

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This article examines two random matrix ensembles that are useful for describing critical spectral statistics in systems with multifractal eigenfunction statistics: the Gaussian non-invariant ensemble and the invariant random matrix ensemble. It first provides an overview of non-invariant Gaussian random matrix theory (RMT) with multifractal eigenvectors and invariant random matrix theory (RMT) with log-square confinement before discussing self-unfolding and not self-unfolding in invariant RMT. It then considers a non-trivial unfolding and how it changes the form of the spectral correlations, along with the appearance of a ghost correlation dip in RMT and Hawking radiation. It also describes the correspondence between invariant and non-invariant ensembles and concludes by introducing a simple field theory in 1+1 dimensions which reproduces level statistics of both of the two random matrix models and the classical Wigner-Dyson spectral statistics in the framework of the unified formalism of Luttinger liquid.
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46

Bowman, Peter. Fun and Games with the Recorder - Ensemble Collection: 36 Pieces for Various Ensembles. SCHOTT MUSIK INT'L MAINZ, 2007.

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47

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Markov Chain Monte Carlo sampling of graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0006.

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This chapter looks at Markov Chain Monte Carlo techniques to generate hard- and soft-constrained exponential random graph ensembles. The essence is to define a Markov chain based on ergodic randomization moves acting on a network with transition probabilities which satisfy detailed balance. This is sufficient to ensure that the Markov chain will sample from the ensemble with the desired probabilities. This chapter studies several commonly seen randomization move sets and carefully defines acceptance probabilities for a range of different ensembles using both the Metropolis–Hastings and the Glauber prescription. Particular care is paid to describe and avoid the pitfalls that can occur in defining randomization moves for hard-constrained ensembles, and applying them without introducing inadvertent bias (i.e. defining and comparing protocols including switch-and-hold and mobility).
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48

Keating, Jon, and Nina Snaith. Random permutations and related topics. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.25.

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This article considers some topics in random permutations and random partitions highlighting analogies with random matrix theory (RMT). An ensemble of random permutations is determined by a probability distribution on Sn, the set of permutations of [n] := {1, 2, . . . , n}. In many ways, the symmetric group Sn is linked to classical matrix groups. Ensembles of random permutations should be given the same treatment as random matrix ensembles, such as the ensembles of classical compact groups and symmetric spaces of compact type with normalized invariant measure. The article first describes the Ewens measures, virtual permutations, and the Poisson-Dirichlet distributions before discussing results related to the Plancherel measure on the set of equivalence classes of irreducible representations of Sn and its consecutive generalizations: the z-measures and the Schur measures.
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49

Bohigas, Oriol, and Hans Weidenmuller. History – an overview. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.2.

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This article discusses the first four decades of the history of random matrix theory (RMT), that is, until about 1990. It first considers Niels Bohr's formulation of the concept of the compound nucleus, which is at the root of the use of random matrices in physics, before analysing the development of the theory of spectral fluctuations. In particular, it examines the Wishart ensemble; Dyson's classification leading to the three canonical ensembles — Gaussian Orthogonal Ensemble (GOE), Gaussian Unitary Ensemble (GUE), and Gaussian Symplectic Ensemble (GSE); and the breaking of a symmetry or an invariance. It also describes how random matrix models emerged from quantum physics, more specifically from a statistical approach to the strongly interacting many-body system of the atomic nucleus. The article concludes with an overview of data on nuclear resonances, many-body theory, chaos, number theory, scattering theory, replica trick and supersymmetry, disordered solids, and interacting fermions and field theory.
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50

Shiota, Masahiro. Geometry of Subanalytic and Semialgebraic Sets. Birkhauser Verlag, 2012.

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