DOCSLIB.ORG

UNIVERSITY of CALIFORNIA, SAN DIEGO Exploring Unexploited

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
UNIVERSITY of CALIFORNIA, SAN DIEGO Exploring Unexploited UNIVERSITY OF CALIFORNIA, SAN DIEGO Exploring Unexploited Compositional Space in Intercultural, Cross-level, and Concurrence Features of Music A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Music by Hsin-Ming Lin Committee in charge: Professor Shlomo Dubnov, Chair Professor Natacha D. Diels Professor Lei Liang Professor Miller S. Puckette Professor Akos Rona-Tas 2017 The dissertation of Hsin-Ming Lin is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Chair University of California, San Diego 2017 iii Table of Contents Signature Page ............................................................................................................... iii Table of Contents .......................................................................................................... iv List of Abbreviations ................................................................................................... viii List of Figures ................................................................................................................. x List of Tables ............................................................................................................... xiv Vita .............................................................................................................................. xvi Abstract of the Dissertation ....................................................................................... xviii 1. Introduction ................................................................................................................ 1 1.1 Preliminary ......................................................................................................... 2 1.1.1 Data Science .................................................................................................. 2 1.1.2 Computational Musicology ........................................................................... 6 1.2 Rationale ............................................................................................................. 8 1.2.1 Personal Background ..................................................................................... 8 1.2.2 Exploitation ................................................................................................. 10 1.2.3 Mutualism .................................................................................................... 12 1.3 Levels of Feature .............................................................................................. 16 1.3.1 Low .............................................................................................................. 17 1.3.2 Middle .......................................................................................................... 19 1.3.3 High ............................................................................................................. 20 1.3.4 Meta ............................................................................................................. 22 1.4 Levels of Representation .................................................................................. 22 iv 1.4.1 MIDI ............................................................................................................ 25 1.4.2 LilyPond ...................................................................................................... 26 1.4.3 Humdrum ..................................................................................................... 27 1.4.4 MusicXML .................................................................................................. 28 1.5 Melodic Contour ............................................................................................... 30 1.5.1 Audio ........................................................................................................... 30 1.5.2 Symbolic ...................................................................................................... 35 1.6 Datasets ............................................................................................................. 36 1.6.1 Audio ........................................................................................................... 36 1.6.2 Symbolic ...................................................................................................... 37 1.6.3 Hybrid .......................................................................................................... 40 2. Implementation ........................................................................................................ 43 2.1 Preparation ........................................................................................................ 44 2.1.1 Data Cleaning .............................................................................................. 44 2.1.2 Samples Quantity ......................................................................................... 44 2.1.3 Environment ................................................................................................ 45 2.2 Features ............................................................................................................. 46 2.2.1 Susceptibility ............................................................................................... 46 2.2.2 Indices of Tessitura and Mobility ................................................................ 51 2.2.3 jSymbolic Feature Set .................................................................................. 52 2.2.4 Concurrence and Non-concurrence ............................................................. 53 2.3 Intercultural and Cross-level ............................................................................. 57 v 3. Exploration ............................................................................................................... 61 3.1 Observation ....................................................................................................... 62 3.1.1 Dataset by Dataset ....................................................................................... 62 3.1.2 Sample by Sample ....................................................................................... 66 3.1.3 Adding Tessitura and Mobility .................................................................... 68 3.2 Perspectives ...................................................................................................... 71 3.2.1 External ........................................................................................................ 72 3.2.2 Internal ......................................................................................................... 76 3.3 Evaluation ......................................................................................................... 82 3.3.1 Balanced Dataset ......................................................................................... 82 3.3.2 Feature Sets ................................................................................................. 84 3.3.3 Classification ............................................................................................... 85 3.3.4 Discussion .................................................................................................... 91 3.4 Composition ...................................................................................................... 95 3.4.1 Fundamentals ............................................................................................... 95 3.4.2 Outliers ........................................................................................................ 98 3.4.3 Experiments ................................................................................................. 99 4. Conclusion ............................................................................................................. 104 4.1 Contribution .................................................................................................... 105 4.2 Application ..................................................................................................... 107 4.3 Limitation ....................................................................................................... 107 4.4 Expectation ..................................................................................................... 108 vi Appendix .................................................................................................................... 110 A1. Selected jSymbolic Features from [15] ........................................................... 110 Sixteen Pitch Features ........................................................................................ 110 Sixteen Melody Features .................................................................................... 110 A2. Susceptibilities of All Datasets ........................................................................ 111 Part One of Three (mean ≥ 6) ............................................................................. 111 Part Two of Three (5 ≤ mean < 6) ...................................................................... 112 Part Three of Three (mean < 5) .......................................................................... 113 A3. Selected Quartets from [53] for Chapter 3.3 ................................................... 114 Haydn’s String Quartets (80 movements) .........................................................
Load more

Recommended publications
  • The Yale-Classical Archives Corpus
    University of Massachusetts Amherst ScholarWorks@UMass Amherst Music & Dance Department Faculty Publication Music & Dance Series 2016 The ale-CY lassical Archives Corpus Christopher William White University of Massachusetts Amherst Ian Quinn Yale University Follow this and additional works at: https://scholarworks.umass.edu/music_faculty_pubs Part of the Music Commons Recommended Citation White, Christopher William and Quinn, Ian, "The ale-CY lassical Archives Corpus" (2016). Empirical Musicology Review. 17. http://dx.doi.org/10.18061/emr.v11i1.4958 This Article is brought to you for free and open access by the Music & Dance at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Music & Dance Department Faculty Publication Series by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. Empirical Musicology Review Vol. 11, No. 1, 2016 The Yale-Classical Archives Corpus CHRISTOPHER W M WHITE [1] The University of Massachusetts, Amherst IAN QUINN Yale University ABSTRACT: The Yale-Classical Archives Corpus (YCAC) contains harmonic and rhythmic information for a dataset of Western European Classical art music. This corpus is based on data from classicalarchives.com, a repository of thousands of user- generated MIDI representations of pieces from several periods of Western European music history. The YCAC makes available metadata for each MIDI file, as well as a list of pitch simultaneities (“salami slices”) in the MIDI file. Metadata include the piece’s
    [Show full text]
  • Shopping for Classical Music Online
    Shopping for Classical Music Online The primary focus of this article concerns finding online sources for purchase of so- called physical media—CDs, SACDs, Blu-Ray Audio discs—and not so much about digital downloads. For those readers interested in digital files and computer audio, I’ve included that as a separate discussion at the end. The ‘online’ part of this essay’s title is really an unnecessary add-on because when was the last time you’ve been in an actual store that sold classical CDs? Today everything is done online, so no need for the qualifier. The last time I was at Barnes and Nobles they only had 26 so-called classical selections which were mostly cheap compilations, crossover titles, and one CD by Bang Bang. I remember the days when I ran the classical section of a large record store and we had sixty titles in the bin just for Beethoven’s Ninth. But I’m not complaining since, in many ways, shopping is now easier both in terms of ordering from the comfort of your home at any time of the day or night, but also in being able sample recordings and find exactly what appeals to your own tastes. I’ve explored several online subscription streaming services, and I continue to buy CDs (and SACDs and DVDs) from a variety of sources which I’ll discuss here. The first question many ask when they see my wall-to-wall room with 6,000 CDs and separate storage room with 3,000 LPs is when am I going to get rid of all this museum stuff and go digital? Well, the answer is probably never.
    [Show full text]
  • Marketing Plan
    ALLIED ARTISTS MUSIC GROUP An Allied Artists Int'l Company MARKETING & PROMOTION MARKETING PLAN: ROCKY KRAMER "FIRESTORM" Global Release Germany & Rest of Europe Digital: 3/5/2019 / Street 3/5/2019 North America & Rest of World Digital: 3/19/2019 / Street 3/19/2019 MASTER PROJECT AND MARKETING STRATEGY 1. PROJECT GOAL(S): The main goal is to establish "Firestorm" as an international release and to likewise establish Rocky Kramer's reputation in the USA and throughout the World as a force to be reckoned with in multiple genres, e.g. Heavy Metal, Rock 'n' Roll, Progressive Rock & Neo-Classical Metal, in particular. Servicing and exposure to this product should be geared toward social media, all major radio stations, college radio, university campuses, American and International music cable networks, big box retailers, etc. A Germany based advance release strategy is being employed to establish the Rocky Kramer name and bona fides within the "metal" market, prior to full international release.1 2. OBJECTIVES: Allied Artists Music Group ("AAMG"), in association with Rocky Kramer, will collaborate in an innovative and versatile marketing campaign introducing Rocky and The Rocky Kramer Band (Rocky, Alejandro Mercado, Michael Dwyer & 1 Rocky will begin the European promotional campaign / tour on March 5, 2019 with public appearances, interviews & live performances in Germany, branching out to the rest of Europe, before returning to the U.S. to kick off the global release on March 19, 2019. ALLIED ARTISTS INTERNATIONAL, INC. ALLIED ARTISTS MUSIC GROUP 655 N. Central Ave 17th Floor Glendale California 91203 455 Park Ave 9th Floor New York New York 10022 L.A.
    [Show full text]
  • Federal Register/Vol. 84, No. 24/Tuesday, February 5, 2019/Proposed Rules
    Federal Register / Vol. 84, No. 24 / Tuesday, February 5, 2019 / Proposed Rules 1661 Insurance Corporation Improvement Act Dated at Washington, DC, on December 18, statutory deadlines set forth in the of 1991, 12 U.S.C. 1828(o), prescribes 2018. Music Modernization Act, no further standards for real estate lending to be By order of the Board of Directors. extensions of time will be granted in used by FDIC-supervised institutions in Federal Deposit Insurance Corporation. this rulemaking. adopting internal real estate lending Valerie Best, ADDRESSES: For reasons of government policies. For purposes of this subpart, Assistant Executive Secretary. efficiency, the Copyright Office is using the term ‘‘FDIC-supervised institution’’ [FR Doc. 2018–28084 Filed 2–4–19; 8:45 am] the regulations.gov system for the means any insured depository BILLING CODE 6714–01–P submission and posting of public institution for which the Federal comments in this proceeding. All Deposit Insurance Corporation is the comments are therefore to be submitted appropriate Federal banking agency LIBRARY OF CONGRESS electronically through regulations.gov. pursuant to section 3(q) of the Federal Specific instructions for submitting Deposit Insurance Act, 12 U.S.C. Copyright Office comments are available on the 1813(q). Copyright Office’s website at https:// ■ 3. Amend § 365.2 by revising 37 CFR Part 201 www.copyright.gov/rulemaking/ paragraphs (a), (b)(1)(iii), (2)(iii) and pre1972-soundrecordings- (iv), and (c) to read as follows: [Docket No. 2018–8] noncommercial/. If electronic submission of comments is not feasible Noncommercial Use of Pre-1972 Sound § 365.2 Real estate lending standards.
    [Show full text]
  • IFPI Digital Music Report 2013 Engine of a Digital World
    IFPI Digital Music Report 2013 Engine of a digital world 9 IN 10 MOST LIKED PEOPLE ON FACEBOOK ARE ARTISTS 9 IN 10 OF THE MOST WATCHED VIDEOS ON YOUTUBE ARE MUSIC 7 IN 10 MOST FOLLOWED TWITTER USERS ARE ARTISTS Deezer4artists-HD_acl.pdf 1 26/02/13 17:37 2 Contents Introduction 4-5 Music is an engine of the digital world 22-23 g Plácido Domingo, chairman, IFPI g Fuelling digital engagement g Frances Moore, chief executive, IFPI g Fuelling hardware adoption g Driving the live entertainment industry An industry on the road to recovery: g Attracting customers, driving profits Facts and figures 6-10 Going global: the promise of emerging markets 24-27 Global best sellers 11-13 g Brazil: A market set to surge g Top selling albums g Russia: Hurdles to growth can be overcome g Top selling singles g India: Nearing an all-time high g Strong local repertoire sales g Strong market potential in The Netherlands Digital music fuels innovation 14-17 Engaging with online intermediaries 28-30 g Download stores receive a boost from the cloud g Advertising: tackling a major source of funding for music piracy g Subscription services come of age g Search engines – a vital role to play g Subscription transforming the industry’s business model g Further ISP cooperation needed g Growth for music video g Payment providers step up action on illegal sites g The next generation radio experience g Europe: Licensing helps digital consumers Disrupting illegal online businesses 31 The art of digital marketing 18-21 g Disrupting unlicensed cyberlockers g Reducing pre-release leaks g One Direction mobilise an online army g Dance label harnesses social media Digital music services worldwide 32-34 g A personal video for every fan: Linkin Park g Taking classical digital Cover photo credits: Michel Teló.
    [Show full text]
  • Digital Music Report 2014 Shows a Fast-Changing, Dynamic Business Is Moving to Unlock It
    ifpi_lift-off_ad_HD.pdf 1 28/02/14 14:23 Contents Introduction Long Live the Record Label 4 Plácido Domingo, Chairman, IFPI 24 Daft Punk: A physical campaign in the digital world Frances Moore, Chief Executive, IFPI Avicii: From club DJ to global superstar Hunter Hayes: The YouTube orchestra Facts, Figures and Trends Passenger and the Embassy of Music 6 Streaming and subscriptions surge Engaging fans in social networks in Brazil A diverse global market Katy Perry: A global phenomenon A mixed economy of revenue streams Tommy Torres: Harnessing the power of Twitter Revival in Scandinavia US stabilises as Europe grows Sweden: A Market Transformed Lighting up developing markets 34 A return to growth Attracting consumers to licensed services A continuous revenue stream Growing diversity Most Popular Artists of 2013 What next in Sweden? 12 Top selling global albums IFPI Global Recording Artist Chart China: New Hopes for a Licensed Music Market Top global singles 36 Moving to the paid model The importance of local repertoire Tackling piracy Lighting Up New Markets and Models Africa: Emerging Opportunity 16 The move to mobile 38 Digital services being established Access & ownership Expanding A&R activity The rise and rise of streaming and subscription More discovery, more from mobile Improving the Environment for Digital Music Streaming: “A sustainable income” 40 Consumer attitudes to piracy Monetising music video Website blocking proves effective Internet radio — looking globally vKontakte: Stifling a licensed business in Russia Engaging
    [Show full text]
  • Efficient Optical Music Recognition Validation Using MIDI Sequence Data by Janelle C
    Efficient Optical Music Recognition Validation using MIDI Sequence Data by Janelle C. Sands Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Engineering in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY May 2020 ○c Massachusetts Institute of Technology 2020. All rights reserved. Author................................................................ Department of Electrical Engineering and Computer Science May 12, 2020 Certified by. Michael Scott Cuthbert Associate Professor Thesis Supervisor Accepted by . Katrina LaCurts Chair, Master of Engineering Thesis Committee ii Efficient Optical Music Recognition Validation using MIDI Sequence Data by Janelle C. Sands Submitted to the Department of Electrical Engineering and Computer Science on May 12, 2020, in partial fulfillment of the requirements for the degree of Master of Engineering in Electrical Engineering and Computer Science Abstract Despite advances in optical music recognition (OMR), resultant scores are rarely error-free. The power of these OMR systems to automatically generate searchable and editable digital representations of physical sheet music is lost in the tedious manual effort required to pinpoint and correct these errors post-OMR, or evento just confirm no errors exist. To streamline post-OMR error correction, I developeda corrector to automatically identify discrepancies between resultant OMR scores and corresponding Musical Instrument Digital Interface (MIDI) scores and then either automatically fix errors, or in ambiguous cases, notify the user to manually fix errors. This tool will be open source, so anyone can contribute to further improving the accuracy of OMR tools and expanding the amount of trusted digitized music. Thesis Supervisor: Michael Scott Cuthbert Title: Associate Professor iii iv Acknowledgments Thank you to my advisor, Professor Cuthbert, for sharing your enthusiasm, expertise, and encouragement throughout this project.
    [Show full text]
  • Digital-Music-Report-2015
    CONTENTS 4 25 Introduction Moving to a global release day in 2015 Plácido Domingo, chairman, IFPI Frances Moore, chief executive, IFPI 27 Country case studies 6 Brazil: a top performing market Market overview China: moving towards paid services An evolving portfolio business Germany: streams grow, CDs resilient Regional picture 32 10 Music and the wider economy Most popular artists and global bestsellers of 2014 The broader effect of investment in artists IFPI Global Recording Artist of the Year 2014 Music helps drive social media Top selling global albums & singles of 2014 Music and economic growth How streaming is changing the charts Tomorrow’s world 14 34 Key recording industry trends in 2014 Labels working with artists: four case studies Subscription drives the music portfolio David Guetta: using data intelligently New global services, more reach Lucas Lucco: artist and digital native Smartphones and partnerships drive growth Sam Smith: innovative promotion Getting subscription to the mass market Wakin Chau: interactive digital promotion Streaming revenue models Streaming services compete on curation 38 Payment options diversify Tackling digital piracy Downloads decline Consumer attitudes towards piracy Video streaming surges Website blocking Search engines 20 Advertising and payment providers New IFPI data shows artist payments Piracy and apps as share of revenues are up IFPI’s content protection work 22 41 Fixing the ‘value gap’ in digital music Digital music services worldwide Cover Taylor Swift photo by Sarah Barlow + Stephen Schofield Robin Schulz photo by Geturshot.com Wakin Chau photo courtesy of Rock Group One Direction photo by RJ Shaughnessy Lucas Lucco photo by Guto Costa Ed Sheeran photo by Ben Watts Katy Perry photo by Cass Bird This report includes new findings from a consumer study carried out by IPSOS in January 2015 across 13 of the world’s leading music markets (Australia, Brazil, Canada, France, Germany, Great Britain, Italy, Japan, Mexico, South Korea, Spain, Sweden, United States), www.ifpi.org commissioned by IFPI.
    [Show full text]
  • A Large-Scale MIDI Dataset for Classical Piano Music Qiuqiang Kong,* Bochen Li,* Jitong Chen,* Yuxuan Wang*
    Qiuqiang Kong, Bochen Li, Jitong Chen, Yuxuan Wang (2020). GiantMIDI-Piano: A large-scale MIDI dataset for classical piano mu- sic, Transactions of the International Society for Music Information Re- trieval, V(N), pp. xx–xx, DOI: https://doi.org/xx.xxxx/xxxx.xx ARTICLE TYPE GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music Qiuqiang Kong,* Bochen Li,* Jitong Chen,* Yuxuan Wang* Abstract Symbolic music datasets are important for music information retrieval and musical analysis. However, there is a lack of large-scale symbolic dataset for classical piano music. In this arti- cle, we create a GiantMIDI-Piano dataset containing 10,854 unique piano solo pieces composed by 2,786 composers. The dataset is collected as follows, we extract music piece names and composer names from the International Music Score Library Project (IMSLP). We search and download their corresponding audio recordings from the internet. We apply a convolutional neural network to detect piano solo pieces. Then, we transcribe those piano solo recordings to Musical Instrument Digital Interface (MIDI) files using our recently proposed high-resolution piano transcription system. Each transcribed MIDI file contains onset, offset, pitch and veloc- ity attributes of piano notes, and onset and offset attributes of sustain pedals. GiantMIDI-Piano contains 34,504,873 transcribed notes, and contains metadata information of each music piece. To our knowledge, GiantMIDI-Piano is the largest classical piano MIDI dataset so far. We anal- yses the statistics of GiantMIDI-Piano including the nationalities, the number and duration of works of composers. We show the chroma, interval, trichord and tetrachord frequencies of six composers from different eras to show that GiantMIDI-Piano can be used for musical analysis.
    [Show full text]
  • Recording Industry Association of America, Inc
    Before the UNITED STATES COPYRIGHT OFFICE Washington, D.C. In the Matter of: Docket No. 2012-7 Mechanical and Digital Phonorecord Delivery Compulsory License REPLY COMMENTS OF THE RECORDING INDUSTRY ASSOCIATION OF AMERICA, INC. The Recording Industry Association of America, Inc. ("RIAA") submits these Reply Comments in response to the Copyright Office's Notice of Proposed Rulemaking ("NPRM") in the above-captioned proceeding. The purpose of these Reply Comments is to address certification of statements (Section 3.C of the NPRM) and to respond to the initial comments of the National Music Publishers' Association, The Harry Fox Agency, Inc., the Songwriters' Guild of America, Inc. and the Nashville Songwriters Association International (collectively, the "publisher/writer groups") concerning treatment of negative reserve balances (Section LB of the NPRM) and identification of third-party distributors of permanent digital downloads and ringtones (Section 3.B of the NPRM). I. Certification The subject of certification is addressed at length in the Joint Reply Comments that RIAA is filing with the Digital Media Association, National Music Publishers' Association, Inc., The Harry Fox Agency, Inc. and Music Reports, Inc. (collectively, the "Joint Commenters"), so we address it only briefly here. RIAA (like the other Joint Commenters) believes that fixing the certification provisions of the current regulations is an important aspect of this proceeding. RIAA and its member companies have a general desire to have a workable compulsory license system, and that requires a certification process that both is workable for licensees and provides copyright owners reasonable assurance that they are receiving accurate statements. The certification issue has proven challenging to resolve.
    [Show full text]
  • A Closer Look at the Yale–Classical Archives Corpus (C
    Empirical Musicology Review Vol. 11, No. 1, 2016 Big Data, Big Questions: A Closer Look at the Yale–Classical Archives Corpus (c. 2015) TREVOR de CLERCQ[1] Middle Tennessee State University ABSTRACT: This paper responds to the article by Christopher White and Ian Quinn, in which these authors introduce the Yale-Classical Archives Corpus (YCAC). I begin by making some general observations about the corpus, especially with regard to ramifications of the keyboard-performance origins of many pieces in the original MIDI collection. I then assess the accuracy of the scale-degree and local-key fields in the database, which were generated by the Bellman-Budge key-finding algorithm. I point out that some of the inaccuracies from the key-finding algorithm’s output may influence the results we obtain from statistical studies of this corpus. I also offer an alternative analysis to the authors’ finding that the ratio of V7 to V chords increases over time in common-practice music. Specifically, I conjecture that this finding may be the result of (or related to) increasing instrumental resources over time. I close with some recommendations for future versions of the corpus, such as enabling end users to help repair transcription errors as well as offer ground truths for harmonic analyses and key area information. Submitted 2015 December 13; accepted 2016 February 8. KEYWORDS: corpus analysis, key-finding, modulation, tonality, machine learning IN the current issue of this journal, Christopher White and Ian Quinn provide an overview of the Yale- Classical Archives Corpus (YCAC)—a massive collection of CSV data files derived from user-generated MIDI encodings of more than 14,000 works, spanning from the 1500s to the late 20th century and representing a broad range of Western classical music styles.
    [Show full text]
  • Sm21-3Fr.Pdf
    sm21-3_FR_01_Cover-Abonnement_sm21-3_FR_pXX 15-10-28 11:18 AM Page 1 sm21-3_FR_02_ADs_sm21-3_FR_pXX 2015-10-27 4:04 PM Page 2 5 décembre 2015 Chapelle historique du Bon Pasteur 100, rue Sherbrooke E. 2015 Montréal 3 E ÉDITION EXPOSITION + CONCERT // EXHIBITION + CONCERT Lutherie et archèterie contemporaines Contemporary Violin and Bow Making 11h - 21h EXPOSITION // EXHIBITION Violons, altos, violoncelles et archets contemporains Contemporary violins, violas, cellos and bows 14h CONCERT Quatuor Molinari Quartet 16h CONFÉRENCE // CONFERENCE Véronique Brouillette Kinésiologue // Kinesiologist 19h RÉCITAL EN TRIO // TRIO RECITAL Andrew Wan Violon solo de l’OSM // MSO Concertmaster Brian Manker Violoncelle solo de l’OSM // MSO Principal Cello Philip Chiu Piano, prix Goyer 2015-2016 Performance surprise au violon solo de Marc Djokic en après-midi. Surprise violin solo performance by Marc Djokic, in the afternoon. Entrée gratuite. Free admission. [email protected] leforumdesfabricants.org Au service des artisans. Au service de la musique. Serving craftmanship. Serving music. sm21-3_FR_03_UdeMad_sm21-3_FR_pXX 2015-10-26 6:34 PM Page 3 Novembre – Décembre 2015 4 20 26 27 5 Miroslav Vitouš Benedetto Lupo Ron Di Lauro Gilles Cantagrel Jean-François Rivest Lundi 2, jeudi 5, vendredi 6 et lundi 9 novembre Samedi 21 novembre – 19 h 30, salle Claude-Champagne Salles Serge-Garant (B-484) et Jean-Papineau-Couture (B-421) CONCERT Hommage à Vladimir Landsman CONFÉRENCES de prestige de l’Observatoire interdisciplinaire La communauté facultaire célébrera le violoniste-pédagogue Vladimir de création et de recherche en musique (OICRM) Landsman qui prenait tout récemment sa retraite après avoir enseigné Invité : Christophe Pirenne, docteur en musicologie de l’Université pendant quarante ans à la Faculté de musique.
    [Show full text]