The Trumpet Shall Sound: De-anonymizing jazz recordings
|
|
- Julius Curtis
- 5 years ago
- Views:
Transcription
1 The Trumpet Shall Sound: De-anonymizing jazz recordings Janet Lazar Rutgers University New Brunswick, NJ, USA Michael Lesk Rutgers University New Brunswick, NJ, USA We are experimenting with automated techniques to identify performers on jazz recordings by using stylistic measures of acoustic signals. Many early jazz recordings do not identify individual musicians, leaving them under-appreciated. We look at individual notes and phrasing for recognition of jazz trumpeters as an example. Jazz, performer identification, music analysis. 1. INTRODUCTION For much of the 20th century jazz recordings did not contain full listings of the performers; attributions would only name a group such as "Count Basie and his All American Rhythm Section" or "Duke Ellington and his Orchestra". Who were the actual performers? Our goal is to recognize them automatically, using jazz trumpeters as an example. The pictures below are all from Wikipedia. Louis Armstrong Harry James Wynton Marsalis Identification of flamenco singers and classical pianists has been studied before [Kroher 2014, Saunders 2008]; the jazz problem is more complex because there is no written score to be aligned with the notes played. However, experienced human listeners can recognize the performers, so the problem is feasible. Some researchers have invested in manual creation of the score [Abesser 2015] followed by a complex separation of the playing of each performer. We ve been looking at solo passages, identified by ear, but hoping to recognize them mechanically in the future. Why not try this as a very general machine learning problem? One could feed all the data into WEKA and sit back and watch. However, there isn t enough data: we have at most hundreds, not millions, of samples. Worse yet, there are many accidental properties of the acoustic signals. For example, different recording studios used microphones with different frequency limits. Until the 1950s many microphones recorded only up to 10kHz [Ford]. We would not wish to train a system on whether a recording was made at RCA in Camden, NJ or at Columbia in New York. What features would be characteristic of musical style? The diagram below is from [Ramirez 2007] and shows the intensity contour of a single note: 279
2 Multi-note features, derived from phrasing, include: Staccato/legato notes separated or continuous? Beat timing regularly spaced notes or ragged time. 2. ST. LOUIS BLUES What features might be exploited for machine classification? Single-note features include: Vibrato are notes steady or wavering? Tone complexity are the notes simple tones or many additional frequencies? Onset speed: do the notes rise quickly or slowly in intensity? Decay speed: do the notes stop quickly or does the performer tail off each note? For demonstration purposes, and to test software, we are using recordings of W. C. Handy s St. Louis Blues, written in 1914 and recorded more than 100 times. Here are sound spectrograms for snippets of sound by Louis Armstrong, Harry James and Wynton Marsalis. The software used in this paper includes BeatRoot [Dixon] and the MIR Toolbox [Lartillot]; we thank the creators and maintainers of these programs. Figure 1: Sound spectrograms of three trumpeters playing St. Louis Blues. Armstrong has the most complex sound (least dominated by the main note frequency) while Marsalis played fewer tones in each note. Marsalis playing is the most staccato; Armstrong and James played more continuously. Looking at frequency stability, Marsalis plays with the most stable notes, i.e., the least vibrato, while James is a bit more variable and Armstrong still more. For another comparison, Figure 2 shows sound spectrograms for about 0.2 seconds (a single note, roughly) taken from three different places for each performer. All are again St. Louis Blues. Look here at the extent to which the pure note and its overtones dominate the signal. Marsalis is playing with the least sound beyond the specific note; James has a more complex note, with extra overtones; and Armstrong has much more in the way of low frequency components in the notes. 280
3 Figure 2: Single-note sound spectrograms. Figure 3: Single-note, Benny Goodman (top), Harry James (bottom). What would we see if we compared two different clarinetists? The next pair of spectra, in Figure 4, show Benny Goodman above and Artie Shaw. Figure 4: Benny Goodman (top), Artie Shaw (bottom). Compared to the trumpet both are weighted to lower frequency and simpler in structure. Comparing these two, Benny Goodman s notes are purer and contain fewer frequencies. 281
4 3. CLARINET AND HARP What happens if we look at other instruments? Figure 3 shows a comparison of Benny Goodman (above, clarinet) with Harry James (below, trumpet). Note the generally lower frequency spectrum of the clarinet and the complexity of the trumpet notes in terms of frequencies. As another example, we took sound spectra of four different harpists. In Figure 5, the top left spectrum is Lucia Bova, top right is Csilla Gulyas, bottom left is Maria Graf and bottom right is Judy Loman. They are all playing C. P. E. Bach s Harp Sonata in G major, Wq 139. Figure 5: Four harpists. Left column: Lucia Bova, Maria Graf. Right column: Csilla Gulyas, Judy Loman. We then calculated the basic tempo for each and the attack time, measuring off the sound spectra, using two samples for each player. Below is a plot showing that the performers differ but each tends to repeat her characteristic choices. 4. CONCLUSION The longer-run purpose of this work is to help with cataloging old recordings. Since music had no requirement for compulsory deposit in the United States until the 1970s, the Library of Congress has an unusually incomplete collection. Rutgers University, at its Institute of Jazz Studies in Newark, NJ, holds more than 100,000 sound recordings, and this is the largest jazz repository. Unfortunately, practical difficulties, such as fragility of records, and legal difficulties, such as copyright ownership of recordings made by companies that may be long out of business, impede the study of these recordings. We hope that by automating the creation of metadata we can help the scholars and bring recognition to artists whose contributions are fading from memory and insufficiently documented. 5, REFERENCES Figure 6: Distribution of tempi and attack time. Abesser, J., Cano, E., Frieier, K., Pfleidere, M., Zaddach, W.-G. (2015) Score-Informed Analysis of Intonation and Pitch Modulations in Jazz Solos. 16 th conference, International Society for Music Information Retrieval. 282
5 Dixon, S. (2001) An Interactive Beat Tracking and Visualisation System. In Proceedings of the 2001 International Computer Music Conference (ICMC'2001). Ford, T. (2005) A recent history of ribbon microphones. Ty Ford Audio and Video, Blogspot. t-history-of-ribbon-microphones.html (retrieved 14 June 2016). Saunders, C., Hardoon, D., Shawe-Taylor, J., Widmer, G. (2008) Using string kernels to identify famous performers from their playing style. Intelligent Data Analysis, 12(4), pp Kroher, Nadine; Gómez, Emilia (2014). Automatic Singer Identification For Improvisational Styles Based On Vibrato, Timbre And Statistical Performance Descriptors. Proceedings ICMCISMCI2014 (Joint International Computer Music and Sound and Music conference), September, Athens, Greece, pp Lartillot, O., Toiviainen, P., and Eerola, T, (2008). A matlab toolbox for music information retrieval. In C. Preisach, P. D. H. Burkhardt, P. D. L. Schmidt-Thieme, and P. D. R. Decker (eds.), Data Analysis, Machine Learning and Applications, Studies in Classification, Data Analysis, and Knowledge Organization, pp Springer, Berlin/Heidelberg. Ramirez, R., Maestre, E., Pertusa, A., Gómez, E., and Serra, X. (2007) Performance-based interpreter identification in saxophone audio recordings. IEEE Transactions on Circuits and Systems for Video Technology, 17(3), pp
OBSERVED DIFFERENCES IN RHYTHM BETWEEN PERFORMANCES OF CLASSICAL AND JAZZ VIOLIN STUDENTS
OBSERVED DIFFERENCES IN RHYTHM BETWEEN PERFORMANCES OF CLASSICAL AND JAZZ VIOLIN STUDENTS Enric Guaus, Oriol Saña Escola Superior de Música de Catalunya {enric.guaus,oriol.sana}@esmuc.cat Quim Llimona
More informationInstrument Recognition in Polyphonic Mixtures Using Spectral Envelopes
Instrument Recognition in Polyphonic Mixtures Using Spectral Envelopes hello Jay Biernat Third author University of Rochester University of Rochester Affiliation3 words jbiernat@ur.rochester.edu author3@ismir.edu
More informationIntroductions to Music Information Retrieval
Introductions to Music Information Retrieval ECE 272/472 Audio Signal Processing Bochen Li University of Rochester Wish List For music learners/performers While I play the piano, turn the page for me Tell
More informationEffects of acoustic degradations on cover song recognition
Signal Processing in Acoustics: Paper 68 Effects of acoustic degradations on cover song recognition Julien Osmalskyj (a), Jean-Jacques Embrechts (b) (a) University of Liège, Belgium, josmalsky@ulg.ac.be
More informationA. began in New Orleans during 1890s. B. Jazz a mix of African and European traditions. 1. Storyville District w/ Creoles of Color
A. began in New Orleans during 1890s 1. Storyville District w/ Creoles of Color B. Jazz a mix of African and European traditions 1. African influences: tonal coloration, blues notes, instrumental and vocal
More informationTowards Music Performer Recognition Using Timbre Features
Proceedings of the 3 rd International Conference of Students of Systematic Musicology, Cambridge, UK, September3-5, 00 Towards Music Performer Recognition Using Timbre Features Magdalena Chudy Centre for
More informationAutomatic characterization of ornamentation from bassoon recordings for expressive synthesis
Automatic characterization of ornamentation from bassoon recordings for expressive synthesis Montserrat Puiggròs, Emilia Gómez, Rafael Ramírez, Xavier Serra Music technology Group Universitat Pompeu Fabra
More informationTOWARD UNDERSTANDING EXPRESSIVE PERCUSSION THROUGH CONTENT BASED ANALYSIS
TOWARD UNDERSTANDING EXPRESSIVE PERCUSSION THROUGH CONTENT BASED ANALYSIS Matthew Prockup, Erik M. Schmidt, Jeffrey Scott, and Youngmoo E. Kim Music and Entertainment Technology Laboratory (MET-lab) Electrical
More informationMusic Similarity and Cover Song Identification: The Case of Jazz
Music Similarity and Cover Song Identification: The Case of Jazz Simon Dixon and Peter Foster s.e.dixon@qmul.ac.uk Centre for Digital Music School of Electronic Engineering and Computer Science Queen Mary
More informationComputational Modelling of Harmony
Computational Modelling of Harmony Simon Dixon Centre for Digital Music, Queen Mary University of London, Mile End Rd, London E1 4NS, UK simon.dixon@elec.qmul.ac.uk http://www.elec.qmul.ac.uk/people/simond
More informationData-Driven Solo Voice Enhancement for Jazz Music Retrieval
Data-Driven Solo Voice Enhancement for Jazz Music Retrieval Stefan Balke1, Christian Dittmar1, Jakob Abeßer2, Meinard Müller1 1International Audio Laboratories Erlangen 2Fraunhofer Institute for Digital
More information10 Visualization of Tonal Content in the Symbolic and Audio Domains
10 Visualization of Tonal Content in the Symbolic and Audio Domains Petri Toiviainen Department of Music PO Box 35 (M) 40014 University of Jyväskylä Finland ptoiviai@campus.jyu.fi Abstract Various computational
More informationCopyright 2009 Pearson Education, Inc. or its affiliate(s). All rights reserved. NES, the NES logo, Pearson, the Pearson logo, and National
Music (504) NES, the NES logo, Pearson, the Pearson logo, and National Evaluation Series are trademarks in the U.S. and/or other countries of Pearson Education, Inc. or its affiliate(s). NES Profile: Music
More informationTOWARD AN INTELLIGENT EDITOR FOR JAZZ MUSIC
TOWARD AN INTELLIGENT EDITOR FOR JAZZ MUSIC G.TZANETAKIS, N.HU, AND R.B. DANNENBERG Computer Science Department, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA E-mail: gtzan@cs.cmu.edu
More informationTrack 2 provides different music examples for each style announced.
Introduction Jazz is an American art form The goal of About 80 Years of Jazz in About 80 Minutes is to introduce young students to this art form through listening examples and insights into some of the
More informationArticulation * Catherine Schmidt-Jones. 1 What is Articulation? 2 Performing Articulations
OpenStax-CNX module: m11884 1 Articulation * Catherine Schmidt-Jones This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 Abstract An introduction to the
More informationTempo and Beat Analysis
Advanced Course Computer Science Music Processing Summer Term 2010 Meinard Müller, Peter Grosche Saarland University and MPI Informatik meinard@mpi-inf.mpg.de Tempo and Beat Analysis Musical Properties:
More informationMUSI-6201 Computational Music Analysis
MUSI-6201 Computational Music Analysis Part 9.1: Genre Classification alexander lerch November 4, 2015 temporal analysis overview text book Chapter 8: Musical Genre, Similarity, and Mood (pp. 151 155)
More informationPRESCOTT UNIFIED SCHOOL DISTRICT District Instructional Guide January 2016
Grade Level: 9 12 Subject: Jazz Ensemble Time: School Year as listed Core Text: Time Unit/Topic Standards Assessments 1st Quarter Arrange a melody Creating #2A Select and develop arrangements, sections,
More informationNATIONAL SENIOR CERTIFICATE GRADE 12
NATIONAL SENIOR CERTIFICATE GRADE 12 MUSIC P2 NOVEMBER 2017 MARKS: 30 TIME: 1½ hours CENTRE NUMBER: EXAMINATION NUMBER: FOR OFFICIAL USE ONLY QUESTION MARKS OBTAINED MODERATED MAX. MARKS OBTAINED SIGN
More informationMusic Emotion Recognition. Jaesung Lee. Chung-Ang University
Music Emotion Recognition Jaesung Lee Chung-Ang University Introduction Searching Music in Music Information Retrieval Some information about target music is available Query by Text: Title, Artist, or
More informationTHE SOUND OF SADNESS: THE EFFECT OF PERFORMERS EMOTIONS ON AUDIENCE RATINGS
THE SOUND OF SADNESS: THE EFFECT OF PERFORMERS EMOTIONS ON AUDIENCE RATINGS Anemone G. W. Van Zijl, Geoff Luck Department of Music, University of Jyväskylä, Finland Anemone.vanzijl@jyu.fi Abstract Very
More informationJazz Artist Project Directions:
Jazz Artist Project Directions: Choose one jazz artist from the designated list Create a poster that includes: - Artist s Name - Birth and Death Dates - Instrument (Including vocal) - Time era (Blues,
More informationMusic Understanding and the Future of Music
Music Understanding and the Future of Music Roger B. Dannenberg Professor of Computer Science, Art, and Music Carnegie Mellon University Why Computers and Music? Music in every human society! Computers
More informationPreview Only. Legal Use Requires Purchase. Emily JAZZ. Music by JOHNNY MANDEL Words by JOHNNY MERCER Arranged by LISA DeSPAIN INSTRUMENTATION
a division of Alfred JAZZ Emily Music by JOHNNY MANDEL Words by JOHNNY MERCER Arranged by LISA DeSPAIN INSTRUMENTATION Conductor 1st Eb Alto Saxophone 2nd Eb Alto Saxophone 1st Bb Tenor Saxophone 2nd Bb
More informationMODELING RHYTHM SIMILARITY FOR ELECTRONIC DANCE MUSIC
MODELING RHYTHM SIMILARITY FOR ELECTRONIC DANCE MUSIC Maria Panteli University of Amsterdam, Amsterdam, Netherlands m.x.panteli@gmail.com Niels Bogaards Elephantcandy, Amsterdam, Netherlands niels@elephantcandy.com
More informationFINE ARTS Institutional (ILO), Program (PLO), and Course (SLO) Alignment
FINE ARTS Institutional (ILO), Program (PLO), and Course (SLO) Program: Music Number of Courses: 52 Date Updated: 11.19.2014 Submitted by: V. Palacios, ext. 3535 ILOs 1. Critical Thinking Students apply
More informationQuery By Humming: Finding Songs in a Polyphonic Database
Query By Humming: Finding Songs in a Polyphonic Database John Duchi Computer Science Department Stanford University jduchi@stanford.edu Benjamin Phipps Computer Science Department Stanford University bphipps@stanford.edu
More informationNCEA Level 2 Music (91275) 2012 page 1 of 6. Assessment Schedule 2012 Music: Demonstrate aural understanding through written representation (91275)
NCEA Level 2 Music (91275) 2012 page 1 of 6 Assessment Schedule 2012 Music: Demonstrate aural understanding through written representation (91275) Evidence Statement Question with Merit with Excellence
More informationThe song remains the same: identifying versions of the same piece using tonal descriptors
The song remains the same: identifying versions of the same piece using tonal descriptors Emilia Gómez Music Technology Group, Universitat Pompeu Fabra Ocata, 83, Barcelona emilia.gomez@iua.upf.edu Abstract
More informationImproving Beat Tracking in the presence of highly predominant vocals using source separation techniques: Preliminary study
Improving Beat Tracking in the presence of highly predominant vocals using source separation techniques: Preliminary study José R. Zapata and Emilia Gómez Music Technology Group Universitat Pompeu Fabra
More informationSenior High School Band District-Developed End-of-Course (DDEOC) Exam Study Guide
Senior High School Band District-Developed End-of-Course (DDEOC) Exam Study Guide Division of Academic Support, Office of Academics & Transformation Miami-Dade County Public Schools 2014-2015 Contents
More informationPreparatory Orchestra Performance Groups INSTRUMENTAL MUSIC SKILLS
Course #: MU 23 Grade Level: 7-9 Course Name: Preparatory Orchestra Level of Difficulty: Average Prerequisites: Teacher recommendation/audition # of Credits: 2 Sem. 1 Credit MU 23 is an orchestra class
More informationCambridge International Examinations Cambridge International General Certifi cate of Secondary Education
Cambridge International Examinations Cambridge International General Certifi cate of Secondary Education MUSIC 040/0 Paper Listening For examination from 05 MARK SCHEME Maximum Mark: 70 Specimen The syllabus
More informationTHE MUSIC ACADEMY AT CCTS.
THE MUSIC ACADEMY AT CCTS Audition requirements for Instrumentalists applying for acceptance into The Music Academy at Camden County Technical Schools www.ccts.org YOUR MUSIC ACADEMY AUDITION DATE Gloucester
More informationChord Classification of an Audio Signal using Artificial Neural Network
Chord Classification of an Audio Signal using Artificial Neural Network Ronesh Shrestha Student, Department of Electrical and Electronic Engineering, Kathmandu University, Dhulikhel, Nepal ---------------------------------------------------------------------***---------------------------------------------------------------------
More informationLevel of Difficulty: Beginning Prerequisites: None
Course #: MU 01 Grade Level: 7 9 Course Name: Level of Difficulty: Beginning Prerequisites: None # of Credits: 1 2 Sem. ½ 1 Credit A performance oriented course with emphasis on the basic fundamentals
More informationSAMPLE ASSESSMENT TASKS MUSIC JAZZ ATAR YEAR 11
SAMPLE ASSESSMENT TASKS MUSIC JAZZ ATAR YEAR 11 Copyright School Curriculum and Standards Authority, 2014 This document apart from any third party copyright material contained in it may be freely copied,
More informationUNIVERSITY OF DUBLIN TRINITY COLLEGE
UNIVERSITY OF DUBLIN TRINITY COLLEGE FACULTY OF ENGINEERING & SYSTEMS SCIENCES School of Engineering and SCHOOL OF MUSIC Postgraduate Diploma in Music and Media Technologies Hilary Term 31 st January 2005
More informationTHE BASIS OF JAZZ ASSESSMENT
THE BASIS OF JAZZ ASSESSMENT The tables on pp. 42 5 contain minimalist criteria statements, giving clear guidance as to what the examiner is looking for in the various sections of the exam. Every performance
More informationWeek 14 Music Understanding and Classification
Week 14 Music Understanding and Classification Roger B. Dannenberg Professor of Computer Science, Music & Art Overview n Music Style Classification n What s a classifier? n Naïve Bayesian Classifiers n
More informationWEST END BLUES / MARK SCHEME
3. You will hear two extracts of music, both performed by jazz ensembles. You may wish to place a tick in the box each time you hear the extract. 5 1 1 2 2 MINS 1 2 Answer questions (a-f) in relation to
More informationSAMPLE ASSESSMENT TASKS MUSIC CONTEMPORARY ATAR YEAR 11
SAMPLE ASSESSMENT TASKS MUSIC CONTEMPORARY ATAR YEAR 11 Copyright School Curriculum and Standards Authority, 014 This document apart from any third party copyright material contained in it may be freely
More informationClassification of Musical Instruments sounds by Using MFCC and Timbral Audio Descriptors
Classification of Musical Instruments sounds by Using MFCC and Timbral Audio Descriptors Priyanka S. Jadhav M.E. (Computer Engineering) G. H. Raisoni College of Engg. & Mgmt. Wagholi, Pune, India E-mail:
More informationRobert Alexandru Dobre, Cristian Negrescu
ECAI 2016 - International Conference 8th Edition Electronics, Computers and Artificial Intelligence 30 June -02 July, 2016, Ploiesti, ROMÂNIA Automatic Music Transcription Software Based on Constant Q
More informationAdvanced Orchestra Performance Groups
Course #: MU 26 Grade Level: 7-9 Course Name: Advanced Orchestra Level of Difficulty: Average-High Prerequisites: Teacher recommendation/audition # of Credits: 2 Sem. 1 Credit MU 26 is a performance-oriented
More informationMiddle School Band District-Developed End-of-Course (DDEOC) Exam Study Guide
Middle School Band District-Developed End-of-Course (DDEOC) Exam Study Guide Division of Academic Support, Office of Academics & Transformation Miami-Dade County Public Schools 2014-2015 Contents Frequently
More informationAdvanced Lesson Plan for Young Performers Initiative: Rockin In Rhythm BEFORE THE VIDEO
Advanced Lesson Plan for Young Performers Initiative: Rockin In Rhythm NOTE TO TEACHER: This lesson plan is designed to encourage focused listening as well as individual and group recognition of the contrast
More informationMiddle School General Music Unit Plan Overview
Middle School General Music Unit Plan Overview Name: _Will Karsten Unit Topic/Title: _Blues and Jazz Detailed Unit Description: Louis Armstrong said, "Jazz is music that's never played the same way once."
More informationLEVELS IN NATIONAL CURRICULUM MUSIC
LEVELS IN NATIONAL CURRICULUM MUSIC Pupils recognise and explore how sounds can be made and changed. They use their voice in different ways such as speaking, singing and chanting. They perform with awareness
More informationLEVELS IN NATIONAL CURRICULUM MUSIC
LEVELS IN NATIONAL CURRICULUM MUSIC Pupils recognise and explore how sounds can be made and changed. They use their voice in different ways such as speaking, singing and chanting. They perform with awareness
More informationMusic Information Retrieval. Juan Pablo Bello MPATE-GE 2623 Music Information Retrieval New York University
Music Information Retrieval Juan Pablo Bello MPATE-GE 2623 Music Information Retrieval New York University 1 Juan Pablo Bello Office: Room 626, 6th floor, 35 W 4th Street (ext. 85736) Office Hours: Wednesdays
More informationMusic Complexity Descriptors. Matt Stabile June 6 th, 2008
Music Complexity Descriptors Matt Stabile June 6 th, 2008 Musical Complexity as a Semantic Descriptor Modern digital audio collections need new criteria for categorization and searching. Applicable to:
More informationSimple Harmonic Motion: What is a Sound Spectrum?
Simple Harmonic Motion: What is a Sound Spectrum? A sound spectrum displays the different frequencies present in a sound. Most sounds are made up of a complicated mixture of vibrations. (There is an introduction
More informationMusic Education. Test at a Glance. About this test
Music Education (0110) Test at a Glance Test Name Music Education Test Code 0110 Time 2 hours, divided into a 40-minute listening section and an 80-minute written section Number of Questions 150 Pacing
More informationMusic Information Retrieval with Temporal Features and Timbre
Music Information Retrieval with Temporal Features and Timbre Angelina A. Tzacheva and Keith J. Bell University of South Carolina Upstate, Department of Informatics 800 University Way, Spartanburg, SC
More informationNATIONAL SENIOR CERTIFICATE GRADE 12
NATIONAL SENIOR CERTIFICATE GRADE 12 MUSIC P2 NOVEMBER 2017 MARKING GUIDELINES MARKS: 30 These marking guidelines consist of 20 pages. Music/P2 2 DBE/November 2017 INSTRUCTIONS AND INFORMATION 1. This
More informationNew Orleans. Storyville, French Opera House, 1900
Jazz Jazz is a genre of music born in the African- American community in New Orleans in the early 20th century. It is a form of music that relies heavily on improvisation, syncopation, polyrhythms, and
More informationMusic for Alto Saxophone & Computer
Music for Alto Saxophone & Computer by Cort Lippe 1997 for Stephen Duke 1997 Cort Lippe All International Rights Reserved Performance Notes There are four classes of multiphonics in section III. The performer
More informationSemi-automated extraction of expressive performance information from acoustic recordings of piano music. Andrew Earis
Semi-automated extraction of expressive performance information from acoustic recordings of piano music Andrew Earis Outline Parameters of expressive piano performance Scientific techniques: Fourier transform
More informationSymphonic Pops Orchestra Performance Groups
Course #: MU 96 Grade Level: 10-12 Course Name: Symphonic Pops Orchestra Level of Difficulty: Average-High Prerequisites: Teacher recommendation/audition # of Credits: 2 Sem. 1 Credit MU 96 provides an
More informationMeasuring & Modeling Musical Expression
Measuring & Modeling Musical Expression Douglas Eck University of Montreal Department of Computer Science BRAMS Brain Music and Sound International Laboratory for Brain, Music and Sound Research Overview
More informationConnecticut State Department of Education Music Standards Middle School Grades 6-8
Connecticut State Department of Education Music Standards Middle School Grades 6-8 Music Standards Vocal Students will sing, alone and with others, a varied repertoire of songs. Students will sing accurately
More informationHowever, in studies of expressive timing, the aim is to investigate production rather than perception of timing, that is, independently of the listene
Beat Extraction from Expressive Musical Performances Simon Dixon, Werner Goebl and Emilios Cambouropoulos Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010 Vienna, Austria.
More informationLa Salle University. I. Listening Answer the following questions about the various works we have listened to in the course so far.
La Salle University MUS 150-A Art of Listening Midterm Exam Name I. Listening Answer the following questions about the various works we have listened to in the course so far. 1. Regarding the element of
More informationREHEARSAL STRATEGIES I AIN T GOT NOTHIN BUT THE BLUES BY LOREN SCHOENBERG
REHEARSAL STRATEGIES I AIN T GOT NOTHIN BUT THE BLUES BY LOREN SCHOENBERG Duke Ellington managed many miracles in his long life, but none is more worthy of study than how he managed to write music that
More informationTOWARDS IMPROVING ONSET DETECTION ACCURACY IN NON- PERCUSSIVE SOUNDS USING MULTIMODAL FUSION
TOWARDS IMPROVING ONSET DETECTION ACCURACY IN NON- PERCUSSIVE SOUNDS USING MULTIMODAL FUSION Jordan Hochenbaum 1,2 New Zealand School of Music 1 PO Box 2332 Wellington 6140, New Zealand hochenjord@myvuw.ac.nz
More informationTHE POTENTIAL FOR AUTOMATIC ASSESSMENT OF TRUMPET TONE QUALITY
12th International Society for Music Information Retrieval Conference (ISMIR 2011) THE POTENTIAL FOR AUTOMATIC ASSESSMENT OF TRUMPET TONE QUALITY Trevor Knight Finn Upham Ichiro Fujinaga Centre for Interdisciplinary
More informationESTIMATING THE ERROR DISTRIBUTION OF A TAP SEQUENCE WITHOUT GROUND TRUTH 1
ESTIMATING THE ERROR DISTRIBUTION OF A TAP SEQUENCE WITHOUT GROUND TRUTH 1 Roger B. Dannenberg Carnegie Mellon University School of Computer Science Larry Wasserman Carnegie Mellon University Department
More informationMusicians Adjustment of Performance to Room Acoustics, Part III: Understanding the Variations in Musical Expressions
Musicians Adjustment of Performance to Room Acoustics, Part III: Understanding the Variations in Musical Expressions K. Kato a, K. Ueno b and K. Kawai c a Center for Advanced Science and Innovation, Osaka
More informationMiddle School Chorus District-Developed End-of-Course (DDEOC) Exam Study Guide
Middle School Chorus District-Developed End-of-Course (DDEOC) Exam Study Guide Division of Academic Support, Office of Academics & Transformation Miami-Dade County Public Schools 2014-2015 Contents Frequently
More informationConcise Guide to Jazz
Test Item File For Concise Guide to Jazz Seventh Edition By Mark Gridley Created by Judith Porter Gaston College 2014 by PEARSON EDUCATION, INC. Upper Saddle River, New Jersey 07458 All rights reserved
More informationREHEARSAL STRATEGIES HARLEM CONGO BY LOREN SCHOENBERG,
REHEARSAL STRATEGIES HARLEM CONGO BY LOREN SCHOENBERG, Like most big band leaders, drummer Chick Webb relied heavily on composers and arrangers to write material that would give his band a distinctive
More informationMusic Standards for Band. Proficient Apply instrumental technique (e.g., fingerings, bowings, stickings, playing position, tone quality, articulation)
Music Standards for Band Product Performance 2. Develop and apply instrumental music skills to perform and communicate through the arts A. Instrumental Performance Skills Apply instrumental technique (e.g.,
More informationTERM 3 GRADE 5 Music Literacy
1 TERM 3 GRADE 5 Music Literacy Contents Revision... 3 The Stave... 3 The Treble clef... 3 Note Values and Rest Values... 3 Tempo... 4 Metre (Time Signature)... 4 Pitch... 4 Dynamics... 4 Canon... 4 Unison...
More informationTiming In Expressive Performance
Timing In Expressive Performance 1 Timing In Expressive Performance Craig A. Hanson Stanford University / CCRMA MUS 151 Final Project Timing In Expressive Performance Timing In Expressive Performance 2
More informationInternational School of Kenya
Creative Arts High School Strand 1: Developing practical knowledge and skills Standard 1.1: Sing, alone and with others, a varied repertoire of music 1.1.1 1.1.2 Sing band repertoire from many sources
More informationWHO IS WHO IN THE END? RECOGNIZING PIANISTS BY THEIR FINAL RITARDANDI
WHO IS WHO IN THE END? RECOGNIZING PIANISTS BY THEIR FINAL RITARDANDI Maarten Grachten Dept. of Computational Perception Johannes Kepler University, Linz, Austria maarten.grachten@jku.at Gerhard Widmer
More informationLa Salle University MUS 150 Art of Listening Final Exam Name
La Salle University MUS 150 Art of Listening Final Exam Name I. Listening Skill For each excerpt, answer the following questions. Excerpt One: - Vivaldi "Spring" First Movement 1. Regarding the element
More informationComputer Coordination With Popular Music: A New Research Agenda 1
Computer Coordination With Popular Music: A New Research Agenda 1 Roger B. Dannenberg roger.dannenberg@cs.cmu.edu http://www.cs.cmu.edu/~rbd School of Computer Science Carnegie Mellon University Pittsburgh,
More informationJazz is a music genre that started in the early 1900's or earlier, within the African-American communities of the Southern United States.
Jazz is a music genre that started in the early 1900's or earlier, within the African-American communities of the Southern United States. It combines African rhythms and European harmony to create a new
More informationK-12 Performing Arts - Music Standards Lincoln Community School Sources: ArtsEdge - National Standards for Arts Education
K-12 Performing Arts - Music Standards Lincoln Community School Sources: ArtsEdge - National Standards for Arts Education Grades K-4 Students sing independently, on pitch and in rhythm, with appropriate
More informationPsychophysiological measures of emotional response to Romantic orchestral music and their musical and acoustic correlates
Psychophysiological measures of emotional response to Romantic orchestral music and their musical and acoustic correlates Konstantinos Trochidis, David Sears, Dieu-Ly Tran, Stephen McAdams CIRMMT, Department
More informationVersion 5: August Requires performance/aural assessment. S1C1-102 Adjusting and matching pitches. Requires performance/aural assessment
Choir (Foundational) Item Specifications for Summative Assessment Code Content Statement Item Specifications Depth of Knowledge Essence S1C1-101 Maintaining a steady beat with auditory assistance (e.g.,
More informationGreenwich Music Objectives Grade 3 General Music
All students are required to take general music one hour per week. All students may elect to take orchestra. The annotations (e.g. *6c, *1d) in the curriculum are based on the National/Connecticut Standards.
More informationCALCULATING SIMILARITY OF FOLK SONG VARIANTS WITH MELODY-BASED FEATURES
CALCULATING SIMILARITY OF FOLK SONG VARIANTS WITH MELODY-BASED FEATURES Ciril Bohak, Matija Marolt Faculty of Computer and Information Science University of Ljubljana, Slovenia {ciril.bohak, matija.marolt}@fri.uni-lj.si
More informationMUSIC WESTERN ART. Western Australian Certificate of Education Examination, Question/Answer Booklet. Stage 3
Western Australian Certificate of Education Examination, 2015 Question/Answer Booklet MUSIC WESTERN ART Stage 3 Please place your student identification label in this box Student Number: In figures In
More informationExperiments on musical instrument separation using multiplecause
Experiments on musical instrument separation using multiplecause models J Klingseisen and M D Plumbley* Department of Electronic Engineering King's College London * - Corresponding Author - mark.plumbley@kcl.ac.uk
More informationOF THE ARTS ADMISSIONS GUIDE 2016 ACADEMY
SIBELIUS ACADEMY UNIVERSITY OF THE ARTS ADMISSIONS GUIDE 2016 JUNIOR ACADEMY CONTENTS 1. GENERAL INFORMATION...1 2. ELIGIBILITY...1 3. APPLICATION PROCEDURE...1 4. ENTRANCE EXAMINATIONS...1 5. ANNOUNCEMENT
More informationDimensional Music Emotion Recognition: Combining Standard and Melodic Audio Features
Dimensional Music Emotion Recognition: Combining Standard and Melodic Audio Features R. Panda 1, B. Rocha 1 and R. P. Paiva 1, 1 CISUC Centre for Informatics and Systems of the University of Coimbra, Portugal
More informationBefore I proceed with the specifics of each etude, I would like to give you some general suggestions to help prepare you for your audition.
TMEA ALL-STATE TRYOUT MUSIC BE SURE TO BRING THE FOLLOWING: 1. Copies of music with numbered measures 2. Copy of written out master class 1. Hello, My name is Dr. David Shea, professor of clarinet at Texas
More informationAutomatic Construction of Synthetic Musical Instruments and Performers
Ph.D. Thesis Proposal Automatic Construction of Synthetic Musical Instruments and Performers Ning Hu Carnegie Mellon University Thesis Committee Roger B. Dannenberg, Chair Michael S. Lewicki Richard M.
More informationHST 725 Music Perception & Cognition Assignment #1 =================================================================
HST.725 Music Perception and Cognition, Spring 2009 Harvard-MIT Division of Health Sciences and Technology Course Director: Dr. Peter Cariani HST 725 Music Perception & Cognition Assignment #1 =================================================================
More informationDEVELOPMENTS IN INSTRUMENTAL JAZZ; 1910 TO THE PRESENT DAY: AOS3
DEVELOPMENTS IN INSTRUMENTAL JAZZ; 1910 TO THE PRESENT DAY: AOS3 195 Duke Ellington Edward Kennedy Duke Ellington (1899 1974) was from Washington D.C. and was introduced to classical piano by music-loving
More informationAnalytic Comparison of Audio Feature Sets using Self-Organising Maps
Analytic Comparison of Audio Feature Sets using Self-Organising Maps Rudolf Mayer, Jakob Frank, Andreas Rauber Institute of Software Technology and Interactive Systems Vienna University of Technology,
More information19 th INTERNATIONAL CONGRESS ON ACOUSTICS MADRID, 2-7 SEPTEMBER 2007
19 th INTERNATIONAL CONGRESS ON ACOUSTICS MADRID, 2-7 SEPTEMBER 2007 AN HMM BASED INVESTIGATION OF DIFFERENCES BETWEEN MUSICAL INSTRUMENTS OF THE SAME TYPE PACS: 43.75.-z Eichner, Matthias; Wolff, Matthias;
More informationCreating a Successful Audition CD
Creating a Successful Audition CD The purpose of the following information is to help you record a quality audition CD for National Youth Band of Canada. The information has been divided into different
More informationfirst year charts Preview Only Legal Use Requires Purchase Pacific Attitude for jazz ensemble JAZZ VINCE GASSI INSTRUMENTATION
first year charts for jazz ensemble a division of Alfred JAZZ Pacific Attitude VINCE GASSI INSTRUMENTATION Conductor 1st Eb Alto Saxophone 2nd Eb Alto Saxophone 1st Bb Tenor Saxophone 2nd Bb Tenor Saxophone
More informationSpecifying Features for Classical and Non-Classical Melody Evaluation
Specifying Features for Classical and Non-Classical Melody Evaluation Andrei D. Coronel Ateneo de Manila University acoronel@ateneo.edu Ariel A. Maguyon Ateneo de Manila University amaguyon@ateneo.edu
More informationMusic Source Separation
Music Source Separation Hao-Wei Tseng Electrical and Engineering System University of Michigan Ann Arbor, Michigan Email: blakesen@umich.edu Abstract In popular music, a cover version or cover song, or
More information