Jam Sesh. Music to Your Ears, From You. Ben Dantowitz, Edward Du, Thomas Pinella, James Rutledge, and Stephen Watson
|
|
- Kathleen May
- 6 years ago
- Views:
Transcription
1 Jam Sesh Music to Your Ears, From You Ben Dantowitz, Edward Du, Thomas Pinella, James Rutledge, and Stephen Watson
2 Jam Sesh: What is it? Inspiration an application to support individual musicians with real-time background synthesis dependent on analyzed input Concept a tool that can be used by the everyday musician to enhance performance With the input by an individual, JamSesh plays chords to support the user
3 Origins You re may be thinking this has already been done In one sense, you could be right In another, we hope to show its unique potential
4 Prior Work Midi Utility Input MIDI file, set parameters GenJam Fixed structure, user-application exchange Others Intelligent musicians improvise with user
5 Unique Potential Real-Time Loosened structure User-centric Technology and music can be increasingly intertwined
6 Real Talk: Real-Time One of the core goals behind our project Achieving sound output with instantaneous analysis has proven to be difficult This goal has since been pushed to the back burner Inputting pitch while synthesizing output is particularly problematic tradeoff between measuring significant lengths of time
7 Application: Stephen, Ben, Edward Access users and synthesised music Set microphones, speakers, user instrument, and output instruments start stop pause recording/synthesis
8 Analysis: James, Thomas, Stephen Goal: Dissect the microphone input for pitch Analyze pitch over time to determine chord progression and key Pass retrieved information and parameters to Synthesis APIs: Tarsos and EchoNest
9 Synthesis: James, Thomas, Edward, Ben Goal: Generate a midi file with all the background music generated and pass the file to individual instrument threads / midi player Possible approach: Synthesis part takes the output of Analysis part (chord, current pitch, etc.) and Application part (user input of selected instruments, key, bpm, possibly chord progression). It then generates a list of pitches to play according to the chord, number of instruments, and current input pitch. Finally, it assigns pitches generated to proper instruments using music theory techniques (trying to avoid parallel fifth, tritone, and other situation that sounds bad), generates the final midi file and returns it.
10 Putting it Together, Together Constant collective revision of our overall vision With each meeting we discuss how the big picture is evolving That said, here is the current framework...
11
12 Individual Reports: Ben Application and Synthesis Given a hypothetical.wav file, synthesize output Researching sound classes Spearheading written work
13 Individual Reports: Edward Application and Synthesis Working on Java Applet Working on Synthesis solution and algorithm using Music Theory knowledge Research on MIDI file format
14 Individual Reports: James Analysis and Synthesis Writing the interfaces and general classes for communication between group classes. Researching procedural music generation. Research into the MIDI file type and use in java. Working on getting a real time MIDI interface working.
15 Individual Reports: Stephen Application and Analysis In charge of general coordination and communication between groups Writing the general computer application
16 Individual Reports: Thomas Analysis and Synthesis Pitch detection with Tarsos API Graphing live pitch Recording.wav file of live input Using EchoNest to determine key and tempo
17 Short Demonstration Enjoy!
18 Closing Thoughts Initial goal had to be scaled back, considerably As we race the clock, we are reevaluating end goals We may change the parameters as needed
19 References Biles, Al. "Al Biles -- The Home Page." Al Biles -- The Home Page. N.p., 25 June Web. 22 Oct < rit.edu/~jabics/>. "GenJam's Journey: From Tech to Music: Al Biles at TEDxBinghamtonUniversity." YouTube. YouTube, 21 Apr Web. 22 Oct < "JorenSix/TarsosDSP." GitHub. N.p., n.d. Web. 19 Oct < "Midi Utility." Midi Utility. KH Midi Music Ltd, n.d. Web. 19 Oct < "Software." 0110.be. N.p., n.d. Web. 19 Oct < "The Echo Nest." GitHub. N.p., n.d. Web. 19 Oct < "We Know Music..." The Echo Nest. N.p., n.d. Web. 19 Oct <
Jam Sesh: Final Report Music to Your Ears, From You Ben Dantowitz, Edward Du, Thomas Pinella, James Rutledge, and Stephen Watson
Jam Sesh 1 Jam Sesh: Final Report Music to Your Ears, From You Ben Dantowitz, Edward Du, Thomas Pinella, James Rutledge, and Stephen Watson Table of Contents Overview... 2 Prior Work... 2 APIs:... 3 Goals...
More informationOutline. Why do we classify? Audio Classification
Outline Introduction Music Information Retrieval Classification Process Steps Pitch Histograms Multiple Pitch Detection Algorithm Musical Genre Classification Implementation Future Work Why do we classify
More information6.UAP Project. FunPlayer: A Real-Time Speed-Adjusting Music Accompaniment System. Daryl Neubieser. May 12, 2016
6.UAP Project FunPlayer: A Real-Time Speed-Adjusting Music Accompaniment System Daryl Neubieser May 12, 2016 Abstract: This paper describes my implementation of a variable-speed accompaniment system that
More information1 Overview. 1.1 Nominal Project Requirements
15-323/15-623 Spring 2018 Project 5. Real-Time Performance Interim Report Due: April 12 Preview Due: April 26-27 Concert: April 29 (afternoon) Report Due: May 2 1 Overview In this group or solo project,
More informationAutomatic Music Clustering using Audio Attributes
Automatic Music Clustering using Audio Attributes Abhishek Sen BTech (Electronics) Veermata Jijabai Technological Institute (VJTI), Mumbai, India abhishekpsen@gmail.com Abstract Music brings people together,
More informationComputers Composing Music: An Artistic Utilization of Hidden Markov Models for Music Composition
Computers Composing Music: An Artistic Utilization of Hidden Markov Models for Music Composition By Lee Frankel-Goldwater Department of Computer Science, University of Rochester Spring 2005 Abstract: Natural
More informationBayesianBand: Jam Session System based on Mutual Prediction by User and System
BayesianBand: Jam Session System based on Mutual Prediction by User and System Tetsuro Kitahara 12, Naoyuki Totani 1, Ryosuke Tokuami 1, and Haruhiro Katayose 12 1 School of Science and Technology, Kwansei
More informationCurriculum Standard One: The student will listen to and analyze music critically, using vocabulary and language of music.
Curriculum Standard One: The student will listen to and analyze music critically, using vocabulary and language of music. 1. The student will analyze the uses of elements of music. A. Can the student analyze
More informationDAY 1. Intelligent Audio Systems: A review of the foundations and applications of semantic audio analysis and music information retrieval
DAY 1 Intelligent Audio Systems: A review of the foundations and applications of semantic audio analysis and music information retrieval Jay LeBoeuf Imagine Research jay{at}imagine-research.com Rebecca
More informationJam Tomorrow: Collaborative Music Generation in Croquet Using OpenAL
Jam Tomorrow: Collaborative Music Generation in Croquet Using OpenAL Florian Thalmann thalmann@students.unibe.ch Markus Gaelli gaelli@iam.unibe.ch Institute of Computer Science and Applied Mathematics,
More informationBen Neill and Bill Jones - Posthorn
Ben Neill and Bill Jones - Posthorn Ben Neill Assistant Professor of Music Ramapo College of New Jersey 505 Ramapo Valley Road Mahwah, NJ 07430 USA bneill@ramapo.edu Bill Jones First Pulse Projects 53
More informationAutoChorale An Automatic Music Generator. Jack Mi, Zhengtao Jin
AutoChorale An Automatic Music Generator Jack Mi, Zhengtao Jin 1 Introduction Music is a fascinating form of human expression based on a complex system. Being able to automatically compose music that both
More informationA Novel Approach to Automatic Music Composing: Using Genetic Algorithm
A Novel Approach to Automatic Music Composing: Using Genetic Algorithm Damon Daylamani Zad *, Babak N. Araabi and Caru Lucas ** * Department of Information Systems and Computing, Brunel University ci05ddd@brunel.ac.uk
More informationESP: Expression Synthesis Project
ESP: Expression Synthesis Project 1. Research Team Project Leader: Other Faculty: Graduate Students: Undergraduate Students: Prof. Elaine Chew, Industrial and Systems Engineering Prof. Alexandre R.J. François,
More informationPLOrk Beat Science 2.0 NIME 2009 club submission by Ge Wang and Rebecca Fiebrink
PLOrk Beat Science 2.0 NIME 2009 club submission by Ge Wang and Rebecca Fiebrink Introduction This document details our proposed NIME 2009 club performance of PLOrk Beat Science 2.0, our multi-laptop,
More informationPart II: Dipping Your Toes Fingers into Music Basics Part IV: Moving into More-Advanced Keyboard Features
Contents at a Glance Introduction... 1 Part I: Getting Started with Keyboards... 5 Chapter 1: Living in a Keyboard World...7 Chapter 2: So Many Keyboards, So Little Time...15 Chapter 3: Choosing the Right
More informationMusic 209 Advanced Topics in Computer Music Lecture 4 Time Warping
Music 209 Advanced Topics in Computer Music Lecture 4 Time Warping 2006-2-9 Professor David Wessel (with John Lazzaro) (cnmat.berkeley.edu/~wessel, www.cs.berkeley.edu/~lazzaro) www.cs.berkeley.edu/~lazzaro/class/music209
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 informationGimmeDaBlues: An Intelligent Jazz/Blues Player And Comping Generator for ios devices
GimmeDaBlues: An Intelligent Jazz/Blues Player And Comping Generator for ios devices Rui Dias 1, Telmo Marques 2, George Sioros 1, and Carlos Guedes 1 1 INESC-Porto / Porto University, Portugal ruidias74@gmail.com
More informationSIMSSA DB: A Database for Computational Musicological Research
SIMSSA DB: A Database for Computational Musicological Research Cory McKay Marianopolis College 2018 International Association of Music Libraries, Archives and Documentation Centres International Congress,
More informationArt of Sound. Professional soundware solution. PULSation. Reference Guide. Waldorf Pulse
Art of Sound Professional soundware solution PULSation Reference Guide Waldorf Pulse Dear Customer Thank you for purchasing our PULSation soundware for Waldorf Pulse and Waldorf Pulse+ instruments. We
More informationPHYSICS OF MUSIC. 1.) Charles Taylor, Exploring Music (Music Library ML3805 T )
REFERENCES: 1.) Charles Taylor, Exploring Music (Music Library ML3805 T225 1992) 2.) Juan Roederer, Physics and Psychophysics of Music (Music Library ML3805 R74 1995) 3.) Physics of Sound, writeup in this
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 informationCSC475 Music Information Retrieval
CSC475 Music Information Retrieval Symbolic Music Representations George Tzanetakis University of Victoria 2014 G. Tzanetakis 1 / 30 Table of Contents I 1 Western Common Music Notation 2 Digital Formats
More informationMusic Alignment and Applications. Introduction
Music Alignment and Applications Roger B. Dannenberg Schools of Computer Science, Art, and Music Introduction Music information comes in many forms Digital Audio Multi-track Audio Music Notation MIDI Structured
More informationSMS Composer and SMS Conductor: Applications for Spectral Modeling Synthesis Composition and Performance
SMS Composer and SMS Conductor: Applications for Spectral Modeling Synthesis Composition and Performance Eduard Resina Audiovisual Institute, Pompeu Fabra University Rambla 31, 08002 Barcelona, Spain eduard@iua.upf.es
More informationStepSequencer64 J74 Page 1. J74 StepSequencer64. A tool for creative sequence programming in Ableton Live. User Manual
StepSequencer64 J74 Page 1 J74 StepSequencer64 A tool for creative sequence programming in Ableton Live User Manual StepSequencer64 J74 Page 2 How to Install the J74 StepSequencer64 devices J74 StepSequencer64
More informationDevices I have known and loved
66 l Print this article Devices I have known and loved Joel Chadabe Albany, New York, USA joel@emf.org Do performing devices match performance requirements? Whenever we work with an electronic music system,
More informationPolyend Poly Polyphonic MIDI to CV Converter User Manual
Polyend Poly Polyphonic MIDI to CV Converter User Manual Made in Poland polyend.com Polyend Poly Polyphonic MIDI to CV Converter in the Eurorack format Poly is probably the easiest entry point for exploring
More informationMusic Information Retrieval
Music Information Retrieval Informative Experiences in Computation and the Archive David De Roure @dder David De Roure @dder Four quadrants Big Data Scientific Computing Machine Learning Automation More
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 informationGus (Guangyu) Xia , NYU Shanghai, Shanghai, Tel: (412) Webpage:
Gus (Guangyu) Xia 1162-2, NYU Shanghai, Shanghai, 200122 Email: gxia@nyu.edu Tel: (412)-979-0662 Webpage: http://www.cs.cmu.edu/~gxia/ EDUCATION May 2010 Aug 2016 Aug 2006 Jul 2010 Aug 2004 Jul 2010 Carnegie
More informationImprovised Duet Interaction: Learning Improvisation Techniques for Automatic Accompaniment
Improvised Duet Interaction: Learning Improvisation Techniques for Automatic Accompaniment Gus G. Xia Dartmouth College Neukom Institute Hanover, NH, USA gxia@dartmouth.edu Roger B. Dannenberg Carnegie
More informationBuilding a Better Bach with Markov Chains
Building a Better Bach with Markov Chains CS701 Implementation Project, Timothy Crocker December 18, 2015 1 Abstract For my implementation project, I explored the field of algorithmic music composition
More informationThe best next note. Work in progress on audio-interactive computer music. Version 1.0. Teun de Lange Table of contents
The best next note Work in progress on audio-interactive computer music Version 1.0 Teun de Lange 20-11-2013 Table of contents 0 Introduction... 3 1 Interactive music... 4 1.1 Concept and existing examples...
More informationWHAT MAKES FOR A HIT POP SONG? WHAT MAKES FOR A POP SONG?
WHAT MAKES FOR A HIT POP SONG? WHAT MAKES FOR A POP SONG? NICHOLAS BORG AND GEORGE HOKKANEN Abstract. The possibility of a hit song prediction algorithm is both academically interesting and industry motivated.
More informationIgaluk To Scare the Moon with its own Shadow Technical requirements
1 Igaluk To Scare the Moon with its own Shadow Technical requirements Piece for solo performer playing live electronics. Composed in a polyphonic way, the piece gives the performer control over multiple
More informationMusic Representations
Lecture Music Processing Music Representations Meinard Müller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de Book: Fundamentals of Music Processing Meinard Müller Fundamentals
More informationA PERPLEXITY BASED COVER SONG MATCHING SYSTEM FOR SHORT LENGTH QUERIES
12th International Society for Music Information Retrieval Conference (ISMIR 2011) A PERPLEXITY BASED COVER SONG MATCHING SYSTEM FOR SHORT LENGTH QUERIES Erdem Unal 1 Elaine Chew 2 Panayiotis Georgiou
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 informationHINSDALE MUSIC CURRICULUM
HINSDALE MUSIC CURRICULUM GRADE LEVEL: 9-12 STANDARD: 1. Sing, alone and with others, a varied repertoire of music. Knowledge & Skills Suggested Activities Suggested Resources & Materials a. sing with
More informationjsymbolic and ELVIS Cory McKay Marianopolis College Montreal, Canada
jsymbolic and ELVIS Cory McKay Marianopolis College Montreal, Canada What is jsymbolic? Software that extracts statistical descriptors (called features ) from symbolic music files Can read: MIDI MEI (soon)
More information// K4815 // Pattern Generator. User Manual. Hardware Version D-F Firmware Version 1.2x February 5, 2013 Kilpatrick Audio
// K4815 // Pattern Generator Kilpatrick Audio // K4815 // Pattern Generator 2p Introduction Welcome to the wonderful world of the K4815 Pattern Generator. The K4815 is a unique and flexible way of generating
More informationMusic Curriculum. Rationale. Grades 1 8
Music Curriculum Rationale Grades 1 8 Studying music remains a vital part of a student s total education. Music provides an opportunity for growth by expanding a student s world, discovering musical expression,
More informationNodal. GENERATIVE MUSIC SOFTWARE Nodal 1.9 Manual
Nodal GENERATIVE MUSIC SOFTWARE Nodal 1.9 Manual Copyright 2013 Centre for Electronic Media Art, Monash University, 900 Dandenong Road, Caulfield East 3145, Australia. All rights reserved. Introduction
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 informationShifty Manual v1.00. Shifty. Voice Allocator / Hocketing Controller / Analog Shift Register
Shifty Manual v1.00 Shifty Voice Allocator / Hocketing Controller / Analog Shift Register Table of Contents Table of Contents Overview Features Installation Before Your Start Installing Your Module Front
More informationON IMPROVISING. Index. Introduction
ON IMPROVISING Index Introduction - 1 Scales, Intervals & Chords - 2 Constructing Basic Chords - 3 Construct Basic chords - 3 Cycle of Fifth's & Chord Progression - 4 Improvising - 4 Copying Recorded Improvisations
More informationMATLAB & Image Processing (Summer Training Program) 4 Weeks/ 30 Days
(Summer Training Program) 4 Weeks/ 30 Days PRESENTED BY RoboSpecies Technologies Pvt. Ltd. Office: D-66, First Floor, Sector- 07, Noida, UP Contact us: Email: stp@robospecies.com Website: www.robospecies.com
More informationBeethoven, Bach, and Billions of Bytes
Lecture Music Processing Beethoven, Bach, and Billions of Bytes New Alliances between Music and Computer Science Meinard Müller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de
More informationMY APPROACH TO STUDYING JAZZ & IMPROVISATION. Wim Dijkgraaf 2014 v1.2
MY APPROACH TO STUDYING JAZZ & IMPROVISATION Wim Dijkgraaf 2014 v1.2 What music is to me interaction What jazz is to me interaction You will sound like what you have studied and mastered the music you
More informationCurriculum Standard One: The student will listen to and analyze music critically, using the vocabulary and language of music.
Curriculum Standard One: The student will listen to and analyze music critically, using the vocabulary and language of music. 1. The student will analyze the uses of elements of music. A. Can the student
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 informationConcepts and Theory Overview of Music Theories p. 3 The Representation of Music p. 7 Types of Representation p. 7 Symbolic Representation of Music p.
Concepts and Theory Overview of Music Theories p. 3 The Representation of Music p. 7 Types of Representation p. 7 Symbolic Representation of Music p. 9 Electronic Scores p. 10 MIDI p. 13 Musical Representation
More informationPitfalls and Windfalls in Corpus Studies of Pop/Rock Music
Introduction Hello, my talk today is about corpus studies of pop/rock music specifically, the benefits or windfalls of this type of work as well as some of the problems. I call these problems pitfalls
More informationy POWER USER MUSIC PRODUCTION and PERFORMANCE With the MOTIF ES Mastering the Sample SLICE function
y POWER USER MUSIC PRODUCTION and PERFORMANCE With the MOTIF ES Mastering the Sample SLICE function Phil Clendeninn Senior Product Specialist Technology Products Yamaha Corporation of America Working with
More informationA STATISTICAL VIEW ON THE EXPRESSIVE TIMING OF PIANO ROLLED CHORDS
A STATISTICAL VIEW ON THE EXPRESSIVE TIMING OF PIANO ROLLED CHORDS Mutian Fu 1 Guangyu Xia 2 Roger Dannenberg 2 Larry Wasserman 2 1 School of Music, Carnegie Mellon University, USA 2 School of Computer
More informationGreeley-Evans School District 6 High School Vocal Music Curriculum Guide Unit: Men s and Women s Choir Year 1 Enduring Concept: Expression of Music
Unit: Men s and Women s Choir Year 1 Enduring Concept: Expression of Music To perform music accurately and expressively demonstrating self-evaluation and personal interpretation at the minimal level of
More informationRhythmic Dissonance: Introduction
The Concept Rhythmic Dissonance: Introduction One of the more difficult things for a singer to do is to maintain dissonance when singing. Because the ear is searching for consonance, singing a B natural
More informationTowards the tangible: microtonal scale exploration in Central-African music
Towards the tangible: microtonal scale exploration in Central-African music Olmo.Cornelis@hogent.be, Joren.Six@hogent.be School of Arts - University College Ghent - BELGIUM Abstract This lecture presents
More informationMusic Composition with Interactive Evolutionary Computation
Music Composition with Interactive Evolutionary Computation Nao Tokui. Department of Information and Communication Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan. e-mail:
More informationKÜNSTLICHE INTELLIGENZ ALS PERSONALISIERTER KOMPONIST AUTOMATISCHE MUSIKERZEUGUNG ALS DAS ENDE DER TANTIEMEN?
FUTURE MUSIC CAMP 2018 PETER KNEES KÜNSTLICHE INTELLIGENZ ALS PERSONALISIERTER KOMPONIST AUTOMATISCHE MUSIKERZEUGUNG ALS DAS ENDE DER TANTIEMEN? PETER KNEES (TU WIEN) FMC 2018 ABOUT ME Music Information
More informationA Bayesian Network for Real-Time Musical Accompaniment
A Bayesian Network for Real-Time Musical Accompaniment Christopher Raphael Department of Mathematics and Statistics, University of Massachusetts at Amherst, Amherst, MA 01003-4515, raphael~math.umass.edu
More informationImplications of Ad Hoc Artificial Intelligence in Music
Implications of Ad Hoc Artificial Intelligence in Music Evan X. Merz San Jose State University Department of Computer Science 1 Washington Square San Jose, CA. 95192. evan.merz@sjsu.edu Abstract This paper
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 informationPaperTonnetz: Supporting Music Composition with Interactive Paper
PaperTonnetz: Supporting Music Composition with Interactive Paper Jérémie Garcia, Louis Bigo, Antoine Spicher, Wendy E. Mackay To cite this version: Jérémie Garcia, Louis Bigo, Antoine Spicher, Wendy E.
More informationTECHNOLOGY FOR USE IN THE LESSON ROOM AND REHEARSAL ROOM. Dr. Brad Meyer Director of Percussion Studies Stephen F. Austin State University
TECHNOLOGY FOR USE IN THE LESSON ROOM AND REHEARSAL ROOM Dr. Brad Meyer Director of Percussion Studies Stephen F. Austin State University EMAIL: meyerbe@sfasu.edu WEBSITE: www.brad-meyer.com TUNERS: TonalEnergy
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 informationExperiment: FPGA Design with Verilog (Part 4)
Department of Electrical & Electronic Engineering 2 nd Year Laboratory Experiment: FPGA Design with Verilog (Part 4) 1.0 Putting everything together PART 4 Real-time Audio Signal Processing In this part
More informationThe Million Song Dataset
The Million Song Dataset AUDIO FEATURES The Million Song Dataset There is no data like more data Bob Mercer of IBM (1985). T. Bertin-Mahieux, D.P.W. Ellis, B. Whitman, P. Lamere, The Million Song Dataset,
More informationMUSIC AND SONIC ARTS MUSIC AND SONIC ARTS MUSIC AND SONIC ARTS CAREER AND PROGRAM DESCRIPTION
MUSIC AND SONIC ARTS Cascade Campus Moriarty Arts and Humanities Building (MAHB), Room 210 971-722-5226 or 971-722-50 pcc.edu/programs/music-and-sonic-arts/ CAREER AND PROGRAM DESCRIPTION The Music & Sonic
More informationDesign considerations for technology to support music improvisation
Design considerations for technology to support music improvisation Bryan Pardo 3-323 Ford Engineering Design Center Northwestern University 2133 Sheridan Road Evanston, IL 60208 pardo@northwestern.edu
More informationInteracting with a Virtual Conductor
Interacting with a Virtual Conductor Pieter Bos, Dennis Reidsma, Zsófia Ruttkay, Anton Nijholt HMI, Dept. of CS, University of Twente, PO Box 217, 7500AE Enschede, The Netherlands anijholt@ewi.utwente.nl
More informationJASON FREEMAN THE LOCUST TREE IN FLOWER AN INTERACTIVE, MULTIMEDIA INSTALLATION BASED ON A TEXT BY WILLIAM CARLOS WILLIAMS
JASON FREEMAN THE LOCUST TREE IN FLOWER AN INTERACTIVE, MULTIMEDIA INSTALLATION BASED ON A TEXT BY WILLIAM CARLOS WILLIAMS INTRODUCTION The Locust Tree in Flower is an interactive multimedia installation
More informationPalestrina Pal: A Grammar Checker for Music Compositions in the Style of Palestrina
Palestrina Pal: A Grammar Checker for Music Compositions in the Style of Palestrina 1. Research Team Project Leader: Undergraduate Students: Prof. Elaine Chew, Industrial Systems Engineering Anna Huang,
More informationmood into an adequate input for our procedural music generation system, a scientific classification system is needed. One of the most prominent classi
Received, 201 ; Accepted, 201 Markov Chain Based Procedural Music Generator with User Chosen Mood Compatibility Adhika Sigit Ramanto Institut Teknologi Bandung Jl. Ganesha No. 10, Bandung 13512060@std.stei.itb.ac.id
More informationDistributed Virtual Music Orchestra
Distributed Virtual Music Orchestra DMITRY VAZHENIN, ALEXANDER VAZHENIN Computer Software Department University of Aizu Tsuruga, Ikki-mach, AizuWakamatsu, Fukushima, 965-8580, JAPAN Abstract: - We present
More informationGame of Life music. Chapter 1. Eduardo R. Miranda and Alexis Kirke
Contents 1 Game of Life music.......................................... 1 Eduardo R. Miranda and Alexis Kirke 1.1 A brief introduction to GoL................................. 2 1.2 Rending musical forms
More informationMusic 209 Advanced Topics in Computer Music Lecture 1 Introduction
Music 209 Advanced Topics in Computer Music Lecture 1 Introduction 2006-1-19 Professor David Wessel (with John Lazzaro) (cnmat.berkeley.edu/~wessel, www.cs.berkeley.edu/~lazzaro) Website: Coming Soon...
More informationThird Grade Music Curriculum
Third Grade Music Curriculum 3 rd Grade Music Overview Course Description The third-grade music course introduces students to elements of harmony, traditional music notation, and instrument families. The
More informationKeyboard Theory and Piano Technique
Keyboard Theory and Piano Technique Copyright Longbow Publishing Ltd. 2008 PRINTED IN CANADA First printing, September 2008 ALL RIGHTS RESERVED. No part of this work may be reproduced or used in any form
More informationMusic Processing Introduction Meinard Müller
Lecture Music Processing Introduction Meinard Müller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de Music Music Information Retrieval (MIR) Sheet Music (Image) CD / MP3
More informationConstructive Adaptive User Interfaces Composing Music Based on Human Feelings
From: AAAI02 Proceedings. Copyright 2002, AAAI (www.aaai.org). All rights reserved. Constructive Adaptive User Interfaces Composing Music Based on Human Feelings Masayuki Numao, Shoichi Takagi, and Keisuke
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 informationArticulation Guide. Berlin Brass - French Horn SFX.
Guide Berlin Brass - French Horn SFX 1 www.orchestraltools.com CONTENT I About this Guide 2 II Introduction 3 III Recording and Concept 4 IV Berlin Series 5 1 Berlin Brass - French Horn SFX... 6 Instruments...
More informationMusic Genre Classification and Variance Comparison on Number of Genres
Music Genre Classification and Variance Comparison on Number of Genres Miguel Francisco, miguelf@stanford.edu Dong Myung Kim, dmk8265@stanford.edu 1 Abstract In this project we apply machine learning techniques
More informationAudio. Meinard Müller. Beethoven, Bach, and Billions of Bytes. International Audio Laboratories Erlangen. International Audio Laboratories Erlangen
Meinard Müller Beethoven, Bach, and Billions of Bytes When Music meets Computer Science Meinard Müller International Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de School of Mathematics University
More informationANNOTATING MUSICAL SCORES IN ENP
ANNOTATING MUSICAL SCORES IN ENP Mika Kuuskankare Department of Doctoral Studies in Musical Performance and Research Sibelius Academy Finland mkuuskan@siba.fi Mikael Laurson Centre for Music and Technology
More informationMusic Understanding by Computer 1
Music Understanding by Computer 1 Roger B. Dannenberg ABSTRACT Although computer systems have found widespread application in music production, there remains a gap between the characteristicly precise
More informationProceedings of the 7th WSEAS International Conference on Acoustics & Music: Theory & Applications, Cavtat, Croatia, June 13-15, 2006 (pp54-59)
Common-tone Relationships Constructed Among Scales Tuned in Simple Ratios of the Harmonic Series and Expressed as Values in Cents of Twelve-tone Equal Temperament PETER LUCAS HULEN Department of Music
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 informationCurriculum Development In the Fairfield Public Schools FAIRFIELD PUBLIC SCHOOLS FAIRFIELD, CONNECTICUT MUSIC THEORY I
Curriculum Development In the Fairfield Public Schools FAIRFIELD PUBLIC SCHOOLS FAIRFIELD, CONNECTICUT MUSIC THEORY I Board of Education Approved 04/24/2007 MUSIC THEORY I Statement of Purpose Music is
More informationTongArk: a Human-Machine Ensemble
TongArk: a Human-Machine Ensemble Prof. Alexey Krasnoskulov, PhD. Department of Sound Engineering and Information Technologies, Piano Department Rostov State Rakhmaninov Conservatoire, Russia e-mail: avk@soundworlds.net
More informationGarageBand for the ipad, A Superstar for the Music Classroom
GarageBand for the ipad, A Superstar for the Music Classroom Floyd Richmond University of Valley Forge frichmond@valleyforge.edu Texas Music Educators Association (TMEA) TI:ME National Conference San Antonio,
More informationQUALITY OF COMPUTER MUSIC USING MIDI LANGUAGE FOR DIGITAL MUSIC ARRANGEMENT
QUALITY OF COMPUTER MUSIC USING MIDI LANGUAGE FOR DIGITAL MUSIC ARRANGEMENT Pandan Pareanom Purwacandra 1, Ferry Wahyu Wibowo 2 Informatics Engineering, STMIK AMIKOM Yogyakarta 1 pandanharmony@gmail.com,
More informationFrankenstein: a Framework for musical improvisation. Davide Morelli
Frankenstein: a Framework for musical improvisation Davide Morelli 24.05.06 summary what is the frankenstein framework? step1: using Genetic Algorithms step2: using Graphs and probability matrices step3:
More informationTMEA "12 Essential ipad Apps for ANY Musician"
TMEA "12 Essential ipad Apps for ANY Musician" Professionals Educators Students Presented by TMEA "12 Essential ipad Apps for ANY Musician" Professionals Educators Students ipad Transforming How Musicians
More informationSmart Pianist Manual
The Smart Pianist is a special app for smart devices, providing various music-related functions when connected with compatible musical instruments. NOTICE When you activate Smart Pianist while the instrument
More informationSTRATFORD PUBLIC SCHOOLS Music Department AP Music Theory
HIGH SCHOOL Rhythm/Meter Major Scales/Key Signatures Intervals Minor Scales/Key Signatures Triads, Chord Inversions and Chord Symbols STRATFORD PUBLIC SCHOOLS Perform rhythmic patterns and phrases. Compose
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 information