Beethoven, Bach, and Billions of Bytes

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Beethoven, Bach, and Billions of Bytes"

Transcription

1 Lecture Music Processing Beethoven, Bach, and Billions of Bytes New Alliances between Music and Computer Science Meinard Müller International Audio Laboratories Erlangen

2 Music

3 Music Processing Sheet Music (Image) CD / MP3 (Audio) MusicXML (Text) Dance / Motion (Mocap) Music MIDI Singing / Voice (Audio) Music Film (Video) Music Literature (Text)

4 Research Goals Music Information Retrieval (MIR) ISMIR Analysis of music signals (harmonic, melodic, rhythmic, motivic aspects) Design of musically relevant audio features Tools for multimodal search and interaction

5 Piano Roll Representation

6 Player Piano (1900)

7 Piano Roll Representation (MIDI) J.S. Bach, C-Major Fuge (Well Tempered Piano, BWV 846) Time Pitch

8 Piano Roll Representation (MIDI) Query: Goal: Find all occurrences of the query

9 Piano Roll Representation (MIDI) Query: Goal: Find all occurrences of the query Matches:

10 Audio Data Various interpretations Beethoven s Fifth Bernstein Karajan Scherbakov (piano) MIDI (piano)

11 Audio Data (Memory Requirements) 1 Bit = 1: on 0: off 1 Byte = 8 Bits 1 Kilobyte (KB) = 1 Thousand Bytes 1 Megabyte (MB) = 1 Million Bytes 1 Gigabyte (GB) = 1 Billion Bytes 1 Terabyte (TB) = 1000 Billion Bytes

12 Audio Data (Memory Requirements) MIDI files < 350 MB One audio CD 650 MB Two audio CDs > 1 Billion Bytes 1000 audio CDs Billions of Bytes

13 Music Synchronization: Audio-Audio Beethoven s Fifth

14 Music Synchronization: Audio-Audio Beethoven s Fifth Orchester (Karajan) Piano (Scherbakov) Time (seconds)

15 Music Synchronization: Audio-Audio Beethoven s Fifth Orchester (Karajan) Piano (Scherbakov) Time (seconds)

16 Application: Interpretation Switcher

17 Music Synchronization: Image-Audio Audio Image

18 Music Synchronization: Image-Audio Audio Image

19 How to make the data comparable? Audio Image

20 How to make the data comparable? Image Processing: Optical Music Recognition Audio Image

21 How to make the data comparable? Image Processing: Optical Music Recognition Audio Image Audio Processing: Fourier Analyse

22 How to make the data comparable? Image Processing: Optical Music Recognition Audio Image Audio Processing: Fourier Analyse

23 Application: Score Viewer

24 Music Processing Coarse Level What do different versions have in common? Fine Level What are the characteristics of a specific version?

25 Music Processing Coarse Level What do different versions have in common? What makes up a piece of music? Fine Level What are the characteristics of a specific version? What makes music come alive?

26 Music Processing Coarse Level What do different versions have in common? What makes up a piece of music? Identify despite of differences Fine Level What are the characteristics of a specific version? What makes music come alive? Identify the differences

27 Music Processing Coarse Level What do different versions have in common? What makes up a piece of music? Identify despite of differences Example tasks: Audio Matching Cover Song Identification Fine Level What are the characteristics of a specific version? What makes music come alive? Identify the differences Example tasks: Tempo Estimation Performance Analysis

28 Performance Analysis Schumann: Träumerei Performance: Time (seconds)

29 Performance Analysis Schumann: Träumerei Score (reference): Performance: Time (seconds)

30 Performance Analysis Schumann: Träumerei Score (reference): Strategy: Compute score-audio synchronization and derive tempo curve Performance: Time (seconds)

31 Performance Analysis Schumann: Träumerei Score (reference): Tempo Curve: Musical tempo (BPM) Musical time (measures)

32 Performance Analysis Schumann: Träumerei Score (reference): Tempo Curves: Musical tempo (BPM) Musical time (measures)

33 Performance Analysis Schumann: Träumerei Score (reference): Tempo Curves: Musical tempo (BPM) Musical time (measures)

34 Performance Analysis Schumann: Träumerei Score (reference): Tempo Curves: Musical tempo (BPM)? Musical time (measures)

35 Performance Analysis Schumann: Träumerei What can be done if no reference is available? Tempo Curves: Musical tempo (BPM) Musical time (measures)

36 Music Processing Relative Given: Several versions Absolute Given: One version

37 Music Processing Relative Given: Several versions Comparison of extracted parameters Absolute Given: One version Direct interpretation of extracted parameters

38 Music Processing Relative Given: Several versions Comparison of extracted parameters Extraction errors have often no consequence on final result Absolute Given: One version Direct interpretation of extracted parameters Extraction errors immediately become evident

39 Music Processing Relative Given: Several versions Comparison of extracted parameters Extraction errors have often no consequence on final result Example tasks: Music Synchronization Genre Classification Absolute Given: One version Direct interpretation of extracted parameters Extraction errors immediately become evident Example tasks: Music Transcription Tempo Estimation

40 Tempo Estimation and Beat Tracking Basic task: Tapping the foot when listening to music

41 Tempo Estimation and Beat Tracking Basic task: Tapping the foot when listening to music Example: Queen Another One Bites The Dust Time (seconds)

42 Tempo Estimation and Beat Tracking Basic task: Tapping the foot when listening to music Example: Queen Another One Bites The Dust Time (seconds)

43 Tempo Estimation and Beat Tracking Example: Happy Birthday to you Pulse level: Measure

44 Tempo Estimation and Beat Tracking Example: Happy Birthday to you Pulse level: Tactus (beat)

45 Tempo Estimation and Beat Tracking Example: Happy Birthday to you Pulse level: Tatum (temporal atom)

46 Tempo Estimation and Beat Tracking Example: Chopin Mazurka Op Pulse level: Quarter note Tempo:???

47 Tempo Estimation and Beat Tracking Example: Chopin Mazurka Op Pulse level: Quarter note Tempo: BPM Tempo curve Tempo (BPM) Time (beats)

48 Tempo Estimation and Beat Tracking Which temporal level? Local tempo deviations Sparse information (e.g., only note onsets available) Vague information (e.g., extracted note onsets corrupt)

49 Tempo Estimation and Beat Tracking Spectrogram Steps: 1. Spectrogram Frequency (Hz) Time (seconds)

50 Tempo Estimation and Beat Tracking Compressed Spectrogram Steps: 1. Spectrogram 2. Log Compression Frequency (Hz) Time (seconds)

51 Tempo Estimation and Beat Tracking Difference Spectrogram Steps: 1. Spectrogram 2. Log Compression 3. Differentiation Frequency (Hz) Time (seconds)

52 Tempo Estimation and Beat Tracking Steps: 1. Spectrogram 2. Log Compression 3. Differentiation 4. Accumulation Novelty Curve Time (seconds)

53 Tempo Estimation and Beat Tracking Steps: 1. Spectrogram 2. Log Compression 3. Differentiation 4. Accumulation Novelty Curve Local Average Time (seconds)

54 Tempo Estimation and Beat Tracking Steps: 1. Spectrogram 2. Log Compression 3. Differentiation 4. Accumulation 5. Normalization Novelty Curve Time (seconds)

55 Tempo Estimation and Beat Tracking Tempo (BPM) Intensity

56 Tempo Estimation and Beat Tracking Tempo (BPM) Intensity

57 Tempo Estimation and Beat Tracking Tempo (BPM) Intensity

58 Tempo Estimation and Beat Tracking Tempo (BPM) Intensity

59 Tempo Estimation and Beat Tracking Tempo (BPM) Intensity Time (seconds)

60 Tempo Estimation and Beat Tracking Novelty Curve Predominant Local Pulse (PLP) Time (seconds)

61 Tempo Estimation and Beat Tracking Light effects Music recommendation DJ Audio editing

62 Motivic Similarity Beethoven s Fifth (1st Mov.)

63 Motivic Similarity Beethoven s Fifth (1st Mov.) Beethoven s Fifth (3rd Mov.)

64 Motivic Similarity Beethoven s Fifth (1st Mov.) Beethoven s Fifth (3rd Mov.) Beethoven s Appassionata

65 Motivic Similarity

66 Motivic Similarity B A C H

67 Book Project A First Course on Music Processing Textbook (approx. 500 pages) 1. Music Representations 2. Fourier Analysis of Signals 3. Music Synchronization 4. Music Structure Analysis 5. Chord Recogntion 6. Temo and Beat Tracking 7. Content-based Audio Retrieval 8. Music Transcription To appear (plan): End of 2015

Tempo and Beat Analysis

Tempo 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 information

Music Representations

Music Representations Advanced Course Computer Science Music Processing Summer Term 00 Music Representations Meinard Müller Saarland University and MPI Informatik meinard@mpi-inf.mpg.de Music Representations Music Representations

More information

TOWARDS AUTOMATED EXTRACTION OF TEMPO PARAMETERS FROM EXPRESSIVE MUSIC RECORDINGS

TOWARDS AUTOMATED EXTRACTION OF TEMPO PARAMETERS FROM EXPRESSIVE MUSIC RECORDINGS th International Society for Music Information Retrieval Conference (ISMIR 9) TOWARDS AUTOMATED EXTRACTION OF TEMPO PARAMETERS FROM EXPRESSIVE MUSIC RECORDINGS Meinard Müller, Verena Konz, Andi Scharfstein

More information

Tool-based Identification of Melodic Patterns in MusicXML Documents

Tool-based Identification of Melodic Patterns in MusicXML Documents Tool-based Identification of Melodic Patterns in MusicXML Documents Manuel Burghardt (manuel.burghardt@ur.de), Lukas Lamm (lukas.lamm@stud.uni-regensburg.de), David Lechler (david.lechler@stud.uni-regensburg.de),

More information

Characteristics of Polyphonic Music Style and Markov Model of Pitch-Class Intervals

Characteristics of Polyphonic Music Style and Markov Model of Pitch-Class Intervals Characteristics of Polyphonic Music Style and Markov Model of Pitch-Class Intervals Eita Nakamura and Shinji Takaki National Institute of Informatics, Tokyo 101-8430, Japan eita.nakamura@gmail.com, takaki@nii.ac.jp

More information

Music Information Retrieval. Juan P Bello

Music Information Retrieval. Juan P Bello Music Information Retrieval Juan P Bello What is MIR? Imagine a world where you walk up to a computer and sing the song fragment that has been plaguing you since breakfast. The computer accepts your off-key

More information

BRAIN BEATS: TEMPO EXTRACTION FROM EEG DATA

BRAIN BEATS: TEMPO EXTRACTION FROM EEG DATA BRAIN BEATS: TEMPO EXTRACTION FROM EEG DATA Sebastian Stober 1 Thomas Prätzlich 2 Meinard Müller 2 1 Research Focus Cognititive Sciences, University of Potsdam, Germany 2 International Audio Laboratories

More information

Writing Assignment #1 Due Today. Lab#1 is tomorrow (8am) Analog vs. digital information. Digitization

Writing Assignment #1 Due Today. Lab#1 is tomorrow (8am) Analog vs. digital information. Digitization Overview of Computer Science CSC 101 Summer 2011 Analog, Binary and Digital Concepts Digitization iti Lecture 4 July 11, 2011 Announcements Writing Assignment #1 Due Today. Hand it to me after class if

More information

Algorithms for melody search and transcription. Antti Laaksonen

Algorithms for melody search and transcription. Antti Laaksonen Department of Computer Science Series of Publications A Report A-2015-5 Algorithms for melody search and transcription Antti Laaksonen To be presented, with the permission of the Faculty of Science of

More information

Searching for Similar Phrases in Music Audio

Searching for Similar Phrases in Music Audio Searching for Similar Phrases in Music udio an Ellis Laboratory for Recognition and Organization of Speech and udio ept. Electrical Engineering, olumbia University, NY US http://labrosa.ee.columbia.edu/

More information

DAY 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 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 information

Course Overview. Assessments What are the essential elements and. aptitude and aural acuity? meaning and expression in music?

Course Overview. Assessments What are the essential elements and. aptitude and aural acuity? meaning and expression in music? BEGINNING PIANO / KEYBOARD CLASS This class is open to all students in grades 9-12 who wish to acquire basic piano skills. It is appropriate for students in band, orchestra, and chorus as well as the non-performing

More information

Statistical Modeling and Retrieval of Polyphonic Music

Statistical Modeling and Retrieval of Polyphonic Music Statistical Modeling and Retrieval of Polyphonic Music Erdem Unal Panayiotis G. Georgiou and Shrikanth S. Narayanan Speech Analysis and Interpretation Laboratory University of Southern California Los Angeles,

More information

Example 1 (W.A. Mozart, Piano Trio, K. 542/iii, mm ):

Example 1 (W.A. Mozart, Piano Trio, K. 542/iii, mm ): Lesson MMM: The Neapolitan Chord Introduction: In the lesson on mixture (Lesson LLL) we introduced the Neapolitan chord: a type of chromatic chord that is notated as a major triad built on the lowered

More information

FULL-AUTOMATIC DJ MIXING SYSTEM WITH OPTIMAL TEMPO ADJUSTMENT BASED ON MEASUREMENT FUNCTION OF USER DISCOMFORT

FULL-AUTOMATIC DJ MIXING SYSTEM WITH OPTIMAL TEMPO ADJUSTMENT BASED ON MEASUREMENT FUNCTION OF USER DISCOMFORT 10th International Society for Music Information Retrieval Conference (ISMIR 2009) FULL-AUTOMATIC DJ MIXING SYSTEM WITH OPTIMAL TEMPO ADJUSTMENT BASED ON MEASUREMENT FUNCTION OF USER DISCOMFORT Hiromi

More information

AUTOMASHUPPER: AN AUTOMATIC MULTI-SONG MASHUP SYSTEM

AUTOMASHUPPER: AN AUTOMATIC MULTI-SONG MASHUP SYSTEM AUTOMASHUPPER: AN AUTOMATIC MULTI-SONG MASHUP SYSTEM Matthew E. P. Davies, Philippe Hamel, Kazuyoshi Yoshii and Masataka Goto National Institute of Advanced Industrial Science and Technology (AIST), Japan

More information

A System for Automatic Chord Transcription from Audio Using Genre-Specific Hidden Markov Models

A System for Automatic Chord Transcription from Audio Using Genre-Specific Hidden Markov Models A System for Automatic Chord Transcription from Audio Using Genre-Specific Hidden Markov Models Kyogu Lee Center for Computer Research in Music and Acoustics Stanford University, Stanford CA 94305, USA

More information

CCCS Music Mastery Skills and Knowledge for Progression

CCCS Music Mastery Skills and Knowledge for Progression Foundation (G-E/1-2) Candidates sing and/or play music with some fluency the resources used. They compose music which shows some ability to organise musical ideas and use resources in response to a brief.

More information

Mark schemes should be applied positively. Students must be rewarded for what they have shown they can do rather than penalized for omissions.

Mark schemes should be applied positively. Students must be rewarded for what they have shown they can do rather than penalized for omissions. Marking Guidance General Guidance The mark scheme specifies the number of marks available for each question, and teachers should be prepared equally to offer zero marks or full marks as appropriate. In

More information

Melody Extraction from Generic Audio Clips Thaminda Edirisooriya, Hansohl Kim, Connie Zeng

Melody Extraction from Generic Audio Clips Thaminda Edirisooriya, Hansohl Kim, Connie Zeng Melody Extraction from Generic Audio Clips Thaminda Edirisooriya, Hansohl Kim, Connie Zeng Introduction In this project we were interested in extracting the melody from generic audio files. Due to the

More information

An Empirical Comparison of Tempo Trackers

An Empirical Comparison of Tempo Trackers An Empirical Comparison of Tempo Trackers Simon Dixon Austrian Research Institute for Artificial Intelligence Schottengasse 3, A-1010 Vienna, Austria simon@oefai.at An Empirical Comparison of Tempo Trackers

More information

HS Music Theory Music

HS Music Theory Music Course theory is the field of study that deals with how music works. It examines the language and notation of music. It identifies patterns that govern composers' techniques. theory analyzes the elements

More information

UNIVERSITY OF DUBLIN TRINITY COLLEGE

UNIVERSITY 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 information

Proc. of NCC 2010, Chennai, India A Melody Detection User Interface for Polyphonic Music

Proc. of NCC 2010, Chennai, India A Melody Detection User Interface for Polyphonic Music A Melody Detection User Interface for Polyphonic Music Sachin Pant, Vishweshwara Rao, and Preeti Rao Department of Electrical Engineering Indian Institute of Technology Bombay, Mumbai 400076, India Email:

More information

Subjective Similarity of Music: Data Collection for Individuality Analysis

Subjective Similarity of Music: Data Collection for Individuality Analysis Subjective Similarity of Music: Data Collection for Individuality Analysis Shota Kawabuchi and Chiyomi Miyajima and Norihide Kitaoka and Kazuya Takeda Nagoya University, Nagoya, Japan E-mail: shota.kawabuchi@g.sp.m.is.nagoya-u.ac.jp

More information

Extracting Significant Patterns from Musical Strings: Some Interesting Problems.

Extracting Significant Patterns from Musical Strings: Some Interesting Problems. Extracting Significant Patterns from Musical Strings: Some Interesting Problems. Emilios Cambouropoulos Austrian Research Institute for Artificial Intelligence Vienna, Austria emilios@ai.univie.ac.at Abstract

More information

Using Deep Learning to Annotate Karaoke Songs

Using Deep Learning to Annotate Karaoke Songs Distributed Computing Using Deep Learning to Annotate Karaoke Songs Semester Thesis Juliette Faille faillej@student.ethz.ch Distributed Computing Group Computer Engineering and Networks Laboratory ETH

More information

Classification of Dance Music by Periodicity Patterns

Classification of Dance Music by Periodicity Patterns Classification of Dance Music by Periodicity Patterns Simon Dixon Austrian Research Institute for AI Freyung 6/6, Vienna 1010, Austria simon@oefai.at Elias Pampalk Austrian Research Institute for AI Freyung

More information

About Giovanni De Poli. What is Model. Introduction. di Poli: Methodologies for Expressive Modeling of/for Music Performance

About Giovanni De Poli. What is Model. Introduction. di Poli: Methodologies for Expressive Modeling of/for Music Performance Methodologies for Expressiveness Modeling of and for Music Performance by Giovanni De Poli Center of Computational Sonology, Department of Information Engineering, University of Padova, Padova, Italy About

More information

Content-based music retrieval

Content-based music retrieval Music retrieval 1 Music retrieval 2 Content-based music retrieval Music information retrieval (MIR) is currently an active research area See proceedings of ISMIR conference and annual MIREX evaluations

More information

Music Complexity Descriptors. Matt Stabile June 6 th, 2008

Music 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 information

Popular Music Theory Syllabus Guide

Popular Music Theory Syllabus Guide Popular Music Theory Syllabus Guide 2015-2018 www.rockschool.co.uk v1.0 Table of Contents 3 Introduction 6 Debut 9 Grade 1 12 Grade 2 15 Grade 3 18 Grade 4 21 Grade 5 24 Grade 6 27 Grade 7 30 Grade 8 33

More information

Quarterly Progress and Status Report. Is the musical retard an allusion to physical motion?

Quarterly Progress and Status Report. Is the musical retard an allusion to physical motion? Dept. for Speech, Music and Hearing Quarterly Progress and Status Report Is the musical retard an allusion to physical motion? Kronman, U. and Sundberg, J. journal: STLQPSR volume: 25 number: 23 year:

More information

6.5 Percussion scalograms and musical rhythm

6.5 Percussion scalograms and musical rhythm 6.5 Percussion scalograms and musical rhythm 237 1600 566 (a) (b) 200 FIGURE 6.8 Time-frequency analysis of a passage from the song Buenos Aires. (a) Spectrogram. (b) Zooming in on three octaves of the

More information

Music Information Retrieval for Jazz

Music Information Retrieval for Jazz Music Information Retrieval for Jazz Dan Ellis Laboratory for Recognition and Organization of Speech and Audio Dept. Electrical Eng., Columbia Univ., NY USA {dpwe,thierry}@ee.columbia.edu http://labrosa.ee.columbia.edu/

More information

Machine Learning Term Project Write-up Creating Models of Performers of Chopin Mazurkas

Machine Learning Term Project Write-up Creating Models of Performers of Chopin Mazurkas Machine Learning Term Project Write-up Creating Models of Performers of Chopin Mazurkas Marcello Herreshoff In collaboration with Craig Sapp (craig@ccrma.stanford.edu) 1 Motivation We want to generative

More information

Descending- and ascending- 5 6 sequences (sequences based on thirds and seconds):

Descending- and ascending- 5 6 sequences (sequences based on thirds and seconds): Lesson TTT Other Diatonic Sequences Introduction: In Lesson SSS we discussed the fundamentals of diatonic sequences and examined the most common type: those in which the harmonies descend by root motion

More information

Musical Examination to Bridge Audio Data and Sheet Music

Musical Examination to Bridge Audio Data and Sheet Music Musical Examination to Bridge Audio Data and Sheet Music Xunyu Pan, Timothy J. Cross, Liangliang Xiao, and Xiali Hei Department of Computer Science and Information Technologies Frostburg State University

More information

Assignment #3: Piezo Cake

Assignment #3: Piezo Cake Assignment #3: Piezo Cake Computer Science: 7 th Grade 7-CS: Introduction to Computer Science I Background In this assignment, we will learn how to make sounds by pulsing current through a piezo circuit.

More information

OBSERVED DIFFERENCES IN RHYTHM BETWEEN PERFORMANCES OF CLASSICAL AND JAZZ VIOLIN STUDENTS

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 information

City, University of London Institutional Repository

City, University of London Institutional Repository City Research Online City, University of London Institutional Repository Citation: Benetos, E., Dixon, S., Giannoulis, D., Kirchhoff, H. & Klapuri, A. (2013). Automatic music transcription: challenges

More information

Improving 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 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 information

Refined Spectral Template Models for Score Following

Refined Spectral Template Models for Score Following Refined Spectral Template Models for Score Following Filip Korzeniowski, Gerhard Widmer Department of Computational Perception, Johannes Kepler University Linz {filip.korzeniowski, gerhard.widmer}@jku.at

More information

ON FINDING MELODIC LINES IN AUDIO RECORDINGS. Matija Marolt

ON FINDING MELODIC LINES IN AUDIO RECORDINGS. Matija Marolt ON FINDING MELODIC LINES IN AUDIO RECORDINGS Matija Marolt Faculty of Computer and Information Science University of Ljubljana, Slovenia matija.marolt@fri.uni-lj.si ABSTRACT The paper presents our approach

More information

Music out of Digital Data

Music out of Digital Data 1 Teasing the Music out of Digital Data Matthias Mauch November, 2012 Me come from Unna Diplom in maths at Uni Rostock (2005) PhD at Queen Mary: Automatic Chord Transcription from Audio Using Computational

More information

Music Self Assessment Tracker

Music Self Assessment Tracker Music Self Assessment Tracker Purpose of study Music is a universal language that embodies one of the highest forms of creativity. A high-quality music education should engage and inspire pupils to develop

More information

RHYTHM EXTRACTION FROM POLYPHONIC SYMBOLIC MUSIC

RHYTHM EXTRACTION FROM POLYPHONIC SYMBOLIC MUSIC 12th International Society for Music Information Retrieval Conference (ISMIR 2011) RHYTHM EXTRACTION FROM POLYPHONIC SYMBOLIC MUSIC Florence Levé, Richard Groult, Guillaume Arnaud, Cyril Séguin MIS, Université

More information

Guide to Analysing Full Spectrum/Frequency Division Bat Calls with Audacity (v.2.0.5) by Thomas Foxley

Guide to Analysing Full Spectrum/Frequency Division Bat Calls with Audacity (v.2.0.5) by Thomas Foxley Guide to Analysing Full Spectrum/Frequency Division Bat Calls with Audacity (v.2.0.5) by Thomas Foxley Contents Getting Started Setting Up the Sound File Noise Removal Finding All the Bat Calls Call Analysis

More information

Chords not required: Incorporating horizontal and vertical aspects independently in a computer improvisation algorithm

Chords not required: Incorporating horizontal and vertical aspects independently in a computer improvisation algorithm Georgia State University ScholarWorks @ Georgia State University Music Faculty Publications School of Music 2013 Chords not required: Incorporating horizontal and vertical aspects independently in a computer

More information

SCORE-INFORMED VOICE SEPARATION FOR PIANO RECORDINGS

SCORE-INFORMED VOICE SEPARATION FOR PIANO RECORDINGS th International Society for Music Information Retrieval Conference (ISMIR ) SCORE-INFORMED VOICE SEPARATION FOR PIANO RECORDINGS Sebastian Ewert Computer Science III, University of Bonn ewerts@iai.uni-bonn.de

More information

Music Information Retrieval

Music Information Retrieval Music Information Retrieval Automatic genre classification from acoustic features DANIEL RÖNNOW and THEODOR TWETMAN Bachelor of Science Thesis Stockholm, Sweden 2012 Music Information Retrieval Automatic

More information

TEMPO AND BEAT are well-defined concepts in the PERCEPTUAL SMOOTHNESS OF TEMPO IN EXPRESSIVELY PERFORMED MUSIC

TEMPO AND BEAT are well-defined concepts in the PERCEPTUAL SMOOTHNESS OF TEMPO IN EXPRESSIVELY PERFORMED MUSIC Perceptual Smoothness of Tempo in Expressively Performed Music 195 PERCEPTUAL SMOOTHNESS OF TEMPO IN EXPRESSIVELY PERFORMED MUSIC SIMON DIXON Austrian Research Institute for Artificial Intelligence, Vienna,

More information

The influence of musical context on tempo rubato. Renee Timmers, Richard Ashley, Peter Desain, Hank Heijink

The influence of musical context on tempo rubato. Renee Timmers, Richard Ashley, Peter Desain, Hank Heijink The influence of musical context on tempo rubato Renee Timmers, Richard Ashley, Peter Desain, Hank Heijink Music, Mind, Machine group, Nijmegen Institute for Cognition and Information, University of Nijmegen,

More information

Unit 1. π π π π π π. 0 π π π π π π π π π. . 0 ð Š ² ² / Melody 1A. Melodic Dictation: Scalewise (Conjunct Diatonic) Melodies

Unit 1. π π π π π π. 0 π π π π π π π π π. . 0 ð Š ² ² / Melody 1A. Melodic Dictation: Scalewise (Conjunct Diatonic) Melodies ben36754_un01.qxd 4/8/04 22:33 Page 1 { NAME DATE SECTION Unit 1 Melody 1A Melodic Dictation: Scalewise (Conjunct Diatonic) Melodies Before beginning the exercises in this section, sing the following sample

More information

A Survey of Audio-Based Music Classification and Annotation

A Survey of Audio-Based Music Classification and Annotation A Survey of Audio-Based Music Classification and Annotation Zhouyu Fu, Guojun Lu, Kai Ming Ting, and Dengsheng Zhang IEEE Trans. on Multimedia, vol. 13, no. 2, April 2011 presenter: Yin-Tzu Lin ( 阿孜孜 ^.^)

More information

PHYSICS OF MUSIC. 1.) Charles Taylor, Exploring Music (Music Library ML3805 T )

PHYSICS 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 information

METRICAL STRENGTH AND CONTRADICTION IN TURKISH MAKAM MUSIC

METRICAL STRENGTH AND CONTRADICTION IN TURKISH MAKAM MUSIC Proc. of the nd CompMusic Workshop (Istanbul, Turkey, July -, ) METRICAL STRENGTH AND CONTRADICTION IN TURKISH MAKAM MUSIC Andre Holzapfel Music Technology Group Universitat Pompeu Fabra Barcelona, Spain

More information

Specifying Features for Classical and Non-Classical Melody Evaluation

Specifying 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 information

AP Music Theory Westhampton Beach High School Summer 2017 Review Sheet and Exercises

AP Music Theory Westhampton Beach High School Summer 2017 Review Sheet and Exercises AP Music Theory esthampton Beach High School Summer 2017 Review Sheet and Exercises elcome to AP Music Theory! Our 2017-18 class is relatively small (only 8 students at this time), but you come from a

More information

Using Musical Knowledge to Extract Expressive Performance. Information from Audio Recordings. Eric D. Scheirer. E15-401C Cambridge, MA 02140

Using Musical Knowledge to Extract Expressive Performance. Information from Audio Recordings. Eric D. Scheirer. E15-401C Cambridge, MA 02140 Using Musical Knowledge to Extract Expressive Performance Information from Audio Recordings Eric D. Scheirer MIT Media Laboratory E15-41C Cambridge, MA 214 email: eds@media.mit.edu Abstract A computer

More information

Efficient Computer-Aided Pitch Track and Note Estimation for Scientific Applications. Matthias Mauch Chris Cannam György Fazekas

Efficient Computer-Aided Pitch Track and Note Estimation for Scientific Applications. Matthias Mauch Chris Cannam György Fazekas Efficient Computer-Aided Pitch Track and Note Estimation for Scientific Applications Matthias Mauch Chris Cannam György Fazekas! 1 Matthias Mauch, Chris Cannam, George Fazekas Problem Intonation in Unaccompanied

More information

Music Understanding By Computer 1

Music Understanding By Computer 1 Music Understanding By Computer 1 Roger B. Dannenberg School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 USA Abstract Music Understanding refers to the recognition or identification

More information

Lecture 11: Chroma and Chords

Lecture 11: Chroma and Chords LN 4896 MUSI SINL PROSSIN Lecture 11: hroma and hords 1. eatures for Music udio 2. hroma eatures 3. hord Recognition an llis ept. lectrical ngineering, olumbia University dpwe@ee.columbia.edu http://www.ee.columbia.edu/~dpwe/e4896/

More information

6.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 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 information

arxiv: v1 [cs.ir] 31 Jul 2017

arxiv: v1 [cs.ir] 31 Jul 2017 LEARNING AUDIO SHEET MUSIC CORRESPONDENCES FOR SCORE IDENTIFICATION AND OFFLINE ALIGNMENT Matthias Dorfer Andreas Arzt Gerhard Widmer Department of Computational Perception, Johannes Kepler University

More information

Polyphonic Audio Matching for Score Following and Intelligent Audio Editors

Polyphonic Audio Matching for Score Following and Intelligent Audio Editors Polyphonic Audio Matching for Score Following and Intelligent Audio Editors Roger B. Dannenberg and Ning Hu School of Computer Science, Carnegie Mellon University email: dannenberg@cs.cmu.edu, ninghu@cs.cmu.edu,

More information

Music 231 Motive Development Techniques, part 1

Music 231 Motive Development Techniques, part 1 Music 231 Motive Development Techniques, part 1 Fourteen motive development techniques: New Material Part 1 (this document) * repetition * sequence * interval change * rhythm change * fragmentation * extension

More information

Smooth Rhythms as Probes of Entrainment. Music Perception 10 (1993): ABSTRACT

Smooth Rhythms as Probes of Entrainment. Music Perception 10 (1993): ABSTRACT Smooth Rhythms as Probes of Entrainment Music Perception 10 (1993): 503-508 ABSTRACT If one hypothesizes rhythmic perception as a process employing oscillatory circuits in the brain that entrain to low-frequency

More information

IMPROVING GENRE CLASSIFICATION BY COMBINATION OF AUDIO AND SYMBOLIC DESCRIPTORS USING A TRANSCRIPTION SYSTEM

IMPROVING GENRE CLASSIFICATION BY COMBINATION OF AUDIO AND SYMBOLIC DESCRIPTORS USING A TRANSCRIPTION SYSTEM IMPROVING GENRE CLASSIFICATION BY COMBINATION OF AUDIO AND SYMBOLIC DESCRIPTORS USING A TRANSCRIPTION SYSTEM Thomas Lidy, Andreas Rauber Vienna University of Technology, Austria Department of Software

More information

Singing Voice Detection for Karaoke Application

Singing Voice Detection for Karaoke Application Singing Voice Detection for Karaoke Application Arun Shenoy *, Yuansheng Wu, Ye Wang ABSTRACT We present a framework to detect the regions of singing voice in musical audio signals. This work is oriented

More information

Automatic meter extraction from MIDI files (Extraction automatique de mètres à partir de fichiers MIDI)

Automatic meter extraction from MIDI files (Extraction automatique de mètres à partir de fichiers MIDI) Journées d'informatique Musicale, 9 e édition, Marseille, 9-1 mai 00 Automatic meter extraction from MIDI files (Extraction automatique de mètres à partir de fichiers MIDI) Benoit Meudic Ircam - Centre

More information

Style-independent computer-assisted exploratory analysis of large music collections

Style-independent computer-assisted exploratory analysis of large music collections Style-independent computer-assisted exploratory analysis of large music collections Abstract Cory McKay Schulich School of Music McGill University Montreal, Quebec, Canada cory.mckay@mail.mcgill.ca The

More information

Topics in Computer Music Instrument Identification. Ioanna Karydi

Topics in Computer Music Instrument Identification. Ioanna Karydi Topics in Computer Music Instrument Identification Ioanna Karydi Presentation overview What is instrument identification? Sound attributes & Timbre Human performance The ideal algorithm Selected approaches

More information

Swing Ratios and Ensemble Timing in Jazz Performance: Evidence for a Common Rhythmic Pattern

Swing Ratios and Ensemble Timing in Jazz Performance: Evidence for a Common Rhythmic Pattern Music Perception Spring 2002, Vol. 19, No. 3, 333 349 2002 BY THE REGENTS OF THE UNIVERSITY OF CALIFORNIA ALL RIGHTS RESERVED. Swing Ratios and Ensemble Timing in Jazz Performance: Evidence for a Common

More information

Classification of Different Indian Songs Based on Fractal Analysis

Classification of Different Indian Songs Based on Fractal Analysis Classification of Different Indian Songs Based on Fractal Analysis Atin Das Naktala High School, Kolkata 700047, India Pritha Das Department of Mathematics, Bengal Engineering and Science University, Shibpur,

More information

Pattern Recognition in Music

Pattern Recognition in Music Pattern Recognition in Music SAMBA/07/02 Line Eikvil Ragnar Bang Huseby February 2002 Copyright Norsk Regnesentral NR-notat/NR Note Tittel/Title: Pattern Recognition in Music Dato/Date: February År/Year:

More information

AUDIO-BASED COVER SONG RETRIEVAL USING APPROXIMATE CHORD SEQUENCES: TESTING SHIFTS, GAPS, SWAPS AND BEATS

AUDIO-BASED COVER SONG RETRIEVAL USING APPROXIMATE CHORD SEQUENCES: TESTING SHIFTS, GAPS, SWAPS AND BEATS AUDIO-BASED COVER SONG RETRIEVAL USING APPROXIMATE CHORD SEQUENCES: TESTING SHIFTS, GAPS, SWAPS AND BEATS Juan Pablo Bello Music Technology, New York University jpbello@nyu.edu ABSTRACT This paper presents

More information

ON THE USE OF PERCEPTUAL PROPERTIES FOR MELODY ESTIMATION

ON THE USE OF PERCEPTUAL PROPERTIES FOR MELODY ESTIMATION Proc. of the 4 th Int. Conference on Digital Audio Effects (DAFx-), Paris, France, September 9-23, 2 Proc. of the 4th International Conference on Digital Audio Effects (DAFx-), Paris, France, September

More information

Multidimensional analysis of interdependence in a string quartet

Multidimensional analysis of interdependence in a string quartet International Symposium on Performance Science The Author 2013 ISBN tbc All rights reserved Multidimensional analysis of interdependence in a string quartet Panos Papiotis 1, Marco Marchini 1, and Esteban

More information

Perceptual Smoothness of Tempo in Expressively Performed Music

Perceptual Smoothness of Tempo in Expressively Performed Music Perceptual Smoothness of Tempo in Expressively Performed Music Simon Dixon Austrian Research Institute for Artificial Intelligence, Vienna, Austria Werner Goebl Austrian Research Institute for Artificial

More information

arxiv: v1 [cs.sd] 8 Jun 2016

arxiv: v1 [cs.sd] 8 Jun 2016 Symbolic Music Data Version 1. arxiv:1.5v1 [cs.sd] 8 Jun 1 Christian Walder CSIRO Data1 7 London Circuit, Canberra,, Australia. christian.walder@data1.csiro.au June 9, 1 Abstract In this document, we introduce

More information

Joint estimation of chords and downbeats from an audio signal

Joint estimation of chords and downbeats from an audio signal Joint estimation of chords and downbeats from an audio signal Hélène Papadopoulos, Geoffroy Peeters To cite this version: Hélène Papadopoulos, Geoffroy Peeters. Joint estimation of chords and downbeats

More information

COMPOSING MUSIC WITH COMPLEX NETWORKS

COMPOSING MUSIC WITH COMPLEX NETWORKS COMPOSING MUSIC WITH COMPLEX NETWORKS C. K. Michael Tse Hong Kong Polytechnic University Presented at IWCSN 2009, Bristol Acknowledgement Students Mr Xiaofan Liu, PhD student Miss Can Yang, MSc student

More information

STRUCTURAL CHANGE ON MULTIPLE TIME SCALES AS A CORRELATE OF MUSICAL COMPLEXITY

STRUCTURAL CHANGE ON MULTIPLE TIME SCALES AS A CORRELATE OF MUSICAL COMPLEXITY STRUCTURAL CHANGE ON MULTIPLE TIME SCALES AS A CORRELATE OF MUSICAL COMPLEXITY Matthias Mauch Mark Levy Last.fm, Karen House, 1 11 Bache s Street, London, N1 6DL. United Kingdom. matthias@last.fm mark@last.fm

More information

Autocorrelation in meter induction: The role of accent structure a)

Autocorrelation in meter induction: The role of accent structure a) Autocorrelation in meter induction: The role of accent structure a) Petri Toiviainen and Tuomas Eerola Department of Music, P.O. Box 35(M), 40014 University of Jyväskylä, Jyväskylä, Finland Received 16

More information

AP Music Theory COURSE OBJECTIVES STUDENT EXPECTATIONS TEXTBOOKS AND OTHER MATERIALS

AP Music Theory COURSE OBJECTIVES STUDENT EXPECTATIONS TEXTBOOKS AND OTHER MATERIALS AP Music Theory on- campus section COURSE OBJECTIVES The ultimate goal of this AP Music Theory course is to develop each student

More information

West Linn-Wilsonville School District Primary (Grades K-5) Music Curriculum. Curriculum Foundations

West Linn-Wilsonville School District Primary (Grades K-5) Music Curriculum. Curriculum Foundations Curriculum Foundations Important Ideas & Understandings Significant Strands Significant Skills to be Learned & Practiced Nature of the Human Experience Making connections creating meaning and understanding

More information

CHAPTER 4 SEGMENTATION AND FEATURE EXTRACTION

CHAPTER 4 SEGMENTATION AND FEATURE EXTRACTION 69 CHAPTER 4 SEGMENTATION AND FEATURE EXTRACTION According to the overall architecture of the system discussed in Chapter 3, we need to carry out pre-processing, segmentation and feature extraction. This

More information

Note Detection and Multiple Fundamental Frequency Estimation in Piano Recordings. Matthew Thompson

Note Detection and Multiple Fundamental Frequency Estimation in Piano Recordings. Matthew Thompson Note Detection and Multiple Fundamental Frequency Estimation in Piano Recordings by Matthew Thompson A thesis submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements

More information

Theory of Music Grade 1

Theory of Music Grade 1 Theory of Music Grade 1 May 2010 Your full name (as on appointment slip). Please use BLOCK CAPITALS. Your signature Registration number Centre Instructions to Candidates 1. The time allowed for answering

More information

MUSIR A RETRIEVAL MODEL FOR MUSIC

MUSIR A RETRIEVAL MODEL FOR MUSIC University of Tampere Department of Information Studies Research Notes RN 1998 1 PEKKA SALOSAARI & KALERVO JÄRVELIN MUSIR A RETRIEVAL MODEL FOR MUSIC Tampereen yliopisto Informaatiotutkimuksen laitos Tiedotteita

More information

Jam Sesh. Music to Your Ears, From You. Ben Dantowitz, Edward Du, Thomas Pinella, James Rutledge, and Stephen Watson

Jam Sesh. Music to Your Ears, From You. Ben Dantowitz, Edward Du, Thomas Pinella, James Rutledge, and Stephen Watson Jam Sesh Music to Your Ears, From You Ben Dantowitz, Edward Du, Thomas Pinella, James Rutledge, and Stephen Watson Jam Sesh: What is it? Inspiration an application to support individual musicians with

More information

Perceptual Evaluation of Automatically Extracted Musical Motives

Perceptual Evaluation of Automatically Extracted Musical Motives Perceptual Evaluation of Automatically Extracted Musical Motives Oriol Nieto 1, Morwaread M. Farbood 2 Dept. of Music and Performing Arts Professions, New York University, USA 1 oriol@nyu.edu, 2 mfarbood@nyu.edu

More information

EVIDENCE FOR PIANIST-SPECIFIC RUBATO STYLE IN CHOPIN NOCTURNES

EVIDENCE FOR PIANIST-SPECIFIC RUBATO STYLE IN CHOPIN NOCTURNES EVIDENCE FOR PIANIST-SPECIFIC RUBATO STYLE IN CHOPIN NOCTURNES Miguel Molina-Solana Dpt. Computer Science and AI University of Granada, Spain miguelmolina at ugr.es Maarten Grachten IPEM - Dept. of Musicology

More information

A Computational Model for Discriminating Music Performers

A Computational Model for Discriminating Music Performers A Computational Model for Discriminating Music Performers Efstathios Stamatatos Austrian Research Institute for Artificial Intelligence Schottengasse 3, A-1010 Vienna stathis@ai.univie.ac.at Abstract In

More information

A Basis for Characterizing Musical Genres

A Basis for Characterizing Musical Genres A Basis for Characterizing Musical Genres Roelof A. Ruis 6285287 Bachelor thesis Credits: 18 EC Bachelor Artificial Intelligence University of Amsterdam Faculty of Science Science Park 904 1098 XH Amsterdam

More information

Appendix A Types of Recorded Chords

Appendix A Types of Recorded Chords Appendix A Types of Recorded Chords In this appendix, detailed lists of the types of recorded chords are presented. These lists include: The conventional name of the chord [13, 15]. The intervals between

More information

The Sound of Emotion: The Effect of Performers Emotions on Auditory Performance Characteristics

The Sound of Emotion: The Effect of Performers Emotions on Auditory Performance Characteristics The Sound of Emotion: The Effect of Performers Emotions on Auditory Performance Characteristics Anemone G. W. van Zijl *1, Petri Toiviainen *2, Geoff Luck *3 * Department of Music, University of Jyväskylä,

More information

Can Song Lyrics Predict Genre? Danny Diekroeger Stanford University

Can Song Lyrics Predict Genre? Danny Diekroeger Stanford University Can Song Lyrics Predict Genre? Danny Diekroeger Stanford University danny1@stanford.edu 1. Motivation and Goal Music has long been a way for people to express their emotions. And because we all have a

More information

Singer Recognition and Modeling Singer Error

Singer Recognition and Modeling Singer Error Singer Recognition and Modeling Singer Error Johan Ismael Stanford University jismael@stanford.edu Nicholas McGee Stanford University ndmcgee@stanford.edu 1. Abstract We propose a system for recognizing

More information