Music Information Retrieval

Similar documents
SIMSSA DB: A Database for Computational Musicological Research

DAY 1. Intelligent Audio Systems: A review of the foundations and applications of semantic audio analysis and music information retrieval

Introductions to Music Information Retrieval

Explorations in linked data practice for early music

Music Information Retrieval. Juan P Bello

Music and Text: Integrating Scholarly Literature into Music Data

DAY 1. Intelligent Audio Systems: A review of the foundations and applications of semantic audio analysis and music information retrieval

Music Information Retrieval

Distributed Digital Music Archives and Libraries (DDMAL)

The Music Information Retrieval Evaluation exchange (MIREX): An Introductory Overview

TOWARD AN INTELLIGENT EDITOR FOR JAZZ MUSIC

Music Emotion Recognition. Jaesung Lee. Chung-Ang University

Audio. Meinard Müller. Beethoven, Bach, and Billions of Bytes. International Audio Laboratories Erlangen. International Audio Laboratories Erlangen

Music Information Retrieval

Content-based music retrieval

Music Radar: A Web-based Query by Humming System

The MAMI Query-By-Voice Experiment Collecting and annotating vocal queries for music information retrieval

CTP431- Music and Audio Computing Music Information Retrieval. Graduate School of Culture Technology KAIST Juhan Nam

Analysing Musical Pieces Using harmony-analyser.org Tools

Music Information Retrieval (MIR)

Music Processing Introduction Meinard Müller

Outline. Why do we classify? Audio Classification


Beethoven, Bach, and Billions of Bytes

Methodologies for Creating Symbolic Early Music Corpora for Musicological Research

Structural Analysis of Large Amounts of Music Information

Trevor de Clercq. Music Informatics Interest Group Meeting Society for Music Theory November 3, 2018 San Antonio, TX

jsymbolic 2: New Developments and Research Opportunities

A Study of Synchronization of Audio Data with Symbolic Data. Music254 Project Report Spring 2007 SongHui Chon

Ask a Librarian: The Role of Librarians in the Music Information Retrieval Community

Statistical Modeling and Retrieval of Polyphonic Music

Music Information Retrieval (MIR)

A linked research network that is Transforming Musicology

Audio Feature Extraction for Corpus Analysis

A PERPLEXITY BASED COVER SONG MATCHING SYSTEM FOR SHORT LENGTH QUERIES

Singer Recognition and Modeling Singer Error

Predicting Variation of Folk Songs: A Corpus Analysis Study on the Memorability of Melodies Janssen, B.D.; Burgoyne, J.A.; Honing, H.J.

Music Information Retrieval Community

Further Topics in MIR

DESIGN AND CREATION OF A LARGE-SCALE DATABASE OF STRUCTURAL ANNOTATIONS

Music Structure Analysis

AUTOMATED METHODS FOR ANALYZING MUSIC RECORDINGS IN SONATA FORM

Methods for the automatic structural analysis of music. Jordan B. L. Smith CIRMMT Workshop on Structural Analysis of Music 26 March 2010

Semi-automated extraction of expressive performance information from acoustic recordings of piano music. Andrew Earis

MedleyDB: A MULTITRACK DATASET FOR ANNOTATION-INTENSIVE MIR RESEARCH

Music Structure Analysis

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

Effects of acoustic degradations on cover song recognition

APPLICATIONS OF A SEMI-AUTOMATIC MELODY EXTRACTION INTERFACE FOR INDIAN MUSIC

The Million Song Dataset

In Search of the Goosebump Factor A Blueprint for Emotional Music Recommenders

Data Driven Music Understanding

Probabilist modeling of musical chord sequences for music analysis

A STATISTICAL VIEW ON THE EXPRESSIVE TIMING OF PIANO ROLLED CHORDS

Doctor of Philosophy

CALCULATING SIMILARITY OF FOLK SONG VARIANTS WITH MELODY-BASED FEATURES

Music Similarity and Cover Song Identification: The Case of Jazz

EVALUATING THE GENRE CLASSIFICATION PERFORMANCE OF LYRICAL FEATURES RELATIVE TO AUDIO, SYMBOLIC AND CULTURAL FEATURES

Automatic Labelling of tabla signals

ETHNOMUSE: ARCHIVING FOLK MUSIC AND DANCE CULTURE

TOWARDS STRUCTURAL ALIGNMENT OF FOLK SONGS

Music Representations. Beethoven, Bach, and Billions of Bytes. Music. Research Goals. Piano Roll Representation. Player Piano (1900)

Week 14 Query-by-Humming and Music Fingerprinting. Roger B. Dannenberg Professor of Computer Science, Art and Music Carnegie Mellon University

Towards the tangible: microtonal scale exploration in Central-African music

mir_eval: A TRANSPARENT IMPLEMENTATION OF COMMON MIR METRICS

Music Information Retrieval. Juan Pablo Bello MPATE-GE 2623 Music Information Retrieval New York University

Singer Traits Identification using Deep Neural Network

Music Information Retrieval

Improving Beat Tracking in the presence of highly predominant vocals using source separation techniques: Preliminary study

Music Understanding and the Future of Music

Aggregating Digital Resources for Musicology

ASSOCIATIONS BETWEEN MUSICOLOGY AND MUSIC INFORMATION RETRIEVAL

Course Report Level National 5

THE COMPOSITIONAL HIERARCHICAL MODEL FOR MUSIC INFORMATION RETRIEVAL

Beethoven, Bach und Billionen Bytes

Department Curriculum Map

A System for Acoustic Chord Transcription and Key Extraction from Audio Using Hidden Markov models Trained on Synthesized Audio

Composer Identification of Digital Audio Modeling Content Specific Features Through Markov Models

Improvised Duet Interaction: Learning Improvisation Techniques for Automatic Accompaniment

A CHROMA-BASED SALIENCE FUNCTION FOR MELODY AND BASS LINE ESTIMATION FROM MUSIC AUDIO SIGNALS

A TEXT RETRIEVAL APPROACH TO CONTENT-BASED AUDIO RETRIEVAL

MUSI-6201 Computational Music Analysis

2 2. Melody description The MPEG-7 standard distinguishes three types of attributes related to melody: the fundamental frequency LLD associated to a t

TEST SUMMARY AND FRAMEWORK TEST SUMMARY

Tool-based Identification of Melodic Patterns in MusicXML Documents

COMPARING RNN PARAMETERS FOR MELODIC SIMILARITY

Computational Models of Music Similarity. Elias Pampalk National Institute for Advanced Industrial Science and Technology (AIST)

African Music Research

Autumn. A: Plan, develop and deliver a music product B: Promote a music product C: Review the management of a music product

ESTIMATING THE ERROR DISTRIBUTION OF A TAP SEQUENCE WITHOUT GROUND TRUTH 1

TOWARDS EVALUATING MULTIPLE PREDOMINANT MELODY ANNOTATIONS IN JAZZ RECORDINGS

Computational Modelling of Harmony

Chroma-based Predominant Melody and Bass Line Extraction from Music Audio Signals

Algorithms for melody search and transcription. Antti Laaksonen

Music Alignment and Applications. Introduction

WHAT MAKES FOR A HIT POP SONG? WHAT MAKES FOR A POP SONG?

Timing In Expressive Performance

Frankenstein: a Framework for musical improvisation. Davide Morelli

Automatic music transcription

Breakscience. Technological and Musicological Research in Hardcore, Jungle, and Drum & Bass

Transcription:

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 machines Distributed Computation Conventional Digital Scholarship Social Cybersecurity Citizen Science Science 2.0 Computation Networks Web 2.0 More people @dder

Social Machines Real life is and must be full of all kinds of social constraint the very processes from which society arises. Computers can help if we use them to create abstract social machines on the Web: processes in which the people do the creative work and the machine does the administration... The stage is set for an evolutionary growth of new social engines. The ability to create new forms of social process would be given to the world at large, and development would be rapid. Berners-Lee, Weaving the Web, 1999 (pp. 172 175)

www.zooniverse.org

Defining Music Information Retrieval? n Music Information Retrieval (MIR) is the process of searching for, and finding, music objects, or parts of music objects, via a query framed musically and/or in musical terms n Music Objects: Scores, Parts, Recordings (WAV, MP3, etc.), etc. n Musically framed query: Singing, Humming, Keyboard, Notation-based, MIDI file, Sound file, etc. n Musical terms: Genre, Style, Tempo, etc.

What is MIR? n Born ca. 1960 s in IR research n Major recent growth precipitated by advent of networked digital music collections n Informed by multiple disciplines and literatures n ISMIR started in 2000

Music representation is VERY heterogeneous!

MIREX Overview n Began as MIREX in 2005 n Tasks defined by community debate n Data sets collected and/or donated n Participants submit code to IMIRSEL n Code rarely works first try J n Huge labour consumption getting programmes to work n Meet at ISMIR to discuss results n Non-consumptive research

2018 Tasks Audio Beat Tracking Audio Chord Estimation Audio Cover Song Identification Audio Downbeat Estimation Audio Fingerprinting Audio Key Detection Audio Onset Detection Audio Tempo Estimation Automatic Lyrics-to-Audio Alignment http://music-ir.org/mirex/wiki/2018:task_captains Drum Transcription Multiple Fundamental Frequency Estimation & Tracking Real-time Audio to Score Alignment (Score Following) Patterns for Prediction Set List Identification Audio Melody Extraction Music and/or Speech Detection

2017 Results

SALAMI 23,000 hours of recorded music Digital Music Collections Music Information Retrieval Community Student-sourced ground truth Community Software Supercomputer Linked Data Repositories

salami.music.mcgill.ca Jordan B. L. Smith, J. Ashley Burgoyne, Ichiro Fujinaga, David De Roure, and J. Stephen Downie. 2011. Design and creation of a large-scale database of structural annotations. In Proceedings of the International Society for Music Information Retrieval Conference, Miami, FL, 555 60

Ashley Burgoyne

The world of music has changed for good in the digital age. This revolution must be matched by a transformation of the means by which music is studied. While preserving the best traditional values and practices of musicology we must take advantage of the immense opportunities offered by music information retrieval Three parallel musicological investigations 1. 16th-century vocal and lute music 2. Wagner's leitmotifs 3. Musicology of the social media Ensure sustainability and repeatability by embedding the above research activities in a framework enabling data, methods and results to be shared permanently as Linked Data Enhance Semantic Web workflow description methods for musicology

Carolin Rindfleisch

Digital Music Objects David De Roure @dder AES, Berlin, May 2017 www.semanticaudio.ac.uk

http://www.researchobject.org/

Discussion points 1. Construction and use of the archive seen as a social machine 2. Computational methods and linked data used in Search and Discovery 3. Adding value through use 4. Increasingly working with born digital content, use of provenance

Thanks to J. Stephen Downie (Illinois), Tim Crawford (Goldsmiths), Mark Sandler (QMUL) and all our colleagues david.deroure@oerc.ox.ac.uk www.oerc.ox.ac.uk/people/dder @dder www.semanticaudio.ac.uk www.transforming-musicology.org www.sociam.org www.researchobject.org