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Acoustic Chord Transcription and Key Extraction from Audio Using Key-Dependent Hmms Trained on Synthesized Audio
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 16, NO. 2, FEBRUARY 2008 291 Acoustic Chord Transcription and Key Extraction From Audio Using Key-Dependent HMMs Trained on Synthesized Audio Kyogu Lee, Member, IEEE, and Malcolm Slaney, Senior Member, IEEE Abstract—We describe an acoustic chord transcription system Finding the key and labeling the chords automatically from that uses symbolic data to train hidden Markov models and audio are of great use for performing harmony analysis of music. gives best-of-class frame-level recognition results. We avoid the Once the harmonic content of a piece is known, we can use a extremely laborious task of human annotation of chord names and sequence of chords for further higher level structural analysis to boundaries—which must be done to provide machine learning models with ground truth—by performing automatic harmony define themes, phrases, or forms. analysis on symbolic music files. In parallel, we synthesize audio Chord sequences and the timing of chord boundaries are a from the same symbolic files and extract acoustic feature vectors compact and robust mid-level representation of musical signals; which are in perfect alignment with the labels. We, therefore, gen- they have many potential applications, such as music identifica- erate a large set of labeled training data with a minimal amount tion, music segmentation, music-similarity finding, and audio of human labor. This allows for richer models. Thus, we build 24 thumbnailing. Chord sequences have been successfully used as key-dependent HMMs, one for each key, using the key information derived from symbolic data. Each key model defines a unique a front end to an audio cover-song identification system [1]. -
Chord Names and Symbols (Popular Music) from Wikipedia, the Free Encyclopedia
Chord names and symbols (popular music) From Wikipedia, the free encyclopedia Various kinds of chord names and symbols are used in different contexts, to represent musical chords. In most genres of popular music, including jazz, pop, and rock, a chord name and the corresponding symbol are typically composed of one or more of the following parts: 1. The root note (e.g. C). CΔ7, or major seventh chord 2. The chord quality (e.g. major, maj, or M). on C Play . 3. The number of an interval (e.g. seventh, or 7), or less often its full name or symbol (e.g. major seventh, maj7, or M7). 4. The altered fifth (e.g. sharp five, or ♯5). 5. An additional interval number (e.g. add 13 or add13), in added tone chords. For instance, the name C augmented seventh, and the corresponding symbol Caug7, or C+7, are both composed of parts 1, 2, and 3. Except for the root, these parts do not refer to the notes which form the chord, but to the intervals they form with respect to the root. For instance, Caug7 indicates a chord formed by the notes C-E-G♯-B♭. The three parts of the symbol (C, aug, and 7) refer to the root C, the augmented (fifth) interval from C to G♯, and the (minor) seventh interval from C to B♭. A set of decoding rules is applied to deduce the missing information. Although they are used occasionally in classical music, these names and symbols are "universally used in jazz and popular music",[1] usually inside lead sheets, fake books, and chord charts, to specify the harmony of compositions. -
Music Theory Contents
Music theory Contents 1 Music theory 1 1.1 History of music theory ........................................ 1 1.2 Fundamentals of music ........................................ 3 1.2.1 Pitch ............................................. 3 1.2.2 Scales and modes ....................................... 4 1.2.3 Consonance and dissonance .................................. 4 1.2.4 Rhythm ............................................ 5 1.2.5 Chord ............................................. 5 1.2.6 Melody ............................................ 5 1.2.7 Harmony ........................................... 6 1.2.8 Texture ............................................ 6 1.2.9 Timbre ............................................ 6 1.2.10 Expression .......................................... 7 1.2.11 Form or structure ....................................... 7 1.2.12 Performance and style ..................................... 8 1.2.13 Music perception and cognition ................................ 8 1.2.14 Serial composition and set theory ............................... 8 1.2.15 Musical semiotics ....................................... 8 1.3 Music subjects ............................................. 8 1.3.1 Notation ............................................ 8 1.3.2 Mathematics ......................................... 8 1.3.3 Analysis ............................................ 9 1.3.4 Ear training .......................................... 9 1.4 See also ................................................ 9 1.5 Notes ................................................ -
Liveprompter Manual (En).Pdf
LivePrompter Manual 1. What is LivePrompter? LivePrompter is essentially a Teleprompter designed specifically for musicians. It displays lyrics and chords for a song on screen and automatically scrolls through the song, so that it always displays the current part of the song. Being designed for live musicians, LivePrompter incorporates some special features: Its file format is based on the “ChordPro” format, which allows very easy entry of chords in song lyrics. Also, there is already a large pool of ChordPro files available on the Web, which you can use with LivePrompter Songs can be organized in “books” (music collections) and set lists LivePrompter is very easy to operate – in live operation, you mostly need only one key (or foot switch) to control the program. The program is also built to be easily operated with touch screens or tablet PCs. Scrolling through songs can be controlled very precisely via timer markers within the song text Songs can be transposed easily via “transpose” and “capo” functions. In transposing, LivePrompter always attempts to interpret chords in a harmonically meaningful way. LivePrompter can connect via MIDI to other software or to MIDI devices in order to trigger sound changes, to set up lighting or even just to synchronize multiple LivePrompter machines The input files for LivePrompter are simple textfiles (extension “.txt”) which contain lyrics, chords, and metadata – sorry, no graphics or PDF documents, so no score display. But you can use tab or drum notation in text format. LivePrompter has been developed for the Windows platform – unfortunately no Mac, Unix, Android or iOS version at present. -
AN EXPLORATION of the KEY CHARACTERISTICS in BEETHOVEN's PIANO SONATAS and SELECTED INSTRUMENTAL REPERTOIRE Dimitri Papadimit
AN EXPLORATION OF THE KEY CHARACTERISTICS IN BEETHOVEN’S PIANO SONATAS AND SELECTED INSTRUMENTAL REPERTOIRE Dimitri Papadimitriou Dissertation submitted to Dublin City University in partial fulfilment of the requirements for the degree Doctor of Music Performance ROYAL IRISH ACADEMY OF MUSIC Supervisor: Dr. Denise Neary July 2013 Declaration I hereby certify that this material, which I now submit for assessment on the programme of study leading to the award of Doctor of Music Performance, is entirely my own work, and that I have exercised reasonable care to ensure that the work is original, and does not to the best of my knowledge breach any law of copyright, and has not been taken from the work of others save and to the extent that such work has been cited and acknowledged within the text of my work. Signed:_____________________________ ID No.:_____________ Date: _______________ Terms and Conditions of Use of Digitised Theses from Royal Irish Academy of Music Copyright statement All material supplied by Royal Irish Academy of Music Library is protected by copyright (under the Copyright and Related Rights Act, 2000 as amended) and other relevant Intellectual Property Rights. By accessing and using a Digitised Thesis from Royal Irish Academy of Music you acknowledge that all Intellectual Property Rights in any Works supplied are the sole and exclusive property of the copyright and/or other Intellectual Property Right holder. Specific copyright holders may not be explicitly identified. Use of materials from other sources within a thesis -
A Unified System for Chord Transcription and Key Extraction Using Hidden Markov Models
A UNIFIED SYSTEM FOR CHORD TRANSCRIPTION AND KEY EXTRACTION USING HIDDEN MARKOV MODELS Kyogu Lee Malcolm Slaney Center for Computer Research in Music and Acoustics Yahoo! Research Stanford University Sunnyvale, CA94089 [email protected] [email protected] ABSTRACT similarity finding, audio summarization, and mood classi- fication. Chord sequences have been successfully used as A new approach to acoustic chord transcription and key a front end to the audio cover song identification system extraction is presented. As in an isolated word recognizer [1]. For these reasons and others, automatic chord recog- in automatic speech recognition systems, we treat a mu- nition has recently attracted a number of researchers in sical key as a word and build a separate hidden Markov the Music Information Retrieval field. Some systems use model for each key in 24 major/minor keys. In order to a simple pattern matching algorithm [2, 3, 4] while others acquire a large set of labeled training data for supervised use more sophisticated machine learning techniques such training, we first perform harmonic analysis on symbolic as hidden Markov models or Support Vector Machines data to extract the key information and the chord labels [5, 6, 7, 8, 9]. with precise segment boundaries. In parallel, we synthe- Hidden Markov models (HMMs) are very successful size audio from the same symbolic data whose harmonic for speech recognition, whose high performance is largely progression are in perfect alignment with the automati- attributed to gigantic databases with labels accumulated cally generated annotations. We then estimate the model over decades. Such a huge database not only helps es- parameters directly from the labeled training data, and timate the model parameters appropriately, but also en- build 24 key-specific HMMs. -
Music and the Occult : French Musical Philosophies, 1750-1950 Eastman Studies in Music Author: Godwin, Joscelyn
cover Página 1 de 213 title: Music and the Occult : French Musical Philosophies, 1750-1950 Eastman Studies in Music author: Godwin, Joscelyn. publisher: University of Rochester isbn10 | asin: 1878822535 print isbn13: 9781878822536 ebook isbn13: 9780585327020 language: English subject Music and occultism--France, Music--France--Philosophy and aesthetics, Harmony of the spheres, Symbolism in music. publication date: 1995 lcc: ML3800.G57213 1995eb ddc: 781/.1/09444 subject: Music and occultism--France, Music--France--Philosophy and aesthetics, Harmony of the spheres, Symbolism in music. cover Page i Music and the Occult page_i Page ii To Antoine Faivre page_ii file://C:\Jogos\Occult\1878822535\files\__joined.html 26/9/2009 cover Página 2 de 213 Page iii Music and the Occult French Musical Philosophies 1750-1950 By Joscelyn Godwin page_iii Page iv Disclaimer: Some images in the original version of this book are not available for inclusion in the netLibrary eBook. Copyright © 1995 University of Rochester Press All Rights Reserved. Except as permitted under current legislation, no part of this work may be photocopied, stored in a retrieval system, published, performed in public, adapted, broadcast, transmitted, recorded or reproduced in any form or by any means, without the prior permission of the copyright owner. First published 1995 University of Rochester Press 34-36 Administration Building, University of Rochester Rochester, New York, 14627, USA and at PO Box 9, Woodbridge, Suffolk IP12 3DF, UK IBSN 1 87822 53 5 Library of Congress Cataloging-in-Publication Data Godwin, Joscelyn. [Esotérisme musical en France, 1750-1950. English] Music and the occult: French musical philosophies, 1750-1950/ Joscelyn Godwin. -
Guilford College Catalog 2019-2020
Guilford College Catalog 2019-2020 Notice of Non-Discrimination: Guilford College does not discriminate on the basis of sex/gender, age, race, color, creed, religion, national origin, sexual orientation, gender identity, disability, genetic information, military status, veteran status, or any other protected category under applicable local, state or federal law, ordinance or regulation. Read the full notice at www.guilford.edu/nondiscrimination. Guilford College Catalog 2019-20 page 1 of 233 Message From the President Dear Students: On assuming the presidency of Guilford College, I was thrilled to become part of a campus community of authentic, brilliant, dedicated and enthusiastic people. I invite you to join us. As our Strategic Plan lays out, we work together to afford “a transformative, practical and excellent liberal arts education that produces critical thinkers in an inclusive, diverse environment.” We are guided in this mission “by Quaker testimonies of community, equality, integrity, peace and simplicity.” Finally, a Guilford education emphasizes “the creative problem-solving skills, experience, enthusiasm and international perspectives necessary to promote positive change in the world.” Our Quaker heritage and longstanding commitments to undergraduate teaching, social justice and seven Core Values set Guilford apart from other small liberal arts colleges. These Core Values—community, diversity, equality, excellence, integrity, justice and stewardship—infuse every aspect of life and work on campus, how we interact with each other and how we relate to the surrounding community and environment. Guilford is a “making a difference” college and one that has been “changing lives” for over 175 years. Students come here to get equipped to make a positive difference in the world. -
A System for Acoustic Chord Transcription and Key Extraction from Audio Using Hidden Markov Models Trained on Synthesized Audio
A SYSTEM FOR ACOUSTIC CHORD TRANSCRIPTION AND KEY EXTRACTION FROM AUDIO USING HIDDEN MARKOV MODELS TRAINED ON SYNTHESIZED AUDIO A DISSERTATION SUBMITTED TO THE DEPARTMENT OF MUSIC AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Kyogu Lee March 2008 c Copyright by Kyogu Lee 2008 All Rights Reserved ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. (Julius Smith, III) Principal Adviser I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. (Jonathan Berger) I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. (Chris Chafe) I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. (Malcolm Slaney) Approved for the University Committee on Graduate Studies. iii Abstract Extracting high-level information of musical attributes such as melody, harmony, key, or rhythm from the raw waveform is a critical process in Music Information Retrieval (MIR) systems. Using one or more of such features in a front end, one can efficiently and effectively search, retrieve, and navigate through a large collection of musical audio.