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Evaluation setup

Evaluation reports





Automatic Audio Segmentation:
Segment Boundary and Structure Detection in Popular Music

by Ewald Peiszer ([firstname].peiszer@gmx.at)

Automatic audio segmentation aims at extracting information on a songs structure, i.e., segment boundaries, musical form and semantic labels like verse, chorus, bridge etc. This information can be used to create representative song excerpts or summaries, to facilitate browsing in large music collections or to improve results of subsequent music processing applications like, e.g., query by humming.

This thesis features algorithms that extract both segment boundaries and recurrent structures of everyday pop songs. Numerous experiments are carried out to improve performance. For evaluation a large corpus is used that comprises various musical genres. The evaluation process itself is discussed in detail and a reasonable and versatile evaluation system is presented and documented at length to promote a common basis that makes future results more comparable.



Phase 1: Boundary detection

This phase tries to detect the segment boundaries of a song, i.e., the time points where segments begin and end. The output of this phase is used as the input for the next phase.

The classic similarity matrix / novelty score approach has been used. In addition, various attempts to further improve the result have been carried out.

The figure below shows the novelty score plot of KC and the Sunshine Band: That’s the Way I Like It. Vertical dotted lines indicate groundtruth boundaries.

Note that automatic boundary extraction worked very well for this song: all major segment boundaries have been found (red askerisks).

Phase 2: Structure detection

This phase tries to detect the form of the song, i.e., a label is assigned to each segment where segments of the same type (verse, chorus, intro, etc.) get the same label. The labels themselves are single characters like A, B, C, and thus not semantically meaningful.

The songs have been fully annotated. Both sequential-unaware approaches and an approach that takes temporal information into account have been used. In addition, cluster validity indices have been employed to find the correct number of segment types for each song.

The right figure (click to enlarge) shows clustering result of KC and the Sunshine Band: That’s the Way I Like It song segments. Numbered circles indicate segments, crosses mark cluster centroids.

The source code of the algorithm implemented in Matlab can be obtain from the download section. For information on how to use it, please refer to the included README file (or ask the author if there are still problems).


Evaluation setup

A significant amount of time has been invested in careful considerations about good evaluation. An easy-to-use evaluation program that produces both appealing and informative HTML reports has been designed and implemented.

You can download the source code from the download section at the bottom of this page.

A novel file format for audio segmentations (SegmXML) has been introduced. This format can contain information about hierarchical segments and alternative labels. See the example groundtruth file for Alanis Morisette: Thank You. A corresponding XML schema definition file for validating SegmXML files is available, too.


Selected evaluation reports

The evaluation reports of the following algorithm runs are available. Note that this table corresponds to Table 3.1 of the thesis. For an explanation of symbols and abbreviations used please refer to the thesis.

Parameter set Parameter changed Boundary extraction results / hyperlink
MFCC40 dS: Euclidean P=0.55+- 0.038, R=0.78+- 0.035, F=0.65
MFCC40 dS: cosine P=0.55+- 0.039, R=0.76+- 0.038, F=0.64
CQT1 nH=8 P=0.45+- 0.04, R=0.77+- 0.037, F=0.56
CQT1 nH=12 P=0.46+- 0.043, R=0.7+- 0.04, F=0.56
CQT1 nH=16 P=0.52+- 0.044, R=0.64+- 0.042, F=0.58
CQT1 nH=18 P=0.52+- 0.043, R=0.62+- 0.041, F=0.57
MFCC40 kC=48, nH=4 P=0.49+- 0.035, R=0.77+- 0.031, F=0.6
MFCC40 kC=96, nH=8 P=0.55+- 0.038, R=0.78+- 0.035, F=0.65
MFCC40 kC=128, nH=8 P=0.59+- 0.039, R=0.72+- 0.039, F=0.65
MFCC40 kC=128, nH=14 P=0.62+- 0.038, R=0.67+- 0.041, F=0.65
MFCC40 boundary removing heuristic P=0.57+- 0.038, R=0.75+- 0.038, F=0.65
MFCC40 post processing P=0.54+- 0.038, R=0.78+- 0.037, F=0.64

MFCC40 and CQT1 are names of two parameter value sets that are explained in Table 3.2 of the thesis. MFCC40 uses Mel Frequency Cepstrum Coefficients features whereas CQT1 employs Constant Q Transform with such parameter values for fundamental frequency, maximal frequency and number of bins that the feature vectors model the semitones of seven octaves, each octave containing twelve notes.



The corpus on which this work is based contains 94 songs of various genres (Rock, Pop, Hiphop, RNB, etc). Final algorithm runs are conducted on a 109 song corpus which is the largest corpus used so far in this research field. The following table contains all songs of the corpus.

Unfortunately, the demonstration songs cannot be published due to copyright issues.

Artist Title
A-HA Take on me
ABBA Waterloo
Alanis Morissette Head Over Feet
Alanis Morissette Thank You
Artful Dodger Craig David Rewind
Beastie Boys Intergalactic
Beatles All I've Got To Do
Beatles All My Loving
Beatles Devil In Her Heart
Beatles Don't Bother Me
Beatles Hold Me Tight
Beatles I saw her standing there
Beatles I Wanna Be Your Man
Beatles It Won't Be Long
Beatles Little Child
Beatles Misery
Beatles Money
Beatles Not A Second Time
Beatles Please Mister Postman
Beatles Roll Over Beethoven
Beatles Till There Was You
Beatles You Really Got A Hold On Me
Beatles Anna go to
Beatles Please please me
Björk It's Oh So Quiet
Black Eyed Peas Cali To New York
Britney Spears Hit Me Baby One More Time
Britney Spears Oops I Did It Again
Chicago Old Days
Chumbawamba Thubthumping
Coolio The Devil Is Dope
Cranberries Zombie
Creedence Clearwater Revival Have You Ever Seen the Rain
Depeche Mode It's no good
Desmond Dekker You Can Get It If You Really Want
Deus Suds & Soda
Dire Straits Money For Nothing
Eminem ft. Dido Stan
Faith No More Epic
Gloria Gayner I Will Survive
KC and the Sunshine Band That's the Way I Like It
KoRn Got The Life
Lucy Pearl Don't Mess With My Man
Madonna Like a virgin
Madonna Into the Groove
Marilyn Manson Sweet Dreams
Michael Jackson Bad
Michael Jackson Black Or White
Nick Drake Northern Sky
Nirvana Smells like teen spirit
Nora Jones Lonestar
Oasis Wonderwall
Pet Shop Boys Always On My Mind
Portishead Wandering star
Prince Kiss
Queen Yahna Ain't It Time
R.E.M. Drive
R Kelly I Believe I Can Fly
Radiohead Creep
Red Hot Chili Peppers Parallel Universe
Salt N Pepa Whatta Man
Saxon The Great White Buffalo
Scooter How Much Is The Fish
Seal Crazy
Shania Twain You're Still The One
Simply Red Stars
Sinhead O Connor Nothing compares to you
Spice Girls Wannabe
Suede Trash
The Beatles A Day In The Life
The Beatles A Hard Days Night
The Beatles Being For The Benefit Of Mr. Kite
The Beatles Fixing A Hole
The Beatles Getting Better
The Beatles Good Morning Good Morning
The Beatles Help
The Beatles I Should Have Known Better
The Beatles If I Fell
The Beatles I'm Happy Just To Dance With You
The Beatles Lovely Rita
The Beatles Lucy In The Sky With Diamonds
The Beatles Sgt. Peppers Lonely Hearts Club Band
The Beatles Sgt. Peppers Lonely Hearts reprise
The Beatles She's Leaving Home
The Beatles When I'm Sixty-Four
The Beatles With A Little Help From My Friends
The Beatles Within You Without You
The Clash Combat Rock
The Jacksons 5 Can You Feel It
The Monkees Words
The Police Message In A Bottle
The Roots The Next Movement
The Roots ft. Erykah Badu You Got Me

Additional 15 songs ("test set")
Apollo 440 Stop The Rock
Eav Wo Ist Der Kaiser
Kazuo Nishi Eien no replica
Hiromi Yoshii Magic in your eyes
Fevers Jinsei konnamono
Kazuo Nishi Doukoku
Kazuo Nishi Kage-rou
Hisayoshi Kazato Cool Motion
Rin Feeling In My Heart
Mitsuru Tanimoto Syounen no omoi
Hiromi Yoshii Dream Magic
Hiromi Yoshii Midarana kami no moushigo
The Crystal Method Born Too Slow
Wise Guys Kinder
Wise Guys Powerfrau


Both boundary detection and structure extraction are quite acceptable, yet improvable.

The algorithm, however, proved to be robust in a negative and positive sense: Many experiments conducted with various parameter settings and heuristics applied did not lead to a statistically significant improvement of the mean performance.

On the other hand, cross validation and the performance on an independent test set did not show any decline in performance either. Thus, the algorithm presented seems suitable to be applied to a wide range of songs and genres.



  • Master's thesis: Ewald Peiszer: Automatic Audio Segmentation: Segment Boundary and Structure Detection in Popular Music (pdf)
  • Poster (pdf)
  • Segmentation algorithm (Matlab) and Evaluation system (Perl) are available on request from the author
  • Beats files (Beat onsets of all songs extracted by Simon Dixon's BeatRoot. Plain text format.)
  • Ground truth files (SegmXML file format). Please note, that the groundtruth for the 36 files which originated from Jouni Paulus is not included. Please contact Jouni Paulus for  obtaining the groundtruth for these files.
last edited 02.08.2007 by Ewald Peiszer, 20.08.2007 by Thomas Lidy