12th International Society for Music Information Retrieval Conference (ISMIR 2011)

SONGLE: A WEB SERVICE FOR ACTIVE MUSIC LISTENING
IMPROVED BY USER CONTRIBUTIONS
Masataka Goto, Kazuyoshi Yoshii, Hiromasa Fujihara, Matthias Mauch, and Tomoyasu Nakano
National Institute of Advanced Industrial Science and Technology (AIST), Japan
structure, which facilitates deeper understanding. However,up to now the general public has not had the chance of using
such research-level interfaces and technologies in daily life.
We therefore developed a web service called Songle thatallows anonymous web users to enjoy music by using active
music listening interfaces on a web browser. Songle uses automatic music-understanding technologies to estimate music scene descriptions (musical elements) [3, 4] of musicalpieces (audio files) available on the web. A user of Songle
can enjoy playing back a musical piece while seeing thevisualization of the estimated descriptions. In our current
implementation, four major types of descriptions are automatically estimated and visualized for content-based music browsing: music structure (chorus sections and repeatedsections), hierarchical beat structure (musical beats and barlines), melody line (fundamental frequency (F0) of the vocal
melody), and chords (root note and chord type). In particular, Songle implements all functions of the SmartMusicKIOSK interface, and a user can jump and listen to the chorus with just a push of the next-chorus button. Songle thus
makes it easier for a user to find desired parts of a piece.
Given the variety of musical pieces on the web, however,
it is difficult to estimate music scene descriptions with highaccuracy. Because of the diversity of music genres, complexity of sound mixtures, and recording conditions, automatic music-understanding technologies cannot avoid making some errors, even though the technologies are constantly
improving. As a result, users of such a web service might
be disappointed by its performance.
To overcome this difficulty, Songle enables anonymous
users to contribute by correcting music-understanding errors. Each user can see the music-understanding visualizations on a web browser, with a moving cursor indicating
the audio playback position. If a user finds an error while
listening, the user can easily correct the error by selecting from a list of candidates, or by providing an alternativedescription on Songle’s efficient error correction interface.
The resulting corrections are then shared and used to immediately improve the user experience with the corrected
piece. We also plan to use such corrections to gradually improve music-understanding technologies through adaptive
machine learning techniques, so that descriptions of othermusical pieces can be estimated more accurately. This approach can be described as collaborative training for musicunderstanding technologies on the web.
Development for Songle started in June 2009, and theSongle website http://songle.jp will be open to the public

ABSTRACTThis paper describes a public web service for active music listening, Songle, that enriches music listening experiences by using music-understanding technologies basedon signal processing. Although various research-level interfaces and technologies have been developed, it has notbeen easy to get people to use them in everyday life. Songle
serves as a showcase to demonstrate how people can benefit from music-understanding technologies by enabling people to experience active music listening interfaces on theweb. Songle facilitates deeper understanding of music by visualizing music scene descriptions estimated automatically,such as music structure, hierarchical beat structure, melody
line, and chords. When using music-understanding technologies, however, estimation errors are inevitable. Songle
therefore features an efficient error correction interface thatencourages people to contribute by correcting those errors
to improve the web service. We also propose a mechanismof collaborative training for music-understanding technologies, in which corrected errors will be used to improve themusic-understanding performance through machine learning techniques. We hope Songle will serve as a researchplatform where other researchers can exhibit results of theirmusic-understanding technologies to jointly promote thepopularization of the field of music information research.
1. INTRODUCTION
The goal of this research is to enrich music listening experiences by using music-understanding technologies based on
signal processing. Toward this goal, we have already developed various active music listening interfaces [1], where
active music listening is a way of listening to music throughactive interactions. In this research, the word active is notmeant to convey that the listeners create new music, but thatthey take control of their own listening experience. For example, the active music listening interface SmartMusicKIOSK [2] has a chorus-search function that enables a user toaccess directly his or her favorite part of a song (and to skip
others) while viewing a visual representation of its music
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c 2011 International Society for Music Information Retrieval.
⃝311

Oral Session 4: Web

tion, a logged-in user can generate a playlist. Such a playlist
has a title (theme) and the user can choose whether to shareit with other users. A logged-in user can also enter andrecord a preference (like or dislike) for each song to get better song recommendations in the future.
Songle supports three main functions: retrieving, browsing, and annotating songs. The retrieval and browsing functions facilitate deeper understanding of music, and the annotation (error correction) function allows users to contributeto improve music scene descriptions. These improved descriptions can then lead to a better user experience of retrieving and browsing songs.
2.1 Retrieval Function
This is a function that enables a user to retrieve a songthrough a text search of the song title or artist name, or
through selection from a list of artists or a list of songs
whose descriptions were recently estimated or corrected.
This function also shows various kinds of ranking, such as
artist ranking, song ranking, and user ranking (by the number of corrected errors).
Following the idea of the active music listening interface
VocalFinder [5], which finds songs with similar vocal timbres, a similarity graph of songs is also visualized so thata user can retrieve a song according to vocal timbre similarity. The graph is a radially-connected network in which
nodes (songs) of similar vocal timbre are connected to thecenter node (a recommended or user-specified song). By
traversing a graph while listening to nodes, a user can find asong having the favorite vocal timbre.
A user can play back a song in any list of songs (e.g.,
playlist, retrieved list, and ranking) for trial listening tojudge whether it is of interest. By selecting one of the songs,
the user switches over to the next browsing function. Whileusing the browsing function, songs in the playlist are automatically switched one after another if a user selects andlistens to a playlist.

Figure 1. Songle screen snapshot of the main interface formusic playback with the visualization of music scene descriptions estimated automatically.
before the ISMIR 2011 conference. In addition to the contribution of enriching music listening experiences, Songle
will serve as a showcase in which everybody can experiencemusic-understanding technologies and understand their nature: for example, what kinds of music or sound mixture
are difficult for the technologies to handle. Furthermore,
we hope to extend Songle so that it can serve as a researchplatform able to support various music-understanding technologies developed by different researchers.
2. OVERVIEW OF SONGLE

2.2 Within-song Browsing Function

Songle is a social annotation web service where users canretrieve, browse, and annotate musical pieces on the web.
Figure 1 shows the web page of the Songle interface after
a musical piece is selected. During the initial stage of theSongle launch we are focusing on popular songs with vocals. Songs recently released on music web services such
as Magnatune (http://magnatune.com/) and PIAPRO1 areadded (registered) to Songle. A user can also register any
song available on the web by providing the URL of its MP3
file, the URL of a web page including multiple MP3 URLs,
or the URL of a music podcast (an RSS syndication feed
including multiple MP3 URLs).
Everybody can enjoy active music listening and correct
errors as an anonymous user without logging in, but a user
has to log in with OpenID to register a new song. In addi-

This is a function that provides a content-based playbackcontrol interface for within-song browsing as shown in theupper half of Figure 1. The upper window is the global view
showing the entire song and the lower window is the local
view magnifying the selected region.
With this function, a user can view the following fourtypes of music scene descriptions estimated automatically:
1. Music structure (chorus sections and repeated sections)
In the global view, the music map of the SmartMusicKIOSK interface [2] is shown below the playback controls
including the buttons, time display, and playback slider.
The music map is a graphical representation of the entire
song structure consisting of chorus sections (the top row)
and repeated sections (the five lower rows). On each row,colored sections indicate similar (repeated) sections. Thismap helps a user decide where to jump. Clicking directly

1 PIAPRO (http://piapro.jp/) is a web service to which musicians canupload their own songs created using singing synthesizers.

31212th International Society for Music Information Retrieval Conference (ISMIR 2011)(a) Correcting music structure(chorus sections and repeated sections)(b) Correcting hierarchical beat structure(musical beats and bar lines)(c) Correcting melody line (F0 of the vocal melody)(d) Correcting chords (root note and chord type)Figure 2. Songle screen snapshots of the annotation function for correcting music scene descriptions.
on a colored section plays that section. There are also buttons for jumping to the next or previous chorus sections,and the next or previous repeated sections.
2. Hierarchical beat structure (musical beats and bar lines)
At the bottom of the local view, musical beats corresponding to quarter notes are visualized by using small triangles.
The top of each triangle indicates its temporal position.
Bar lines are marked by larger triangles.
3. Melody line (F0 of the vocal melody)
The piano roll representation of the melody line is shown
above the beat structure in the local view. It is also shownin the lower half of the global view. For simplicity, thefundamental frequency (F0) can be visualized after being
quantized to the closest semitone.
4. Chords (root note and chord type)
Chord names are written in the text at the top of the local
view. Twelve different colors are used to represent twelve
different root notes so that a user can notice the repetitionof chord progressions.
In the lower half of Figure 1, a user can add and sharesocial tags and time-synchronous comments for Crowd Music Listening [6]. Clicking on a time-synchronous comment
starts playback from that position.

music. Here, annotation means describing the contents of asong, either by modifying the estimated descriptions or by
selecting the correct candidate if available. For this purpose,
we provide an efficient error correction interface (editor) as
shown in Figure 2.
Editors for four types of music scene descriptions can beswitched between in the local view.
1. Music structure (Figure 2(a))
The beginning and end points of every chorus or repeatedsection can be adjusted. It is also possible to add, move,or delete each section. Repeated sections can serve as candidates for the chorus sections, but since it is not obvious
how to correct repeated sections, we plan to let a user typein a section label (e.g., verse A, verse B, etc.) on each row.
This correction function improves the SmartMusicKIOSK
experience.
2. Hierarchical beat structure (Figure 2(b))
Several alternative candidates for the beat structure can beselected at the bottom of the local view. If none of thecandidates are useful, a user can enter the beat position bytapping a key during music playback. Each beat positionor bar line can also be changed directly. For fine adjustment it is possible to play back the audio with click tones
at beats.
3. Melody line (Figure 2(c))
Songle allows note-level correction on the piano roll rep-

2.3 Annotation (Error Correction) Function
This function allows users to add annotations to correct any
estimation errors they may come across while listening to313

Database manager
Music-understanding manager
Speechrecognizer
recognizer
SpeechSpeechrecognizer
Speechrecognizer
Music-understanding
moduleWeb server

Web crawler(MP3 music file)

Musicalpiece
Musicalpiece(MP3music file)
Musicalpiece(MP3music file)
Musicalpiece(MP3 music file)

Oral Session 4: Webworks as a website that provides the Songle user interface,which directly plays back the MP3 file from the originalURL.
The web server of Songle was implemented by using a
web application framework Ruby on Rails, a programming
language Ruby, a web server Passenger and Apache, and a
database MySQL. The client user interface was implemented
by using a scripting language ActionScript 3, an ActionScript 3 compiler Adobe Flex Compiler, and a scripting language JavaScript.
Music scene descriptions are estimated as follows.
1. Music structure
Chorus sections and repeated sections are estimated by using the chorus-section detection method RefraiD [2] which
focuses on popular music. By analyzing relationships between various repeated sections, the RefraiD method can
detect all the chorus sections in a song and estimate bothends of each section. It can also detect modulated chorus
sections.
2. Hierarchical beat structure
The beats are estimated using a hidden Markov model(HMM) with 43 tempo states, each having 18 to 60 substates corresponding to the beat phase of different tempi.
In each tempo a beat is modeled as a left-to-right HMM
in which only some states have non-deterministic transition probabilities to allow for tempo fluctuations or tempochanges. The emission probability of a sub-state is calculated via the cosine similarity between a comb filter and asimple onset detection function. Five different comb-filter
shapes are used to output five different beat-tracking results, including those that are likely true candidates if thedefault algorithm tracks the back-beat or beats at twice thetrue tempo. This strategy maximizes the likelihood of offering the beat-tracking result desired by the user.
The bar lines are estimated using harmonic cues. First, we
extract tatum-synchronous bass and treble chromagrams
using NNLS Chroma [7]. We build a simple chord detection model and calculate posterior probabilities of chord
changes. Using a sliding window, we compute the cosine
similarity between the chord change probabilities and several different bar patterns that cover the 3/4, 4/4 and 6/8
meters and all possible bar phases. We normalize the cosine similarities at each frame and use them as emissions
in another HMM similar to the beat-tracking model.
3. Melody line
The fundamental frequency (F0) of the vocal part is estimated by using the F0 estimation method for the vocal melody [8], which is implemented by extending thepredominant-F0 estimation method PreFEst [4]. Thismethod focuses only on the vocal melody by evaluating a
GMM-based vocal probability for each F0 candidate estimated by PreFEst. Moreover, vocal activity detection was
implemented by using a method described in [9].
4. Chords
We transcribe chords using 14 chord types: major, major 6th, major 7th, dominant 7th, minor, minor 7th, half-

User interface
User interface
User interface

Figure 3. Implementation overview of Songle.
resentation of the melody line. Since the melody line is internally represented as the temporal trajectory of F0, more
precise correction is also possible by choosing from F0candidates. A user can listen to the melody line only or the
melody-cancelled background playback. More accurate
melody annotations will lead to better similarity graphs ofsongs.
4. Chords (Figure 2(d))
Chord names can be corrected by choosing from candidates or by explicit typing of chord names. Each chordboundary can also be adjusted. Chords can be played backalong with the original song to make it easier to check thecorrectness.
Note that users can simply enjoy active music listening
without correcting errors. We understand that it is too difficult for some users to correct the above descriptions (especially, chords). Designing an interface that makes it easierfor them to correct will be another future challenge. Moreover, users are not expected to correct all errors, only someaccording to each user’s interests.
When the music-understanding results are corrected byusers, the original values are visualized as trails with different colors (white, gray, or yellow marks in Figure 2)
that can be distinguished by anybody.
These trails
are important to prevent overestimation of the automaticmusic-understanding performance after the user corrections.
Moreover, all the correction histories are recorded, and descriptions before and after corrections can be compared.
3. IMPLEMENTATION OF SONGLE
The implementation overview of Songle is shown in
Figure 3. The web crawler collects musical pieces (MP3files) on the basis of their URLs and RSS feeds, which canbe added by users, and stores the pieces in the database.
Several music-understanding modules, each corresponding
to a particular type of music scene description, then process each musical piece. For example, the beat structure andthe music structure are estimated by two different modules.
When a request from an idle music-understanding module
is received by the music-understanding manager, the next
available musical piece lacking an estimation result for thecorresponding description is handed over. After the musicunderstanding module finishes processing its piece, the estimation result is passed to the database manager via themusic-understanding manager. The database manager controls the processing state of the musical pieces and stores
their estimation results in a database together with the corrections when provided by users. Finally, the web server314

12th International Society for Music Information Retrieval Conference (ISMIR 2011)

diminished, diminished, augmented, and five variants ofmajor chords with different bass notes: /2, /3, /5, /b7, and/7. The resulting 14 types × 12 root notes = 168 chords
and one ‘no chord’ label are estimated using an HMM approach on the same tatum-synchronous chromagram used
for the bar-line estimation. Chord changes are allowed tohappen only on beats.
We also include knowledge from the bar-line estimation
and a key estimate. Instead of building one large modelin which chords and keys are modeled simultaneously(e.g. [10]), we chose a less memory-intensive, progressive
approach. First, we model the key in a simple separate
HMM with three different key scales: major, natural minor, and harmonic minor. Every key state has observation
probabilities for all different chords, based on an expert
function [10]. The posterior probability obtained from the
HMM is then used to weight the chord probabilities for thechord HMM. During Viterbi decoding we use the bar-line
estimates for dynamic transition probability weighting inorder to encourage chord changes at bar lines.
To show the similarity graph of songs, the vocal timbre
similarity between songs is calculated by using the method
used in VocalFinder [5]. The current recommendation ofsongs simply uses the same similarity, which shall be improved in the future.

based on the ESP Game [18] in which players are not asked
to describe a sound, but told to guess what their randomly
paired partners are thinking. Songle provides a stronger motivation for users to contribute than these related approaches
because the user is aware of improving the service for otherusers.
To annotate musical pieces, various useful editors havebeen developed, such as Sonic Visualiser [19], Audacity extension [20], CLAM [21], and MUCOSA [22]. The Echo
Nest API (http://developer.echonest.com/) is also useful foraccess to various annotations. Songle is the first system thatallows anonymous users to collaborate to edit various music scene descriptions (chorus, beats, melody, and chords)
directly on the web without stand-alone applications.
4.2 Contributions of Songle
Songle makes a social contribution by providing theworld’s first public web service for enjoying active music listening interfaces with music-understanding technologies. It also promotes the popularization and use ofmusic-understanding technologies by raising user awareness. Users can grasp the nature of music-understanding
technologies just by seeing results of the technologies applied to songs available on the web. When there are manyerrors, we run the risk of attracting criticism, but we believe that sharing these results with users will promote further popularization of this research field.
The academic contribution of this study is to propose a
new research approach to music understanding based on signal processing; this approach aims at improving both themusic-understanding performance and the usage rate while
benefiting from the cooperation of anonymous end users.
This approach is designed to set into motion a positive spiralwhere (1) we enable users to experience a service based onmusic understanding to let them better understand its performance, (2) users contribute to improved performance, and(3) the improved performance leads to a better user experience, which encourages further use of the service at step
(1) of this spiral. This is a social correction framework,where users can improve the performance by sharing theircorrection results over a web service. The game-based approach of Human Computation or GWAPs (games with apurpose) [23] like the ESP Game [18] often lacks step (3)and depends on the feeling of fun. In this framework, users
gain a real sense of contributing for their own benefit andthat of others and can be further motivated to contribute byseeing corrections made by other users. In this way, we canuse the wisdom of crowds or crowdsourcing to achieve a better user experience.
Another important technical contribution of this study
is to investigate how far the performance of musicunderstanding technologies can be improved by getting errors corrected through the cooperative efforts of users. Although we have not yet implemented a machine-learning
mechanism to improve the performance on the basis of usercorrections, we could implement such a mechanism once we

4. DISCUSSION
While research dealing with a public web service for active
music listening and social annotation has not been pursued
in the past, there have been various approaches related tomusic annotation. In the following, we introduce related
work and then discuss how Songle could contribute to society and academic research.
4.1 Related Research
Several approaches have been proposed to collect a large
amount of ground-truth annotations. Such annotations
are useful for improving the accuracy of music information retrieval (MIR) systems using machine learning techniques, and for evaluating MIR systems [11]. For example, Lee [12] used a web service called Amazon Mechanical
Turk (MTurk) to ask people to make similarity judgments.
Mandel et al. [13] also used MTurk for tag collection. These
approaches showed that the quality of the collected annotations was sufficiently high. However, the human effort
necessary increases in proportion to the required number ofannotations.
To solve this problem, it is interesting to let non-experts
contribute to making ground-truth annotations. One promising approach is based on games. For example, Turnbull etal. [14, 15] proposed an annotation game, where players are
asked to choose the most and least suitable descriptions fora given musical piece. Mandel and Ellis [16] proposed another annotation game, where players can get points by providing useful descriptions that were not provided by otherplayers. Law et al. [17] proposed another annotation game315

Oral Session 4: Web

have collected enough corrections. This study will then provide a framework for amplifying user contributions in music research. In a typical Web 2.0 service like Wikipedia,
improvements are limited to an item directly contributed(edited) by users. In Songle, improvements will automatically spread to other songs because of the improvementof the music-understanding technologies through machinelearning techniques. This will be a novel technology of amplifying user contributions, which could be beyond Web 2.0
and Human Computation [23]. We hope that this study willshow the importance and potential of incorporating and amplifying user contributions in music research, as have been
demonstrated by PodCastle in speech research [24, 25].
We think we can trust users with respect to the qualityof correction according to our experiences from PodCastle [25]. Even if some users deliberately make inappropriate
corrections (the vandalism problem), we will be able to develop countermeasures to acoustically evaluate the reliability of corrections. For example, we could validate whether
the corrected descriptions can be supported by acoustic phenomena. This will be another interesting topic of research.

ACKNOWLEDGMENTS: We thank Utah Kawasaki for
the web service implementation and Minoru Sakurai for the
web design of Songle. This work was supported in part by
CrestMuse, CREST, JST.
6. REFERENCES[1] M. Goto, “Active music listening interfaces based on signal processing,” in Proc. of ICASSP 2007, 2007.
[2] M. Goto, “A chorus-section detection method for musical audio signals and its application to a music listening station,” IEEE Trans. on
ASLP, vol. 14, no. 5, pp. 1783–1794, 2006.
[3] M. Goto, “Music scene description project: Toward audio-based
real-time music understanding,” in Proc. of ISMIR 2003, pp. 231–232,
2003.
[4] M. Goto, “A real-time music scene description system:
Predominant-F0 estimation for detecting melody and bass lines
in real-world audio signals,” Speech Communication, vol. 43, no. 4,
pp. 311–329, 2004.
[5] H. Fujihara et al., “A modeling of singing voice robust to accompaniment sounds and its application to singer identification and vocaltimbre-similarity-based music information retrieval,” IEEE Trans. on
ASLP, vol. 18, no. 3, pp. 638–648, 2010.
[6] M. Goto, “Music listening in the future: Augmented MusicUnderstanding Interfaces and Crowd Music Listening,” in Proc. of the
AES 42nd International Conf. on Semantic Audio, pp. 21–30, 2011.
[7] M. Mauch and S. Dixon, “Approximate note transcription for the
improved identification of difficult chords,” in Proc. of ISMIR 2010,
pp. 135–140, 2010.
[8] H. Fujihara et al., “F0 estimation method for singing voice inpolyphonic audio signal based on statistical vocal model and Viterbi
search,” in Proc. of ICASSP 2006, pp. V–253–256, 2006.
[9] H. Fujihara et al., “Automatic synchronization between lyrics andmusic CD recordings based on Viterbi alignment of segregated vocal
signals,” in Proc. of ISM 2006, pp. 257–264, 2006.
[10] M. Mauch and S. Dixon, “Simultaneous estimation of chords andmusical context from audio,” IEEE Trans. on ASLP, vol. 18, no. 6,
pp. 1280–1289, 2010.
[11] J. S. Downie, “The music information retrieval evaluation exchange(2005–2007): A window into music information retrieval research,”
Acoustical Science and Technology, vol. 29, pp. 247–255, 2008.
[12] J. H. Lee, “Crowdsourcing music similarity judgments using Mechanical Turk,” in Proc. of ISMIR 2010, pp. 183–188, 2010.
[13] M. I. Mandel, D. Eck and Y. Bengio, “Learning tags that vary withina song,” in Proc. of ISMIR 2010, pp. 399–404, 2010.
[14] D. Turnbull et al., “A game-based approach for collecting semantic
annotations of music,” in Proc. of ISMIR 2007, pp. 535–538, 2007.
[15] D. Turnbull, L. Barrington and G. Lanckriet, “Five approaches tocollecting tags for music,” in Proc. of ISMIR 2008, pp. 225–230, 2008.
[16] M. I. Mandel and D. P. W. Ellis, “A Web-based game for collecting
music metadata,” in Proc. of ISMIR 2007, pp. 365–366, 2007.
[17] E. L. M. Law et al., “TagATune: A game for music and sound annotation,” in Proc. of ISMIR 2007, pp. 361–364, 2007.
[18] L. von Ahn and L. Dabbish, “Labeling images with a computer
game,” in Proc. of CHI 2004, pp. 319–326, 2004.
[19] C. Cannam et al., “The Sonic Visualiser: A visualisation platform
for semantic descriptors from musical signals,” in Proc. of ISMIR
2006, pp. 324–327, 2006.
[20] B. Li, J. A. Burgoyne and I. Fujinaga, “Extending Audacity as agrouth-truth annotation tool,” in Proc. of ISMIR 2006, pp. 379–380,
2006.
[21] X. Amatriain et al., “The CLAM annotator: A cross-platform audio
descriptors editing tool,” in Proc. of ISMIR 2005, pp. 426–429, 2005.
[22] P. Herrera et al., “MUCOSA: A music content semantic annotator,”in Proc. of ISMIR 2005, pp. 77–83, 2005.
[23] L. von Ahn, “Games with a purpose,” IEEE Computer Magazine,
vol. 39, pp. 92–94, June 2006.
[24] M. Goto, J. Ogata and K. Eto, “PodCastle: A Web 2.0 approach tospeech recognition research,” in Proc. of Interspeech 2007, 2007.
[25] M. Goto and J. Ogata, “PodCastle: Recent advances of a spoken
document retrieval service improved by anonymous user contributions,” in Proc. of Interspeech 2011, 2011.

4.3 Songle as a Research Platform
In the future, we hope to extend Songle to serve as a research platform where other researchers can also exhibit results of their own music-understanding technologies. Wheneach technology is implemented as a music-understanding
module in Figure 3, which can be executed anywhere in theworld even in our current implementation, it is not necessary to share its source and binary codes. Each in-housemodule, even inside an Internet firewall, can just connect
to the music-understanding manager to receive an audio
file and send back music-understanding results via HTTP.
The results should always be shown with clear acknowledgments/credits so that users can distinguish the sources. We
are also interested in adding other types of music scene descriptions, which are not yet supported.
Once this happens, it will be interesting to compare/visualize differences of modules on each music scene
description. Results from different researchers can also be
used as candidates for the correction and even as votes to letdifferent results converge.
5. CONCLUSION
We have described Songle, an active music listening service
that is continually improved by user contributions. In ourcurrent implementation, four types of music scene descriptions are estimated and exposed through web-based interactive user interfaces. Since automatic music-understanding
technologies are not perfect, Songle allows users to makeerror corrections, which are shared with other users, thus
creating a positive spiral and an incentive to keep correcting. For the MIR community this platform will act both as atest-bed or showcase for new technologies, and as a way ofcollecting valuable annotations.

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