Change search
ReferencesLink to record
Permanent link

Direct link
Song Similarity Classication
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The purpose of this study was to investigate the possibility of automatically

classifying the similarity of song pairs. The machine learning algorithm

K-Nearest Neighbours

, combined with both bootstrap aggregating and


attribute selection classier, was rst trained by combining the acoustic

features of 45 song pairs extracted from the Million Song Dataset with usersubmitted

similarity for each pair. The trained algorithm was then utilized

to predict the similarity between 50 hand-picked and about 4000 randomly

chosen pop and rock songs from the Million Song Dataset.

Finally, the algorithm was subjectively evaluated by asking users to identify

which out of two randomly ordered songs, one with a low and one with

a high predicted similarity, they found most similar to a target song. The

users picked the same song as the algorithm 365 out of 514 times, giving the

algorithm an accuracy of 71%.

The results indicates that automatic and accurate classication of song

similarity may be possible and thus may be used in music applications. Further

research on improving the current algorithm or nding alternative algorithms

is warranted to draw further conclusions about the viability of using

automatically classied song similarity in real-world applications.

Abstract [sv]

Syftet med denna studie var att undersoka huruvida det ar mojligt att automatiskt

rakna ut hur lika tva latar ar. I studien anvandes maskininlarningsalgoritmen

k narmaste grannar

tillsammans med bootstrap aggregering och

en klassicerare som sallar bort ovidkommande egenskaper. Algoritmen

tranades forst genom att kombinera ett ertal akustiska parametrar med

anvandares likhetsbedomningar for 45 latpar skapades genom att kombinera

10 latar uttagna fran The Million Song Dataset med varandra. Den tranade

algoritmen anvandes sedan for att rakna ut likheten mellan 50 handplockade

och ungefar 4000 slumpmassigt valda pop- och rocklatar fran the Million

Song Dataset.

Avslutningsvis utvarderades resultaten genom en andra fas av anvandartestning.

Anvandare blev ombedda att lyssna pa en mallat, en av de 50

handplockade latarna, foljt av en av de latar som algoritmen matchat som

mycket lik och en lat som den matchat som mycket olik, i slumpmassig

ordning. Anvandaren ck sedan valja vilken av de tva latarna som tycktes

likna mallaten. Algoritmen och anvandaren valde samma lat i 365 av 514

fall, vilket ger algoritmen en trasakerhet pa 71%.

Resultaten tyder pa att det kan vara mojligt att utveckla en algoritm

som automatiskt kan klassicera likhet mellan latar med hog precision och

darmed skulle kunna anvandas i musikapplikationer. Ytterligare utveckling

av algoritmen, eller forskning pa alternativa algoritmer, ar nodvandigt for

att kunna dra vidare slutsatser om hur anvandbart automatisk uppskattning

av latlikhet ar for verkliga tillampningar.

Place, publisher, year, edition, pages
Kandidatexjobb CSC, K13037
National Category
Computer Science
URN: urn:nbn:se:kth:diva-134955OAI: diva2:668893
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2013-12-13 Created: 2013-12-02 Last updated: 2013-12-13Bibliographically approved

Open Access in DiVA

Song Similarity Classication(332 kB)16 downloads
File information
File name FULLTEXT01.pdfFile size 332 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Other links
By organisation
School of Computer Science and Communication (CSC)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 16 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 79 hits
ReferencesLink to record
Permanent link

Direct link