Proceedings of the Peradeniya University Research Sessions, Sri Lanka, Vol. 12, Part II, 3dh November 2007

An Algorithm for Automatic Play list Generation in Radio Stations

I.M.K. Kahanda" and V.G. Kanewela

Department of Computer Engineering, Faculty of Engineering. University of Peradeniya

IntroductionIn a typical radio station, DJs play songs usingpre-generated play lists. A play list is a se~ ofmusic titles which should be played in a givenorder. Composing high quality play lists forradio stations is very demanding because of thelarge number of songs available for selection.Therefore the person responsible forcomposing playlists have to either memorize orlisten to the excerpts of the songs in order tochoose the right song for the right situation.

All the radio stations in Sri Lanka build theirplaylists manually. In today's competitiveworld, even though maintaining a highstandard largely defines the popularity of thestation, the above situation makes it difficultfor a human to achieve this consistently. Thisis the motivation for this research whichexamined the feasibility of using a computer togenerate music play lists for radio stations usinga newly developed Automatic Play listGeneration (APG) algorithm.

Related workAPG, which is formally defined by Pauws etal., (2006) has enjoyed lots of research recentlyand the researchers have come up with manydifferent approaches depending on differentapplications, such as similarity basedtechniques for Personal music players toConstraint Satisfaction Programming (CSP)approach for Electronic Music Distribution(EMD) systems. Similarity based techniquesare specially used for recommender systems, inwhich the listener selects one or more of herfavorite songs to be used as seed songs and thegenerator will pick the rest of the songs one byone for the unordered playlist. Collaborativefiltering (French and Hauver, 2001; Pestoni etal., 2001), timbre similarity measures (Loganand Salomon, 2001; Aucouturier and Pachet,2002) and machine learning using existingplaylists (Platt et al., 2002) are the popu~arsimilarity based techniques. The majoradvantage of using these techniques is thattheir sensitivity in capturing the changes in thechoices of a single listener.

In CSP approach, instead of selectingindividual music titles, one by one, like above,the idea is to produce a collection of musictitles, in an order, restricted to a set of globalconstraints (Pachet et al., 1999). One of thebetter CSP approaches to APG, with regard toscalability is proposed by Aucouturier andPrachet (Pachet, 2002), which starts by havinga random set of songs from the whole set, anditeratively tries to improve the playlist bymaking small changes to it, using adaptivesearch (an adaptation of the local search)algorithm (Codognet and Diaz, 2001).

Both these approaches so far have looked atAPG for play list generation mainly for a set ofvery few listeners or a large set of listenerswhich can provide immediate feedback. Thispaper, for the first time, presents an APGtechnique for radio stations (a large global setof listeners from which continuous immediatefeedback cannot be obtained), PlayGen, whichis a hybrid of the aforementioned techniquesand enjoys the benefits of both the schemes.

MethodologyThe APG algorithm presented in this paper,PlayGen, uses the algorithm presented in(Prachet, 2002) as a starting point due to itsscalability. The main idea of that the algorithmpresented is that the qualities/characteristicswhich a playlist should posses are described asconstraints.

Problem statementThe playlist generation problem is defined asfollows in (Prachet, 2002). A playlist S is asequence of variables v••V2 ••• , Vn whose valuesXi can be taken from a catalogue of music titlesD. The problem is to assign values to variables,so that the resulting sequence satisfiesconstraints c[, C2, ••. ,cm.

ConstraintsConstraints are seen as simple cost functions.The cost represents how 'bad' the constraint issatisfied, for the given assignment of variables.Constraints may hold on attributes of music

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Proceedings of the Peradeniya University Research Sessions, Sri Lanka, Vol.l Z, Part II, 3dhNollember 2007

titles and these are typically metadata of songs.Therefore, a playlist will have a total costassociated with it. The algorithm starts with acertain initial playlist (having a certain totalcost) and iteratively refines the solution until aplaylist satisfying all the given constraints(having a total cost less than a given threshold)is found. Authors have developed the followingthree compulsory constraints for radio stationsalong with several other optional ones.

• C-ALL-DIFFERENT: Two songs of thesame album/artist should not be included.

• C-DISTRIBUTION: Genre (e.g. pop),type of artist (e.g. female), release yearand tempos (in beats per minute) of twoconsecutive songs should be different.

• C-VARJETY: Number of differentgenres of songs should exceed a certainthreshold.

For example, the cost associated with C-VARlETY is quantified as follows.

Cost(C-VARlETY):

Return the length of the playlist - number ofgenres of music titles divided by length of theplaylist

PlayGen algorithmPlayGen possesses two major features whichare not found in the APG algorithm presentedin (Pachet, 2002). First, as shown in Figure 1,the PlayGen algorithm searches only through asmall subset of songs termed request neighborset (RNS), as opposed to the whole set D. RNSis the set of similar songs to the recent listenersong requests, which is generated by thenearest neighbour algorithm utilizing themetadata and calculated attributes of musictitles, and specially helps when the collection isvery large like in a typical radio station, since itnarrows the search space effectively. Formaldefinition ofRNS is given below.

RNS = N songs with min(TotalDistance(xi,xr))

Where, x=requested song and x.=all othersongs.

Totallnstancetx, ,x2)=I.distance( a Ij,a2j)Where a1i and a2j are the t attribute of XI andX2 respectively.

Secondly the algorithm introduces a newattribute of a song, playability, which derivesthe suitability of a song to be played at the

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moment of time considered. Playabilityfunction P is defined as follows.

P=P(release year, number. of times requestedJrecommended, last time played, time of theday).

Further, the algorithm addresses a set of globalconstraints that would cover variousrequirements of any radio station as detailedunder constraints. Therefore, this is the firsttime that these two (CSP and similarity based)techniques are combined to develop a hybridAPG algorithm for radio stations.

Let S ;; playlist, P Ii! set of playable songs and dJ E D

Assign random songs from pnRNS to S

Until cost(S) is less than threshold

Foreaeh V,

Calculate cost Vi

Find v; whose cost Is the worst

For each song dj" Sand dj E P

vw~ dj

Calculate cosI(vw)

Locate d, with the lowest- cosl(vw~dJ)

Vw~d,

Find cosl(S)

Figure l. PlayGen algorithm

Experimental results

(0) bonnie & dide - beyonze - rap - 2002(1) give it 10me - nelly furtado - rock - 2007(2) you all dat - baha men - world - 2000(3) all rise - blue - rock - 2001(4) smack that - eminem - rap -2006(5) feel good Inc. - gorillaz - rock -2005(6) wijh oive - hilary duff - pop - 2007(7) because of you - ne-yo - mb - 2007(8) coco jumbo - mr president - pop - 2000(9) real girt - mutya buena - rock - 2007

Figure 2. Example play list

The evaluation method of the algorithm was toprovide a collection of playlists generated byPlayGen to two different sets of testers, to berated in a scale of 5 where score of I means"completely unsuitable to be played on radio"and a score of 5 means "perfectly suitable to beplayed on radio". Firstly, a professional playlistcomposer (E) at a radio broadcastingcorporation rated them by estimating thesuitability of them to be played on their radio

Proceedings of the Peradeniya University Research Sessions, Sri Lanka, Vo1.l2, Part II, 3dhNovember 2007

station, Secondly, a set of frequent radiolisteners (L) of the above station rated themconsidering their own satisfaction derived outof listening to them, relating to qualities likevariety etc. In a set of five playlists, theplaylists 1-4 were generated using all threecompulsory constraints (of which playlist 3 isdepicted in Figure 2) while playlist 5 wasrandomly generated using only C-ALL-DlFFRENT constraint. Figure 3 depicts theplaylist ratings from the test. All five playlistswere generated from a database containingsong attributes of 200 music titles. Most of theat~ibutes were metadata of songs like title,artist, album, and tempo, which were extractedfrom allmusic.com and id3v2 tags of the songs.

Discussionr---------~-------

Ratings for Playlists

5.00 ,..------=::---=--=,.-".,---go 4.00 +lilii}-l!l!lil-"---l!II!I---l!m---IMI--f~&~'~'1- ---

! 3.00 H!lI---lIIII--_IIi1__ __ -ra<J-Ij;i!-· ~~'lHJII__-__• ill •II 2.00 tlHllJ, H~f[«j...---I!c'\1H""i-m--..ilJ._,-

l!:::+m--i~l~~IW~I~~IL ! Ell-lll-IL !~,I :11~, "PI!Iyist1 P2Iylist21 PaYliSt3! l'aYist4! PayUstSI

__________________ J

Figure 3. PlayGen ratings

Figure 2 depicts a typical playlist generated byPlayGen. All three compulsory constraints ofthis playlist are satisfied 100%.The thresholdvalue used was zero and PlayGen took only 1second in a typical personal computer (PC) toproduce the above result. As evident fromFigure 3, all playlists (except playlist5, whichwas randomly generated) have received verygood average ratings (higher than 4.3 out of 5)from both E and L, which concludes thatPlayGen is capable of generating qualityplaylists that significantly satisfies the radiostation requirements and listener preferences atthe same time, even with just three carefullydeveloped constraints.

Conclusions

In this paper the authors considered theautomatic play list generation problem. For thefirst time, two most popular methods, namely

constraint satisfaction approach and thesimilarity based techniques, of playlistgeneration, are combined to develop anautomatic playlist generator, which would suitradio stations. The developers were able to testand prove that it is feasible to generate qualitymusic playlists using a computer.

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