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(MUSIC) (MUSIC) (MUSIC) (MUSIC) (MUSIC) (MUSIC) (MUSIC) (MUSIC) conventional 　 　 　 recommender 　 　 systems 　 are roughly categorised into collaborative filtering and content 　 　 　 based 　 　 filtering 　 　 collaborative filtering 　 　 recommends one you is a of p. is as a i taken into account or how someone else has read it uh and about this approach has been used and many e-commerce 　 so of this as uh at the all of can ex an interesting in rick um and a show is know you can't and based filtering rick am at to musical case as bite automatically at a i see it the condoms up music we've how an knee of the rating system what the accuracy of making recommendations as a limited oh the f. for we proposed to hybrid music we um and a system at come by as them their is of both approaches but is c. in example of the hype prick it needs if we come and h. and system for example we will try to may a weak come and a shins to do is a more or an nah me a fan a likes the is to peace it's for lab or to if that are we we commence familiar a some is than the top five rankings well as cells i we calm and at half the top five for i can condom based filtering all so does not hide to rick am end bo us song's who in mood map to use as musical taste hi per killed a ink can make um more act with recommendation including both i'm more or an i i the at and but less uh you know your a soon that to was or a like john were as and then take into account the rate things and the content of musical eases of when you lisp in to music an a fact of modelling of this process we all at the height to you s. c. of this recommendation system oh i and the future we but right to to develop a system at that can at patiently han of several millions peace is and he was served to find a sound at have to desire sinking voice from the large me see data base the vocal find a can do this time consuming task automatically i oh a uh and that an the same yeah the uh a okay this be a lot uh uh a a in you hmmm i i um i i uh that would be and a uh um for um uh um um uh uh i um and if we all and not a be cost we thought if would be the all can be any and if we could freak reece arm's you using a scene voice an at sick where or we develop a system ain't vocal find their i'll one in that that can extract features that expressed characteristic of a singing voice from using cull already a signals can ting both dis sin a noise and a come pattern to cells this as that enables hour system directory users pay ever it sounds uh the vocal fine an makes sit do easier to discover and new favourite is are um that you had to you know before you a all (JAPANESE) (JAPANESE) (JAPANESE) (JAPANESE) (JAPANESE) our research aims at exploring and achieving future music listening with more active and deeper 　 experiences 　 　 toward 　 this goal i propose a new approaches 　 in 　 three domains music playback music customization 　 and 　 music discovery for example first 　 for music playback our research enables a 　 user to skip sections of no interest to jump to 　 and listen 　 from the chorus section or 　 click on a word in the lyrics shown 　 on a screen to jump to and listen 　 from that 　 word (JAPANESE) second for music 　 customization 　 　 　 our research enables 　 a 　 　 novice user 　 to personalise available 　 music 　 　 recordings by 　 easily changing the volume and timber of sounds of recorded musical instruments such as drum sounds (JAPANESE) third for music discovery our research enables a 　 　 user to 　 find songs 　 　 having favourite vocal 　 timbres or we can build a system that infers 　 　 user's 　 musical taste and 　 recommends interesting songs i named the series of these approaches active 　 music 　 　 　 listening 　 interfaces and we will lead 　 this research in 　 various ways musical instrument equalise or is a novel musical all you'll play or use as can listen to musical pieces quite changing a lot nicer of each musical instrument pored and not the frequency bands i ah it it a all of of musical instrument equalise accommodate to user's music listening in needs to just changing them mead of musical pieces is and listening only need to a specific musical instrument for instance a the user as kind the range the favoured piece of injure rock stile by boosting the guitar or and the rhymes it or in to of a lot stile by bruised to a of a c. i. e. e. a. o. e. o. k. o. l. a. p. e. a to or the or or uh i i what to to uh a to uh the you a you uh oh okay here are the uh of t. of this research is in the u.s of a sci what's so was that are a schnapps that uh buy integrating they harmonic and in a how uh extract a rest to represent all musical instrument sounds what a for next we all plan to incorporate this technology for the musical instrument a all cos system and the music retrieval system lee what introduce it music play a what the unique technology sound of at which can we placed to ember of each musical instrument pod by painting using colour pressure is a buttons since we can you think of changing in the for ten are of musical instrument ass using a different colour palette ike call the system at so uh oh well we'll be on conventional met a that i need to pitch an duration of musical instrument knot the is in just some so owns use in these methods are different from the saw a one's exceeded but a real musical instrument as a please medals i don't think it can put characteristic are we constructed a mathematical model based on the like a acoustics so that it can cover it can put characteristics i thank a is um uh a with can there t. thing i to know a or uh a or is a it a um it oh uh a a that a musical category i'll off a medal music by remiss is to convert all of music leave medal temper so that everybody will enjoy am more middle music