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  Music Analysis
The first step in the process for our technologies is to analyze a 
large amount of music. In conjunction with our partner Loudeye (see 
Partners) we have access to the audio content of more than 250,000 
CDs.
The analysis process measures a number of physical characteristics of 
the audio, including such parameters as brightness and tempo and how 
these parameters change over time. The characteristics we measure 
have been identified in user testing to be the ones that produced the 
strongest reaction in testers. Often the characteristics are detected 
unconsciously by the listener, and the mix of parameters is more 
important than any individual parameter.
HSS
Our HSS (Hit Song Science) technology takes the analyzed data and 
overlays extra parameters relating to the commercial success of the 
music. These parameters are data such as total sales, highest chart 
position, date of release and others.
Using this extra dimension, new releases, potential releases and even 
unsigned acts can be compared with the database to allow a record 
label to see how well it fits into the current market and to identify 
potential hits. As the market changes, the system reflects this by 
finding new patterns in the hit clusters and applying these to the 
process.
The system allows for trends to be identified as they develop over 
time, meaning that a song that contains strong characteristics that 
are becoming more prevalent in new music and less of the 
characteristics that are diminishing can be identified as having high 
potential. Simply put this means that a song that sounds uncommercial 
to a human listening to it right now may just be ahead of its time 
and in fact contains the right ingredients to appeal to the CD-buying 
public a few months from now.
Music Recommendation
Once we have a large selection of music analyzed, there are two keys 
ways to recommend music to an individual user. One simply links a 
song or, more interestingly, a group of songs to a selection of music 
that has a similar profile. This technique takes the individual 
profile of the song or songs and matches it to the whole catalogue of 
music in the database. So given a list of songs, each can have a 
"more like this" link to similar music.
The second way to capture a user's own personal taste profile is to 
allow them to take a "music taste test". In this process, the user 
will be presented with a number of binary choices between two short 
audio clips and will choose the clip they prefer. After a series of 
these questions, it is possible to generate a profile for that user; 
the profile is analagous to a song's own profile, as measured in the 
analysis phase. So in this way songs from the database that share 
commonalities to this profile can be identified and presented to the 
user to preview.
In a retail environment, both the "more like this" and the "music 
taste test" can be efficiently presented on in-store terminals, or on 
a retail website. The same technique could be applied to many other 
situations, such as automatically recommending songs from a personal 
collection as a playlist, or anywhere that commonalities between 
pieces of music can be useful.
In both cases, results can further be customized as the user can 
select to receive music matches across genres, from a given epoch, 
new releases or other customizable variables.

Copyright =A9 2002, 2003 Polyphonic HMI S.L.
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<div><font face=3D"Geneva" size=3D"+1" color=3D"#000000"> Music
Analysis<br>
The first step in the process for our technologies is to analyze a
large amount of music. In conjunction with our partner Loudeye (see
Partners) we have access to the audio content of more than 250,000
CDs.<br>
The analysis process measures a number of physical characteristics of
the audio, including such parameters as brightness and tempo and how
these parameters change over time. The characteristics we measure have
been identified in user testing to be the ones that produced the
strongest reaction in testers. Often the characteristics are detected
unconsciously by the listener, and the mix of parameters is more
important than any individual parameter.<br>
HSS<br>
Our HSS (Hit Song Science) technology takes the analyzed data and
overlays extra parameters relating to the commercial success of the
music. These parameters are data such as total sales, highest chart
position, date of release and others.<br>
Using this extra dimension, new releases, potential releases and even
unsigned acts can be compared with the database to allow a record
label to see how well it fits into the current market and to identify
potential hits. As the market changes, the system reflects this by
finding new patterns in the hit clusters and applying these to the
process.<br>
The system allows for trends to be identified as they develop over
time, meaning that a song that contains strong characteristics that
are becoming more prevalent in new music and less of the
characteristics that are diminishing can be identified as having high
potential. Simply put this means that a song that sounds uncommercial
to a human listening to it right now may just be ahead of its time and
in fact contains the right ingredients to appeal to the CD-buying
public a few months from now.<br>
Music Recommendation<br>
Once we have a large selection of music analyzed, there are two keys
ways to recommend music to an individual user. One simply links a song
or, more interestingly, a group of songs to a selection of music that
has a similar profile. This technique takes the individual profile of
the song or songs and matches it to the whole catalogue of music in
the database. So given a list of songs, each can have a "more
like this" link to similar music.<br>
The second way to capture a user's own personal taste profile is to
allow them to take a "music taste test". In this process,
the user will be presented with a number of binary choices between two
short audio clips and will choose the clip they prefer. After a series
of these questions, it is possible to generate a profile for that
user; the profile is analagous to a song's own profile, as measured in
the analysis phase. So in this way songs from the database that share
commonalities to this profile can be identified and presented to the
user to preview.<br>
In a retail environment, both the "more like this" and the
"music taste test" can be efficiently presented on in-store
terminals, or on a retail website. The same technique could be applied
to many other situations, such as automatically recommending songs
from a personal collection as a playlist, or anywhere that
commonalities between pieces of music can be useful.<br>
In both cases, results can further be customized as the user can
select to receive music matches across genres, from a given epoch, new
releases or other customizable variables.<br>
<br>
Copyright =A9 2002, 2003 Polyphonic HMI S.L.</font></div>
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