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Ex. 1 Ex. 2 Ex. 3 Score Song Title - Artist
2 1 6 931.99 Fools Get Wise - B.B. King
8 8 1 876.10 Strange - R.E.M
1 7 7 837.14 Lonely Days - M.E.S.T.
3 2 4 625.93 Thief Of Always - Jaci Velasquez
9 4 9 372.41 Almost Independence Day - Van Morrison
7 6 3 343.87 Impossible - UB40
5 5 2 299.51 Try Me - Val Emmich
4 3 8 134.05 One Too Many - Baby Animals
6 9 5 131.38 Aries - Galahad
10 10 10 26.76 Walkin’ On The Sidewalks - Queens of the Stone Age
Table 11: Expected and application rankings for ten randomly selected songs.
LyricWiki Collection
Number One Hits
0 1000 2000 3000 4000
Figure 1: Boxplots showing the quartiles of lyric
scores for the lyric collection from the 1990-2010
era, and the corresponding set of number one hits
from the era.
hits are indeed more typical than other songs,
or perhaps that a song that reaches number
one influences other lyricists who then create
works in a similar style. Earlier attempts to
compare number one hits with the full collection
of lyrics revealed an increase in cliché
score over time for hit songs. We believe that
this was not so much due to an increase in
cliché in pop over time but that the language
in the lyrics of popular music changes over
time, as happens with all natural language.
6 Conclusion
We have explored the use of tf-idf weighting to
find typical phrases and rhyme pairs in song
lyrics. These attributes have been extracted
with varying degrees of success, dependent on
sample size. The use of a background model
of text worked well in removing ordinary language
from the results, and the technique may
go towards improving rhyme detection software.
An application was developed that estimates
how clichéd given song lyrics are. However,
while results were reasonable for distinguishing
very clichéd songs from songs that
are fairly free from cliché, it was less successful
with songs that are not at the extremes.
Our method of obtaining human judgements
was not ideal, consisting of two rankings
of ten songs by the research team involved
in the project. For our future work we hope
to obtain independent judgements, possibly of
smaller snippets of songs to make the task easier.
As it is unclear how consistent people are
in judging the clichédness of songs, we expect
to collect a larger set of judgements per lyric.
There were several instances where annotations
in the lyrics influenced our results. Future
work would benefit from a larger, more
accurately transcribed collection. This could
be achieved using Multiple Sequence Alignment
as in Knees et al. (2005). Extending the
model beyond trigrams may also yield interesting
results.
A comparison of number one hits with a
larger collection of lyrics from the same time
period revealed that the typical number one
hit is more clichéd, on average. While we have
examined the relationship between our cliché
score and song popularity, it is important to
note that there is not necessarily a connection
between these factors and writing quality, but
this may also be an interesting area to explore.
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