Computational Models of Music Similarity and their ... - OFAI

34 2 Audio-based Similarity Measures
Blue Rondo A La Turk
86.4
Kathy’s Waltz
80.9
Bad Medicine
38.6
10
20
30
0
−39.5
0
−28.4
0
−9.1
Bring Me To Life
37.4
Someday
85.1
Bolero
43.0
10
20
30
10 20 30
0
−13.3
10 20 30
0
−13.6
10 20 30
0
−13.7
Figure 2.12: Full covariance matrices for 6 songs (G1). On both axes the dimensions
are Mel frequency bands. The dimension of the gray shadings is dB.
the problem pieces had only little variance in their spectra. For example,
one of them was very short (30 second) and calm. Such cases can easily be
identified and excluded (e.g., all pieces can be ignored which have a value
larger than 10 10 in the inverse covariance).
Illustrations
Figure 2.12 shows the covariances for the 6 songs used in previous figures. As
can be seen, there is a lot of information besides the diagonal. Noticeable are
that the variances for lower frequencies are higher. Furthermore, for some
of the songs there is a negative covariance between low frequencies and mid
frequencies.
Figure 2.13 shows the same plots for G1 which were already discussed for
G30 and G30S in Figures 2.10 and 2.11. Since G1 uses only one Gaussian,
there is only one line plotted in the second row. Noticeable, is also that there
are more lines visible in rows 4 and 5. This indicates there are fewer frames
(sampled or original) which have much higher probabilities than all others.
Otherwise, in particular the third and last row are very similar to those of
G30 and G30S and indicate that the models are very similar.
2.2.3.5 Computation Times
The CPU times for G30, G30S, and G1 are given in Table 2.1. The frame
clustering (FC) time is less interesting than the time needed to compute the
cluster model similarity (CMS). While FC can be computed offline, either all
possible distances need to be precomputed (and at least partially stored) or