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

16 2 Audio-based Similarity Measures
The ZCR is the average number of times the audio signal crosses the zero
amplitude line per time unit. Figure 2.1 illustrates this. The ZCR is higher if
the signal is noisier. Examples are given in Figure 2.2. The first and second
excerpts (both jazz) are close to each other with values below 1.5/ms, and
the third and fourth (both hard pop) are close to each other with values
above 2.5/ms. Thus the ZCR seems capable of distinguishing jazz from hard
pop (at least on these 4 examples).
The fifth excerpt is electronic dance music with very fast and strong
percussive beats. However, the ZCR value is very low. The sixth excerpt
is a soft, slow, and bright orchestra piece in which wind instruments play a
dominant role. Considering that the ZCR is supposed to measure noisiness
the ZCR value is surprisingly high.
The limitations of the ZCR as a noisiness feature for music are obvious
when considering that with a single sine wave any ZCR value can be generated
(by varying the frequency). In particular, the ZCR does not only
measure noise, but also pitch. The fifth excerpt has relatively low values
because its energy is mainly in the low frequency bands (bass beats). On
the other hand, the sixth excerpt has a lot of energy in the higher frequency
bands.
This leads to the question of how to evaluate features and to ensure that
they perform well in general and not only on the subset used for testing.
Obviously it is necessary to use larger test collections. However, in addition
it is also helpful to understand what the features describe and how this
relates to a human listener’s perception (in terms of similarity). Evaluation
procedures will be discussed in detail in Section 2.3.
Another question this leads to is how to extract additional information
from the audio signal which might solve problems the ZCR cannot. Other
features which can be extracted directly from the audio signal include, for
example, the Root Mean Square (RMS) energy which is correlated with loudness
(see Subsection 2.2.5). However, in general it is a standard procedure to
first transform the audio signal from the time domain to the spectral domain
before extracting any further features. This will be discussed in the next
subsection.
2.2.2 Preprocessing (MFCCs)
The basic idea of preprocessing is to transform the raw data so that the
interesting information is more easily accessible. In the case of computational
models of music similarity this means drastically reducing the overall amount
of data. However, this needs to be achieved without losing too much of the
information which is critical to a human listener’s perception of similarity.