CHALLENGES AND METHODOLOGY
VISUAL REPRESENTATIONS
The vast majority of musical examples in this dissertation are not represented in musical notation. Rather, I use spectrograms and form timelines generated with the aid of the Sonic
Visualizer and Variations Audio Timeliner software, respectively. Using these tools, I am able to visually represent the aspects of this music that are most relevant to this dissertation. The
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Form Timelines
In addition to the traditional defining traits of sections in pop songs (e.g., verse, chorus), sections and subsections are also defined by changes in sonic texture. Thus, it is helpful for me to represent the form of a song as a timeline graph of a specific recording, allowing a relatively condensed formal overview of an entire song as a single image, and simplifying reference to sections in the song. This facilitates discussion of the relation between sections when musical notation is not possible. It also makes it easy to compare songs and to identify formal trends within the genre, as well as similarities of typical formal structures between genres.
Example 2.4 shows a formal breakdown of Ariana Grande’s “Break Free” (2014). Each section is represented by a yellow bubble, and those are grouped by orange bubbles to represent the larger-scale structure of the song. Some sections, which are longer and feature internal sonic changes, are subdivided using blue bubbles to indicate these changes.
Example 2.4. Ariana Grande, “Break Free” (2014), featuring Zedd. Form timeline.
Spectrograms
Spectrograms visualize the sonic activity in an audio file over time. In particular, they show the relative amplitude of frequencies at any given time, and can therefore illustrate changes
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in sonic density, the relative brightness of certain sections, and gradual processes, such as filter sweeps, among other things. I use two types of spectrograms to visualize sonic activity – the
plain spectrogram and the melodic range spectrogram. In both types, the horizontal axis represents time (in minutes:seconds), the vertical axis represents frequency in Hz, and the color brightness at each point represents the relative amplitude of each frequency at each time point. The difference between the two types of spectrograms is in the range of frequencies that they cover, and in the type of sonic activity that they show.
The plain spectrogram covers more or less the entire audible range of frequencies. As certain frequencies gain amplitude (i.e., become louder), they are represented by brighter colors on the spectrogram, changing from dark green to bright green, then yellow, and finally orange and red. I use the plain spectrogram to show overall changes in sonic density between and within sections, the relative presence of certain frequency bands in a given sonic texture, and processes such as filter sweeps and other notable timbral transformations.
The melodic range spectrogram represents frequencies up to just under 2 kHz (around B6), in order to focus on the fundamental tones of the sounds in the mix. It is easier to pick out the sonic activity of single sounds or tracks by using the melodic range spectrogram, because it has a much higher amplitude threshold – frequencies below a distinctly-audible amplitude level are not represented in the melodic range spectrogram. The color scheme of the melodic range spectrogram includes blue (lowest amplitude above the threshold), red, and yellow (highest amplitude), as well as a black background that encompasses all of the frequencies that did not reach the amplitude threshold. I use the melodic range spectrogram primarily as a pseudo-score, representing the activity of individual instruments/sounds along with their presence in specific regions of the sonic spectrum. For example, vertical lines in the low register that resemble flames
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often represent loud kick drum hits, while horizontal lines in higher registers can represent melodic material.
Example 2.5 shows both a melodic range and plain spectrogram of the bridge (2:49-3:23) from the aforementioned “Till the World Ends.” In the melodic range spectrogram (Example 2.5.a) we can clearly see a downwards bass glissando at the beginning of the section, heightened snare drum activity at around 3:00, and the bass and vocals between 3:07 and 3:23 (notice the keyboard diagram to the right of the frequency scale, which shows the correspondence of notes to frequencies). By contrast, it is difficult to see these details in the plain spectrogram illustrated in Example 2.5.b, but much easier to see the general timbre of the mix become darker after 2:49, and then gradually brighter and louder over the course of a filter sweep riser, starting at 3:07. In both spectrograms, it is easy to see a pause in sonic activity just before the drop at 3:23, as all the sounds are muted, leaving only the vocals as a pickup to the final chorus.
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a) Melodic Range Spectrogram. Activity of individual sounds/instruments is visible, due to the more limited frequency range and higher amplitude threshold for visibility.
b) Plain Spectrogram. Large scale timbral changes and processes are visible. The entire audible frequency spectrum is included, and the lower amplitude threshold allows harmonic partials, which are not heard as independent sounds but affect the timbre, to be represented.
Example 2.5. Britney Spears, “Till the World Ends,” (2011), 2:49-3:23. Functions of the plain and melodic range spectrograms.
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How I Use Spectrograms and Form Timelines
On occasion, I use each of the above methods of representation separately. I use the form timeline as a reference in discussions that include multiple sections of a song. I use one type of spectrogram or the other when I want to show a very specific detail, though most of my
examples include both the plain and melodic range spectrograms positioned above a time axis. In many cases they are combined with a form timeline. For instance, Example 2.6 shows a riser- drop sequence from Alesso’s “Heroes” (2014), which I discuss in Chapter 4. Here, I did not include a form timeline because the process only spans a single section. By contrast, when discussing processes that occur over a number of sections or an entire song, I position a form timeline above the spectrograms, showing the correlation between the section changes and sonic processes. Example 2.7 shows such a representation of Ariana Grande’s “One Last Time” (2014), taken from Chapter 5.
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Example 2.6. Alesso, “Heroes,” (2014), 0:53-1:10. High frequencies gain more amplitude (as indicated by the colors becoming progressively brighter in the plain spectrogram,) while the kick drum and bass are gradually obscured (as indicated by the steadily reduced presence of “flames” in the low register of the melodic range spectrogram). This forms a riser, which leads to a drop at ~1:08, represented by the return of the flames (in the MR spectrogram) and bright yellow color (in the plain spectrogram).
36 SUMMARY
Most of the methods presented in this chapter were born of a growing awareness of the syntactical dominance of sound production in contemporary pop music, as well as from the need to incorporate technology when analyzing this music. Music theory scholars have alluded to the challenges theorists have faced in their attempts to analyze timbre (Spicer 2005, par. 14; Blake 2012, par. 2.1). However, modern technology allows record producers nearly-unlimited control over timbre, which they have used to create new forms of musical expression, and the increasing user-friendliness of DAWs and sonic visualizers allow timbre to be much more analyzable than it was in the past. This dissertation takes advantage of these technological developments in order to illuminate the musical innovation represented in today’s commercial pop music.
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