More Than a Feeling: Some Technical Details of Swing Rhythm in Music

Introduction If you ask a musician what makes music swing, he will reply that Swing is a feeling, and may mention counting or subdividing the beat. Commonly, in classic Jazz for example, triplet note subdivision is a feature in Swing music, but this is not the entire story. Otherwise a waltz (3/4 meter), or a 6/8 or 12/8 meter piece would inherently swing. Some pieces do, and some do not. There are also musical examples that one knows intuitively have Swing, but on close analysis do not appear to have triplet subdivision either as the main or only feature that contributes to the Swing. In this article, the authors presume that accent (differences in loudness between note events) also contributes to Swing, but thus far our research has focused solely on the timing aspects of swing rhythm. One aspect of Swing is interpreted to be the changes in the rhythmic structure around a solid and precise beat. It is the variations in that structure that are swinging. Since classic Jazz is not the only representative of Swing, the authors want to extend the definition of Swing to include all musical styles that might be considered to “swing” by some valid metric, e.g., the musicians or dancers think the music is swinging. An ad hoc cultural definition rather than a technical definition is used to describe Swing: it is a property of music as played which causes listeners to dance or otherwise move their bodies in a cyclical, energetic, rhythmic manner. This definition allows consideration of a broader range of music than most prior research into Swing rhythm as well as to distinguish between Swing and other types of rhythmic expression. Rhythmic expression is the parent category of Swing, and includes many examples of differences in music as played compared to the strict metronomic timing that is specified in the written form of music. This rigid structural framework is referred to as Mozart-Bach or MB notation due to its historical origins. It is not implied that European Classical music is only played strictly by the metronome, however, this mind set is quite common in the training of musicians in the Western academic tradition. The real world of music as performed is more complex and interesting than the mechanistic world of sheet music, just as a movie or stage play performance has more depth and expression than is apparent by reading the script. This article will provide a short summary of prior computer science research into Swing rhythm and the analysis methods used will be briefly described. Finally, the fun stuff—a detailed technical analysis of the timing variation for a variety of styles of Swing music will be given. Prior research Cholakis (1995) cataloged an extensive set of Jazz drummers and analyzed the statistical nature of how each musician swung the beat in a different style by extracting the ratio of temporal intervals for notes as played. He claimed that this analysis allowed MIDI sequencer music with a more “human” feel to be produced. Gabriellson (1987 and 2000) observed that rhythmic variation is almost universal in music performance and reports that listeners generally prefer music played with rhythmic expression than music played strictly by the metronome. This phenomenon applies to popular music, European Classical music, and non-European traditions, such as African and Middle Eastern music. Waadelund (2004) has linked swing style to body movement, and used video recordings to study the body english of drummers in order to correlate their movements to the rhythmic style being played. Friberg and Sundstrom (1999 and 2002) extended Cholakis’ swing ratio work. Guoyon (2005) developed computational signal processing techniques to change the swing feel in a music sample. Hamer (2000) puts a cultural slant on Friberg’s and other’s research, as does Birch (2003). Several software companies have products aimed at training musicians to understand and play various types of Swing. In our research, extensive use was made of the standard spectrogram, i.e., the short time Fourier transform (STFT), for extracting the rhythms of different instruments. Fulop and Fitz (2006) describe a newly rediscovered form of the spectrogram which we consider to be a major advance in this information processing approach. The new spectrogram allows better time and frequency resolution for a given data set, and makes available information which is ignored in traditional uses of fast Fourier transforms (FFTs) and STFTs, such as instantaneous phase and frequency. Many prior researchers have analyzed rhythm by using statistical analysis of note events in musical samples. This can be a useful technique, but we assert that the performance of music, whether by human or computer, is not a statistical process. Rather, each note event relates to other note events in very specific ways, and metaphors other than statistical analysis, such as symbolic relationships or local measures of specific timing between note events, should be used as appropriate. An obvious example is the hierarchical timing relations between repetitive groups of note events at different time scales. This gives rise to common musical features such as meter, beat, and subdivision. Statistical analysis can be “The real world of music