Category: Music Education

Abstract

The purpose of this interdisciplinary literature review is to examine the current research on the subtleties of rhythm from different perspectives. This review will draw from the research of ethnomusicology, music perception, psychoacoustics, psychology, neuroscience, electrical engineering, and music education. This paper will provide an overview of rhythmic notation, ethnomusicology, music cognition, music technology, and music teaching & learning as they pertain to complex topics such as meter, subdivision, polyrhythm, stylistic nuance, and groove.

Rhythm and Notation

Music notation is not meant to be an exact dictation of rhythm, but rather an approximation or interpretation of what one hears or wants another musician to perform. Since humans are not machines, it is impossible to play every note perfectly even, so human performances have some slight variation in temporal accuracy. Human-performed rhythms have natural fluctuations and do not always align with a metronome, particularly the inner beats. Even though rigorous practice is required to achieve near-perfect timing and rhythm, some music is expected to be played uneven and imperfect. Musical interpretation involves accents, dynamics, and purposeful tempo fluctuations that are often not written down. Musicians in a large ensemble are also required to adapt their time to that of the conductor’s baton.

Rhythm Perception and Cognition

Temporal expectancies occur when one’s brain decomposes a pattern, however complex, and looks for the same or similar pattern in the near future (Desain, 1992). With these expectations, we can decipher rhythm, tempo, and meter. Our brains do this automatically to the extent where we try and create patterns where none may exist, such as looking at random numbers or abstract art (Potter, Fenwick, Abecasis, & Brochard, 2008).

Rhythm is important because our brains are hardwired to respond and synchronize with rhythmic patterns (Thaut et al. 2009). Our brains’ motor control functions naturally respond to temporal structures, especially when the listener consciously monitors a rhythm. This explains why humans naturally tap their foot or finger, nod their head, or sway back and forth when listening to music.

Our brains have trouble separating all the layers that we hear from complex rhythms and meters. We have the ability to focus on one sound or rhythmic sequence at a time and ignore all other sounds by treating them like "noise." This process, called selective masking, is similar to when one can block out the sound of an air conditioner whirring in the background and focus on only the person speaking in the room. Another way we decipher complex sounds is by creating a composite pattern that combines all of the layers that are similar (Poudrier & Repp, 2013).

Swing and Stylistic Nuance

Music is filled with natural rhythmic imperfections that are often desirable within a style. A jazz swing feel can change depending on tempo, style, and performer. A standard jazz swing pattern is notated as a dotted-eighth sixteenth, a triplet, or as eighth-notes, when in practice, it is constantly fluctuating and lies somewhere in between those rhythms. In 16th Century French Baroque music, this swing feel is known as notes inégales.

Music Technology and MIDI

Music Instrument Digital Interface (MIDI) data does not contain or produce any sound, but is rather a roadmap of when, where, and how notes from a synthesizer or sampler should be played. There have been many advancements made in varying the samples used including round robin sampling, articulations, ambience, instrument noises like key clicks and buzzing sounds, and performance analysis to account for pull-offs or alternate strings. Quantization is when notes, either MIDI or digital audio, are snapped to a metronomic grid with standard subdivisions. Groove quantization occurs when those notes are moved to a template that differs from the metronome. The templates can be made from a human performance or by manually moving each subdivision within a measure. This allows MIDI data to be played behind the beat or ahead of the beat on purpose to imitate the temporal qualities of a human performance.

Implications for Music Education

It is possible that the advancements made programming a computer how to adjust time between the beats can influence how music educators teach rhythmic accuracy. Subdivision is discussed regularly in classrooms, but not necessarily how to control it and apply that subtleness to musical performance, especially in different styles of music. The better we can understand our brain and how it processes and perceives rhythm, the better software we can program in the future that can then assist us in the future of music creation and education.

References

Abramson, R. M. (1980). Dalcroze-based improvisation. Music Educators Journal, 66(2), 62-68.

Anderson, W. T. (2011). The dalcroze approach to music education: Theory and applications. General Music Today, 26(1), 27-33.

Benadon, F. (2006). Slicing the beat: Jazz eighth-notes as expressive microrhythm. Ethnomusicology, 50(1), 73-98.

Bengtsson, S. L., Ullén, F., Ehrsson, H. H., Hashimo, T., Kito, T., Naito, E., . . . Sadato, N. (2009). Listening to rhythms activates motor and premotor cortices. Cortex, 2009(45), 62-71.

Busse, W. G. (2002). Toward objective measurement and evaluation of jazz piano performance via midi-based groove quantize templates. Music Perception: An Interdisciplinary Journal, 19(3), 443-461.

Butterfield, M. (2010). Participatory discrepancies and the perception of beats in jazz. Music Perception: An Interdisciplinary Journal, 27(3), 157-176.

Desain, P. (1992). A (de)composable theory of rhythm perception. Music Perception: An Interdisciplinary Journal, 9(4), 439-454.

Gordon, E. E. (2012). Learning sequences in music: A contemporary music learning theory. Chicago, IL: GIA Publications.

Grahn, J. A., & Brett, M. (2007). Rhythm and beat perception in motor areas of the brain. Journal of Cognitive Neuroscience, 19(5), 893-906.

Honing, H., & Bas de Haas, W. (2008). Swing once more: Relating timing and tempo in expert jazz drumming. Music Perception: An Interdisciplinary Journal, 25(5), 471-476.

Keil, C. (1987). Participatory discrepancies and the power of music. Cultural Anthropology, 2(3), 275-283.

Keil, C. (1995). The theory of participatory discrepancies: A progress report. Ethnomusicology, 39(1), 1-19.

London, J. (2012). Hearing in time: Psychological aspects of musical meter (2nd ed.). Oxford, UK: Oxford University Press.

Poudrier, È., & Repp, B. H. (2013). Can musicians track two different beats simultaneously? Music Perception, 30(4), 369-390.

Potter, D. D., Fenwick, M., Abecasis, D., & Brochard, R. (2009). Perceiving rhythm where non exists: Event-related potential (erp) correlates of subjective accenting. Cortex, 45, 103-109.

Prögler, J. A. (1995). Searching for swing: Participatory discrepancies in the jazz rhythm section. Ethnomusicology, 39(1), 21-54.

Thaut, M. H., Stephan, K. M., Wunderlich, G., Schicks, W., Tellman, L., Herzog, H., . . . Hömberg, V. (2009). Distinct cortico-cerebellar activations in rhythmic auditory motor synchronization. Cortex, 45(2009), 44-53.

Zagorski-Thomas, S. (2007). The study of groove. Ethnomusicology Forum, 16(2), 327-335.

Abstract

The purpose of this descriptive study is to investigate the differences in microtiming discrimination – perception of rhythmic fluctuations at the millisecond level – between dancers, percussionists, and business students. Many popular musical genres have established grooves or feels, and experienced dancers express those grooves through movement. This study aims to produce correlative data that can provide insight for music educators when teaching music of different styles in their classroom. This study may help provide evidence supporting the use of popular music, multicultural music, and movement or dancing in the classroom so that students receive a well-rounded music education. I hypothesized that the discrimination accuracy of the dancers would be in between that of the percussionists and the business students. A sub-hypothesis included that experienced dancers will be more sensitive to microtiming deviations within their preferred musical style compared to inexperienced participants.

Research Questions

1. How do dancers’ auditory perception of microtiming deviations in drum grooves in various styles of music differ from percussionists and business students?
2. To what extent does dancing experience in a specific musical style affect the temporal resolution of the participant?

Background

Groove is defined as the innate feeling of wanting to move some part of your body along with the music (Davies, Madison, Silva, & Gouyon, 2013). Music cognition researchers have documented the neurological relationship between movement and musical rhythms (Bengtsson et al., 2009; Grahn & Brett, 2007; Leow, Parrott, & Grahn, 2014; Loehr & Palmer, 2009; Thaut, 2009; Thaut, Trimarchi, & Parsons, 2014; Trainor et al., 2009; Witek et al., 2014). Researchers have shown that percussionists have a higher rhythmic sensitivity than nonmusicians (Davies, Madison, Silva, & Gouyon, 2013; Rammsayer & Altenmüller, 2006; Rammsayer, Buttkus, & Altenmüller, 2012). However, research on the audiomotor entrainment of dancers is still in its infancy. Typically, dancers may or may not produce movements that synchronize with the overall pulse of the music (Kotz, Ravignani, & Fitch, 2018). It is the researcher’s interest to understand the rhythmic perception of dancers in order to make connections between movement, music cognition, and music education.

The National Association for Music Education (2014) have rhythmic components for all grade levels, and by the 8th grade, it recommends that music students be able to sight-read notation and understand the cultural and historical context of the music to inform with performance. Many of the popular approaches to music teaching include elements of learning rhythm through movement. Dalcroze Eurythmics uses body motion and rhythmic solfege to develop a student’s inner ear, to internalize rhythmic expression, and to develop a foundation for improvisation (Abramson, 1980; Anderson, 2011; Caldwell, 1993; Juntunen & Hyvönen, 2004; Mead, 1994). Orff Schulwerk can help develop students’ understanding of rhythm through group activities using rhythmical body movements, such as clapping or stamping (Shamrock, 1986). The Kodály method infuses games, dances, and play to represent musical lines, in addition to using body percussion (Bowyer, 2015). Music learning theory stresses the importance of using movement to develop rhythmic audiation (Gordon, 2012). Contemporary music education researchers can use cognitive and neurological music research to inform their instruction (Flohr, 2010).

Methodology

Participants

In order to find participants with experience moving to specific musical styles, volunteers from various dance classes at a major university were recruited to participate in the study. A group of percussionists was recruited from the music school to help establish the upper bound for the auditory perception test created for this experiment. A group of business students served as a control group that would ideally have less experience in music or dance instruction.

Materials

The stimuli consisted of 48 four-measure drum samples: 12 unaltered drum samples, three samples in four different musical styles, plus three alterations of each drum sample. The altered drum samples differed only in the 16th note swing ratio with microtiming deviations of plus or minus 10ms, 20ms, and 40ms. Musical styles included Latin, jazz, and hip-hop, as well as snare drum patterns.

To create the altered drum samples, the researcher spliced stylized drum loops from either Logic Pro X or Loopmasters.com at each 16th note transient. All of the drum samples were time-shifted to 130BPM so that the 40ms deviations were sufficiently noticeable across the different musical styles. The snare drum patterns were recorded live, quantized to the metronomic grid, and then edited for the variations. Then, using Logic Pro X, the second and fourth 16th note of each beat was moved plus or minus 10ms, 20ms, or 40ms, depending on whether the sample already contained swung or straight rhythms. The researcher did not edit the 8th note swing variation within each stylized groove. In other words, only the upbeat 16th notes of each beat moved, adding swing to straight samples and removing swing to already swung samples; the downbeats and upbeats remained the same. The audio edit points at each transient were crossfaded manually to remove any digital artifacts. A subtle 80ms reverberation sound effect was added to each sample to help conceal the edit points.

Procedure

The subjects participated in an A-Not-AR with Sureness discrimination task with a familiarization process in order to reduce response bias when indicating the confidence in their answers (van Hout, 2014). The subjects compared the 12 unaltered drum samples with the three microtiming alterations of each sample in a random order within blocks based on different musical styles. For each sample pair, the original, unaltered sample was always presented first as a reminder, followed by either the identical sample or one with a microtiming deviation.

Subjects sat at individual computers and listened to stimuli via headphones. They registered their responses through an online program designed on www.surveygizmo.com. Subjects had to determine if the second audio sample was "A" or "Not A" and indicate their level of confidence by deciding if they were sure, not sure, or guessing their answer for a total of 6 ordinal variables. The subjects also answered background questions about their music listening preferences, musical instruction and performance experience, and dancing instruction and performance experience. As an incentive, the participants were able to enter in a drawing for a gift card. The overall procedure took approximately 25 minutes.

Discussion

The analysis indicates that the percussion group was able to perceive the 40ms microtiming deviations with more confidence than the other two groups, supporting previous research on the superior rhythmic acuity of percussionists (Davies, Madison, Silva, & Gouyon, 2013). However, due to high variance and small sample size, a significant difference could not be established between the performance of the dancers and the other two groups. There is a statistically significant, positive, and moderate correlation between the dance group’s self-reported swing dancing experience and their composite mean of -10ms jazz variations. r(n=11) = .559, p = .001. It makes sense that the correlation weakens as the alterations become more obvious with the 20ms and 40ms microtiming deviations. However, the results were not consistent with the hip-hop and Latin samples.

Additional analysis is required to determine the difficulty of each drum sample. The snare drum and hip-hop samples may have been easier to perceive for some groups because of the clear separated attacks of the rhythms. The alterations in the jazz samples may have been difficult to perceive due to the constant ringing of the ride cymbal and that the fact that the swing feel was being subtracted rather than added. The Latin drum samples may have been difficult due to rhythmic density added from the congas and a shaker on top of the drum set. Future studies may want to compare only single instruments in different styles performing linear rhythm patterns for a more accurate comparison.

Tempo may also be a factor in the performance of the groups and the quality of the samples as 130BPM may not be the most ecologically valid tempo within each style and pattern. Future studies may want to test the effect tempo plays on the perception of microtiming deviations.

As research on beat perception and synchronization of dancers is still in its infancy, this pilot study helps lay the groundwork for future investigations on this topic. Auditory rhythmic perception of dancers is a complex topic since they may or may not produce movements that synchronize with the overall pulse of the music (Kotz, Ravignani, & Fitch, 2018). Similar studies may be expanded with a larger sample size and use different discrimination tasks. Future studies can add additional groups for comparison, such as "musician, non-percussionist" or "dancers with musical instruction experience," as well as more styles of music that correspond with appropriate dancing styles. An experimental study can be designed to compare microtiming discrimination before and after dancing instruction in specific styles. Further research will be required to understand the differences in the cognitive decision strategy used by percussionists versus less rhythmically experienced groups, as well as the effect that audiation has on discrimination ability. Overall, this topic is ripe for exploration in the music perception and cognition realm and may produce data that is relevant to music teaching and learning.

References

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Anderson, W. T. (2011). The dalcroze approach to music education: Theory and applications. General Music Today, 26(1), 27-33.

Bengtsson, S. L., Ullén, F., Ehrsson, H. H., Hashimo, T., Kito, T., Naito, E., . . . Sadato, N. (2009). Listening to rhythms activates motor and premotor cortices. Cortex, 2009(45), 62-71.

Bowyer, J. (2015). More than solfége and hand signs: Philosophy, tools and lesson planning in the authentic kodály classroom. Music Educators Journal, 102(2), 69-76.

Caldwell, T. (1993). A dalcroze perspective on skills for learning. Music Educators Journal, 79(7), 27-29.

Davies, M., Madison, G., Silva, P., & Gouyon, F. (2013). The effect of microtiming deviations on the perception of groove in short rhythms. Music Perception: An Interdisciplinary Journal, 30(5), 497-510.

Flohr, J. W. (2010). Best practices for young children’s music education: Guidance from brain research. General Music Today, 23(2), 13-19.

Gordon, E. E. (2012). Learning sequences in music: A contemporary music learning theory. Chicago, IL: GIA Publications.

Grahn, J. A., & Brett, M. (2007). Rhythm and beat perception in motor areas of the brain. Journal of Cognitive Neuroscience, 19(5), 893-906.

Juntunen, M., & Hyvönen, L. (2004). Embodiment in musical knowing: How body movement facilitates learning within Dalcroze Eurhythmics. British Journal of Music Education, 21(2), 199-214.

Kotz, S. A., Ravignani, A., & Fitch, W. T. (2018) The evolution of rhythm processing. Trends in Cognitive Sciences, 22(10), 896-910.

Leow, L., Parrott, T., & Grahn, J. A. (2014). Individual differences in beat perception affect gait responses to low- and high-groove music. Frontiers in Human Neuroscience, 8(811), 1-12.

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Thaut, M. H., Stephan, K. M., Wunderlich, G., Schicks, W., Tellman, L., Herzog, H., . . . Hömberg, V. (2009). Distinct cortico-cerebellar activations in rhythmic auditory motor synchronization. Cortex, 45(2009), 44-53.

Thaut, M. H., Trimarchi, P. D., & Parsons, L. M. (2014). Human brain basis of musical rhythm perception: Common and distinct neural substrates for meter, tempo, and pattern. Brain Sciences, 4, 428-452.

Trainor, L. J., Gao, X., Lei, J.-j., Lehtovaara, K., & Harris, L. R. (2009). The primal role of the vestibular system in determining musical rhythm. Cortex, 45, 35-43.

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Music Teaching and Learning Through Creative Musical Activities: A Technological, Pedagogical, and Content Knowledge Case Study

Abstract

Use of technology in music teaching and learning has evolved rapidly in recent years. Creative musical activities (CMAs), such as composition, arranging, and improvisation, are part of the National Core Arts Standards for music. Technology-based music classes (TBMCs) provide music composition opportunities for music students. However, music teachers indicate that a lack of time, confidence, and knowledge may prevent them from incorporating CMAs (Piazza & Talbot, 2021) or music technology (Bauer, 2012; Bauer & Dammers, 2016; Dorfman, 2013; Gall, 2013) into their curricula. Music educators require a deeper understanding of how technology influences CMAs so they may approach TBMCs in a pedagogically sound way. The purpose of this intrinsic case study is to document how one teacher and their students use technology to facilitate CMAs in an exemplary high school TBMC. Research questions guiding this study include: 1) What are the goals of this class? 2) What characterizes and enables technology used in this class? 3) How is technology used to facilitate CMAs? and 4) How do the teacher and students feel about using technology to facilitate CMAs? Data collection included observations, analytic memos, semi-structured interviews, student reflections, student focus group(s), and classroom artifacts based on a 6-week creative music technology unit. After triangulating the data using a hybrid coding model, I present the emerging findings through the lens of the technological, pedagogical, and content knowledge (TPACK) conceptual framework.

Keywords: music technology, creative musical activities, music education, technology-based music classes, TPACK

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