Agreement in Musical Experts on the Identification of Beat Levels and their Salience

Agreement in Musical Experts on the Identification of Beat Levels and their Salience

Schroeder, J., Simmons, G.

Yale University, Cognition of Musical Rhythm, Virtual Lab

1. BACKGROUND AND AIMS

1.1  Introduction

This experiment aimed to look at the salience of beat (or pulse) levels, or subdivisions, in certain songs. Salience is a measure of how perceivable each beat level is, and is made of up a number of different variables, including volume, timbre, and pitch. The purpose of studying the number of salient pulse levels, or subdivisions, was to explore whether their variance might affect the perception of a song’s groove. A pulse level is a steady beat in the music, a stream of musical events that happen in equal and predictable intervals, and has also been defined more anecdotally as a beat that you might feel compelled to tap along or move to. However, in many pieces of music, there are several possible pulse levels that one could focus on. We theorized, after reading Janata, Tomic, and Haberman (2011), that having more pulse levels accessible in the music might be connected with a higher groove rating. There are, of course, many different factors that make up the perception of groove; in this study we wanted to isolate this one factor as best as possible to see what the relationship is. [Genevieve]

1.2  Previous Research

Our initial inspiration was drawn from the “Sensorimotor Coupling in Music and the Psychology of the Groove” study by Janata, Tomic, and Haberman (2011). Many other studies have investigated the meaning of ‘groove’ and the rhythmic properties related to it, by comparing microtiming deviations (Gouyon, Hornstrom, Madison, Ullen, 2011) or just categorizing the prominent factors “regular-irregular, groove, having swing, and flowing” (Madison, 2006).

Methods included assessing correlations between listeners’ ratings and a number of quantitative descriptors of rhythmic properties for one hundred music examples from five distinct traditional music genres (Gouyon, Hornstrom, Madison, Ullen, 2011) and in terms of differences in ratings across sixty-four music examples taken from commercially available recordings (Madison, 2006).

Janata et al. explored the urge to move in response to music using phenomenological, behavioral, and computation techniques. Assuming that groove is a psychological construct, they posited that the “degree of experienced groove is inversely related to experienced difficulty of bimanual sensorimotor coupling under tapping regimes with varying levels of expressive constraint and that high-groove stimuli elicit spontaneous rhythmic movements” (Haberman, Janata, Tomic, 2011). [Genevieve]

1.3  Present Research

The questions we set out to attempt to answer was if the saliency of beat level pulses affected perceived groove rating of a set of songs with already-established groove ratings from the 2011 study of Haberman, Janata, and Tomic.

Our initial proposal was to have a panel of musical experts rate levels of beat levels in songs for confirmation and then have the ability to choose songs with a variety of beat levels before giving subsequent subjects songs to rate grooviness of, but because of difficulties in collecting data and inconsistencies between experts’ opinions, we have decided to use only the first part of our initially proposed project. [Genevieve]

2. METHOD

1.1  Participants

There were 5 participants, all students from the Yale School of Music, as well as one professor. The participants were contacted by email, and were not offered any sort of compensation. [Jordan]

1.2  Stimuli

The experiment consisted of a Qualtrics survey, built with the Qualtrics website, and contained fourteen 30 second excerpts of songs of various style and genre, which were supplied by Petr Janata, and had been used in Janata et al. (2011). Each of the fourteen excerpts constituted a trial, and the number of beat levels present in the song, the salience of each of those beat levels, and the primary instrument that contributed to the creation of each beat level were used as variables. Salience was rated on a scale from 0 – 10, and the labelling of instrumentation was left up to the subjects. The tempos shown below were found by Stefan Tomic and Petr Janata using the method described in Tomic & Janata (2008), and a few were halved, due to the fact that they were obviously associated with a faster metric levels. One song (Step it Up Joe) was excluded in later analyses due to a lack of information provided for us by Janata et al.

Song Artist Genre Tempo (bpm) Groove Rating
Superstition Stevie Wonder Soul 99 108.7
Yeah! Usher feat. Lil’ John & Ludacris Soul 211 89.7
Freedom of the Road Martin Sexton Folk 25 59.7
What a Wonderful World Louis Armstrong Jazz 36 66.4
Beauty of the Sea The Gabe Dixon Band Rock 63 32.1
Thugamar Fein an Samhradh Linn Barry Phillips Folk 33 29.3
The Child is Gone Fiona Apple Rock 195 62.3
Mama Cita (Instrumental) Funk Squad Soul 95 101.6
Citi Na GCumman William Coulter & Friends Folk 20 35.2
Summertime Ella Fitzgerald & Louis Armstrong Jazz 99 67.9
Goodies Ciara feat. Petey Pablo Soul 50 92.3
Step it Up Joe Mustard’s Retreat Folk X X
In the Mood Glenn Miller & His Orchestra Jazz 162 96.9
Squeeze Robert Randolph & The Family Band Rock 58 63.4

Figure 1: This figure details the information about each song as collected and found by Janata et al. (2012). [Jordan]

1.3  Task & Procedure

Participants were asked to complete a survey which presented 14 excerpts of songs each 30 seconds long in a random order. Participants were then asked to identify the salience of each beat level, on a scale of one to five with the first being the slowest, and the last being the fastest. They were instructed to only put record beat levels that they believe existed clearly in the music, not subdivided and less-natural beat levels that they were able to find due to musical training. They were also asked to provide the instrument that contributed the most to the creation of each beat level.

Figure 2: This figure shows the basic setup of each trial. An additional space was provided below in each trial for miscellaneous or explanatory comments. [Jordan]

1.4  Data Collection & Analysis

The data was collected through the Qualtrics website, and then exported into an excel sheet. An analysis was conducted by looking at the descriptive statistics on the experts’ agreement on the number of beat levels in each song, as well as the most salient beat level of those. Tempos of the most salient beats were found using an online tap metronome. This was conducted by the authors, and though there was a certain amount of subjectivity involved, the experts’ provided information concerning the instrumentation of each level, as well as its placement on the scale of Slowest Beat Level to Fastest Beat Level was carefully consulted to make decisions regarding the tempo.  These measures were then used to compare to the groove ratings and tempos found in Janata et al. (2011). [Jordan]

3. RESULTS

1.1 Population Sample

Our population sample consisted of four (4) Yale School of Music students, and one (1) professor and researcher of music. By these criteria, we judged them to be musical “experts”, defined as having many years of experience and a solid basis of theoretical and practical application of music. We believe our results allow us to generalize to the population of people who have this foundation of musical knowledge, but also to comment on the general population as a whole. No further descriptive statistics such as age, gender, etc. were obtained as their study was only intended to be exploratory at the outset of this experiment. [Jordan]

1.2

The # of Beat Levels

Expert # 1 Expert #2 Expert #3 Expert #4 Expert #5
Superstition 3 2 4 3 3
Yeah! 2 4 3 3 5
Freedom of the Road 2 4 4 3 4
What a Wonderful World 2 3 5 3 4
Beauty of the Sea 3 4 2 2 4
Thugamar Fein an Samhradh Linn 2 3 2 2 4
The Child is Gone 2 3 4 3 5
Mama Cita (Instrumental) 2 3 2 3 4
Citi Na GCumman 2 2 3 2 5
Summertime 2 2 4 2 5
Goodies 2 3 3 3 2
Step it Up Joe 2 3 3 2 4
In the Mood 3 3 3 3 3
Squeeze 3 3 4 3 4

Figure 3: Table of the numbers of beat levels assigned to each song by each expert.

As the table shows, there were a wide variety of beat levels identified by each individual expert. Different experts had different trends of what number of beat that they consistently identified, such as Expert #1 only alternating between identifying 2-3 beat levels per song. Expert #4 only identified 2-3 beat levels per song as well, and Expert #2 only had three songs with 4 beat levels identified. Experts #3 and #5 both had more variety between beat levels identified. Only Expert #3 and #5 identified any song as having 5 beat levels, although this was not consistent across songs and Expert #3 only identified “What a Wonderful World” as having 5 beat levels. The only song consistent in number of beat levels between all five experts was “In The Mood”, but the experts still disagreed on the particular order of instrumentation as organized by tempo in distinguishing each beat level.

Descriptive Statistics of the # of Beat Levels by Song

Range Mean Standard Deviation
Superstition 2 to 4 3 0.707106781
Yeah! 2 to 5 3.6 1.140175425
Freedom of the Road 2 to 4 3.4 0.894427191
What a Wonderful World 2 to 5 3.4 1.140175425
Beauty of the Sea 2 to 4 3 1
Thugamar Fein an Samhradh Linn 2 to 4 2.6 0.894427191
The Child is Gone 2 to 5 3.4 1.140175425
Mama Cita (Instrumental) 2 to 4 2.8 0.836660027
Citi Na GCumman 2 to 5 2.8 1.303840481
Summertime 2 to 5 3 1.414213562
Goodies 2 to 3 2.6 0.547722558
Step it Up Joe 2 to 4 2.8 0.836660027
In the Mood 3 3 0
Squeeze 3 to 4 3.4 0.547722558

Figure 4: Descriptive statistics based on the number of beat levels the experts assigned each song, organized by song.

The majority of the ranges seem to be centered around 3, with the mean of all the songs falling between 2.6 – 3.6.  The mean for all the beat levels perceived by the experts, across all songs, was M = 3.04285714, SD = .32749465. No song was identified as having less than 2 beat levels.

Figure 5: A histogram of the frequencies of each mean # of beat levels of the songs

Figure 6: This bar graph displays the means of the number of beat levels given by the experts for each song.

The histogram in Figure 5 appears to be relatively normal, except for gap in the center for a mean number of beats of 3.2, which tells us that statistical analyses are valid, but also that our results may be more random than we expected, as a normal distribution is the distribution of a random sampling. A mean number of beat levels of 3 and 3.4 beat levels are the most frequent, occurring four times each – 3.4 falls just outside one Standard Deviation from the overall mean, so it’s odd that there are 4 songs with that mean, but we had only 5 participants, which may explain some of the randomness of the data. “Yeah!” falls furthest from the overall mean, but is still less than 2 Standard Deviations away.

Descriptive Statistics of the Number of Beat Levels by Expert

Range Mean Number of Beat Levels Perceived Standard Deviation
Expert #1 2 to 3 2.28571429 0.468807231
Expert #2 2 to 4 3 0.67936622
Expert #3 2 to 5 3.28571429 0.913873533
Expert #4 2 to 3 2.64285714 0.497245158
Expert #5 2 to 5 4 0.877058019

Figure 7: This table shows descriptive statistics of the number of perceived beat levels, but this time is organized by expert, rather than song.

Figure 8: This figure shows how many beat levels each expert perceived in each song.

As we can see from these two figures, it is clear that different experts had different concepts of beat levels and used different strategies to find the beat levels in a given song. After all, for the same 14 clips of songs, one expert (Expert #5) perceived mostly 4 and 5 beat levels in the songs, while two others (Expert #1 & #4) perceived no more than 3 beat levels in all of the clips. We can also see that there was wide disagreement between the experts on almost every song – the only song that the experts unanimously agreed on was “In the Mood”, which they perceived as having 3 beat levels. It must also be taken into account that the mean and standard deviation across all the reported numbers of beat levels is M = 3.04285714, SD = .32749465. We can see that Expert #2 is closest to this mean. For the all the results from each expert in table form, please check the appendix.

1.3  Expert’s Perceived Instrumentation and Salience Ratings

Tempos of Each Expert’s Most Salient Beat Levels

Person 1 Person 2 Person 3 Person 4 Person 5
Superstition Bass Drum + Snare: 50
Clavinett + High Hat: 99
Bass: 100 Vocals: 100
Guitars: 200
Voice: 100 Bass, Kick, + Snare: 100
Yeah! High Hat  + Synth: 210 Percussive Click: 52 Vocals: 210 Bass Drum + Clap: 105 Kick: 52
Cymbals, Voice + Synth: 210
Freedom of the Road Bass Drum + Snare: 49 Bass: 25
Drums: 146
Harmony Piano: 25
Drums: 146
Vocals: 146
Bass Drum + Snare Drum: 49 Kick + Snare: 49
What a Wonderful World Bass Drum + Snare: 72 Drums: 72 Vocals: 72 Voice: 72 Kick, Snare, Horns, + Strings: 72
Beauty of the Sea Keyboard: 60
Keyboard: 120
Synth: 120 Saxophones: 60 Strings (Synthesizer): 60 Organ: 60
Thugamar Fein an Samhradh Linn Downbeats Every 6: 17 Cello: 34
Bagpipes: 34
Wind Cello: 34 Rolled Chords: 17 Cello: 34
The Child is Gone Piano: 65
Drums: 195
Piano: 65 Vocals: 65
Drums + Keyboard: 195
Piano Chords: 65 Drums, Bass, + Piano: 195
Mama Cita (Instrumental) Bass Drum + Bass:  95
High Hat + Percussion: 190
Drum: 95 Keyboard: 95
Percussion: 190
Bass Drums:  95 Kick + Bass: 95
Cabasa: 190
Citi Na GCumman Guitar’s Bass Notes: 38 Guitar Whole Note: 38
Guitar Melody: 114
Guitar Arpeggios: 114 Strum: 38
Summertime Strings: 34
Trumpet: 70
Drums: 70 Trumpet: 70 Drum Pattern: 70 Strings: 34
Bass: 70
Goodies Claps: 51 Drums: 101 Vocals: 101 Snare Drum:  101 Vocals: 204
In the Mood Bass: 162 High Hat: 162 Saxes + Trumpets: 162 Double Bass: 162 Drum + Bass: 162
Squeeze Bass Guitar + Bass Drum + Snare: 120
High Hat: 244
Drum Kit: 244 Bass: 120
Drums: 244
Guitar: 475
Bass Guitar: 120 Bass: 120
Rhythm + Solo: 240

Figure 9: A table showing the instrumentation and tempo of the beat level (or beat levels, as is the case with a few songs and experts) experts perceived as the most salient. Tempos are in BPM, and when an expert rated a song as having two or more equally salient “most salient” beat levels, all have been included.

Figure 10: This graph depicts the tempo found by the authors of the “Most Salient Beat Level” identified by each expert, compared to the tempos found by Janata et al. (2011). In order to graph the information, only one “Most Salient Beat Level” could be shown, though as the table above shows, some experts identified more than one “Most Salient Beat Level” – in these cases we have arbitrarily chosen to graph the slowest one.

As we can see from Figure 9 & 10, many of the experts were able to agree on a most salient beat level – all of the experts and Janata agreed unanimously on the tempo of the most salient beat level of “In the Mood”, placing it at around 162 BPM, as well as on the most salient beat level of “Mama Cita (Instrumental)”. However, as is shown in the table, in the case of “Mama Cita (Instrumental)”, three of the experts (Experts #1, #3, and #5) also identified a second most salient beat level at double the first tempo. This is an example of how different beat levels seemed to follow simple ratios such as halves and thirds. The keyboard, drum, and bass in “Mama Cita” (Instrumental) were identified by all five experts as being the most salient beat level at the tempo we further analyzed to be 95 BPM. Additionally, those three experts all identified a second, equally most salient beat level, at 190BPM (double time of the slower, more salient beat level) for high hat, percussion, and cabasa. This can also be seen in “Superstition,” where the most salient beat levels identified were even at the four different tempos of 50, 99, 100, and 200 BPM, all easily subdivided into each other.

There was some disagreement though; all of the experts deemed either percussive sounds or vocals as the most salient beat levels for “Goodies.” However, each expert identified each at a different tempo, based on our interpretations of their rankings of each salient instrument. Expert #1’s ‘claps’ are half the tempo of Experts #2 and #4 percussion sounds, which are actually the same tempo of Expert #3’s vocals because they are listed before percussion in the five levels. Expert #5 has vocals listed after percussion beat levels though, leading us to interpret that theirs was the faster subdivision of vocals at the tempo of 204 BPM.

Experts tended to differ particularly in identifying in the instrumentation of specific beat levels, or assigned different instruments different tempos, but for the most part always had at least one level that could be found in the results for other experts as well. For example, in “What a Wonderful World,” the most salient beat level, although identified as some combination of bass drum, strings, horn, and voice, always was within a consistent tempo of 72 BPM. Many times differences in instrumentation were only due to strategies in identifying specific names of instruments, i.e. labelling a beat level as ‘bass drum and snare’ versus ‘kick and snare.’

In conclusion, we found a wide variance in our data – sometimes the experts agreed, even unanimously in the case of “In the Mood”, and sometimes they disagreed not only on the most salient beat levels, but on the instrumentation and tempos of those beat levels. However, we generally found that the experts identified tempos that fell into simple beat ratios, and if not the same as Janata’s ratings, were at least multiples or factors of them.

1.4  Analysis & Figure 3

Figure 11: This graph compares our mean number of beat levels identified to the groove ratings of the same song clips from Janata et al. (2011)

As we can see from this graph, though there was no statistical analysis possible (due to the lack of a “known value” for the beat levels) we can say that we failed to reject the null hypothesis that the number of beat levels in a song is not correlated with the grooviness of the song. In other words, it is not clear at all whether having more beat levels in a song might contribute to hearing the song as having more groove. In our findings, the song with the highest groove rating ended up having roughly an average number of beat levels, while songs that received a very low groove rating had the same average number or even higher numbers of beat levels. “Citi Na CGumman,” with one of the lowest groove ratings of 35.2, had up to five levels of beat levels identified.

4. CONCLUSIONS

It’s clear from our results that beat levels are a far more subjective and varied measure than we anticipated. Our assumption was that school of music students and professors, with their expertise and experience with music, would be more likely to identify similar numbers of beat levels for each song.  However, as shown by the results, only one song was unanimously agreed upon, “In the Mood” – the rest showed a wide range of variety, with some experts identifying only 2 beat levels for some songs and others identifying up to 5 beat levels for the same songs. As noted in the Present Research section of this structured abstract, this unexpected variability made it difficult and of questionable worth to continue on with our original experiment.  However, with the data we gathered from the experts, we were still able to conduct several analyses exploring the variability between experts we found more thoroughly, and comparing our results with those of Janata et. al (2011).

Through these analyses we discovered that each expert often had their own quirks and trends – this makes sense when you consider that each expert likely had their own specific strategy for discerning the number of beat levels in each clip. Across the experts, we also noticed a habit of recognizing a combination of instruments as creating beat levels not articulated by one instrument alone.  For example, in “Yeah!”, Expert #4 identified the instrumentation of the most salient beat level as “Bass Drum + Clap”.  What we can hear when listening to the clip is that the Bass Drum and the Clap both move at slower tempos individually [which Expert #2 separated, as is shown in Figure 7, (we attributed Expert #2’s instrumentation of a “percussive click” as the Clap heard by Expert #4) and which we found to have tempo roughly half that of the two instruments combined], but when heard together, and perceived as one beat level, they combine to form a faster metric structure, the downbeats of each instrument falling on beats of a faster tempo.

Experts, in addition to disagreeing about the number of beat levels present in the songs, also disagreed about the most salient beat, and the instrumentation of it. As noted above, this was sometimes caused by some experts’ combinations of instruments, but sometimes different experts simply seemed to be listening for different auditory stimuli in order to obtain the number of beat levels.  When comparing Figure 6 with Figure 7, we can see that Expert #1 seemed to focus more on the different percussion present in a song to find salient beat levels, while Expert #2 seemed to look more consistently to the strings in order to find the salient beat levels.  From these results we can infer that these two experts were likely using different strategies, but also may have different concentrations or focuses within music, one being more attuned to percussion and the other to string instruments.

In addition to the variability found between experts, we ran into the problem that all of the song’s averaged beat levels fell between 2.6 and 3.6.  Despite the variability of reported beat levels, sometimes ranging from 2 beat levels all the way to 5 beat levels, the averages showed little variation. It may be that this difference is still significant, but it debunks an implicit assumption we possessed that there would be a wider variety of averages, and that it would be more clearly in different categories. This also seems to imply that most songs would fall within this range, as we sampled a variety of genres and tempos of music.

Though were not able to correlate the number of salient beat levels with the perceived groove of a song, we do not believe that the two are necessarily unrelated. There were several implicit assumptions that became clear only after analyzing the results, but that might have accounted for the variability as well as the findings we drew from those results.  For example, a better method, and one that might have produced more consistent results, would have been for the authors to analyze the songs beforehand and produce a list of instruments contained in each song, which the participants could have then put into order from slowest beat level to fastest.  This practice would have at least eliminated some of the variability within the instrumentation, as many experts put down varying names for the same beat level.  The way we chose to measure salience also had its flaws; 0 – 10, while seemingly a simplistic subjective measure, was found difficult to attach to a measure of salience, as there is no unit for salience, and no value that immediately corresponds across the two measures. (For example, from 0 – 10, what does a salience of 5 mean?  The beat level is halfway salient?). As stated above, a question asking participants to order the salient beat levels, and using that as a comparative measure may have been a more effective method. Lastly, our method of finding the tempo of each beat level was flawed.  Though the authors were as accurate as possible, following the results given by the experts as closely as possible, it would have been more accurate to have the participants give the tempo for each beat level they were identifying themselves, by use of a within survey tap metronome.  Using this method would have eliminated any possible bias (no matter how much the authors tried to uphold the integrity of the results, it is possible, though unlikely, that we misinterpreted some of the information when finding the tempos), and made the results much clearer.  In another direction, a future study may have each expert rate the song’s grooviness in addition to finding the beat levels – perhaps the connection is not between the structural beat levels within the song and the song’s groove, but between a person’s personal perceived number of beat levels (however many beat levels that person is hearing/believes to exist in the song) and that person’s personal assessment of the song’s groove.  Future studies may also want to ask the experts specific questions about their training, in order to better understand their backgrounds and the strategies they may be employing in the task.  By implementing these measures, we believe that a more successful experiment, more sound and distributed to a larger number and wider variety of people may be conducted in the future to better explore the relationship between the number of beat levels and groove.

 

ACKNOWLEDGEMENTS

The authors would like to thank our musical experts for taking the time to complete our survey, as well as Professor Eve Poudrier for advising us throughout the process. We would also like to extend our gratitude to Petr Janata, who was the leading researcher for the original study our experiment was based off of, and who very generously provided us with the stimuli he and his team used and the data they collected.

REFERENCES

Janata, P., Tomic, S. T., & Haberman, J. M. (2011). Sensorimotor Coupling in Music and the Psychology of the Groove. Journal of Experimental Psychology, Vol. 141, No. 1, 54–75

 

Madison, G. (2006). Experiencing Groove Induced by Music: Consistency and Phenomenology. Music Perception: An Interdisciplinary Journal Vol. 24, No. 2 (pp. 201-208)

Madison, G., Gouyon, F., Ullen, F., Hornstrom, K. (2011). Modeling the tendency for music to induce movement in humans: First correlations with low-level audio descriptors across music genres. Journal of Experimental Psychology: Human Perception and Performance, Vol 37(5), 1578-1594.

Tomic, S. T., & Janata, P. (2008). Beyond the Beat: Modeling Metric Structure in Music and Performance. The Journal of the Acoustical Society of America 124.6: 4024. Web.

APPENDIX

Expert #1

1st Beat Level and Salience 2nd Beat Level and Salience 3rd Beat Level and Salience 4th Beat Level and Salience 5th Beat Level and Salience
Superstition Bass Drum + Snare: 10 Clavinet + High Hat: 10 Horns + Clavinet + High Hat: 9
Yeah! Bass Drops: 8 High Hat + Synth: 10
Freedom of the Road Bass Drum + Snare: 10 High Hat + Guitar: 8
What a Wonderful World Bass Drum + Snare: 10 Guitar Arpeggios: 9
Beauty of the Sea Everything else: 8 Keyboard: 10 Keyboard: 10
Thugamar Fein an Samhradh Linn Stuff On Downbeats Every 6: 10 Cello: 3 Cello: 6
The Child is Gone Piano: 10 Drums: 10
Mama Cita (Instrumental) Bass Drum + Bass: 10 High Hat + Percussion: 10
Citi Na GCumman 8 10
Summertime Strings: 10 Trumpet: 10
Goodies Claps: 10 Vocals + Synth: 10
In the Mood Open High Hat: 7 Bass: 10 High Hat + Horns: 9
Squeeze Bass Guitar + Bass Drum + Snare: 10 Bass Guitar: 10 High Hat: 10

Figure 12

As stated above, Expert #1 never perceived more than 3 beat levels in a given song, and tended to rate the salience of those beat levels relatively highly, even at times rating two or more beat levels as “The Most Salient”. Expert #1 also tended to focus on the percussion parts in the piece in order to extract the salient beat levels although they tended to deconstruct the drum kit, for example differentiating between high hat, and bass drum and snare.

Expert #2

1st Beat Level and Salience 2nd Beat Level and Salience 3rd Beat Level and Salience 4th Beat Level and Salience 5th Beat Level and Salience
Superstition Drums: 6 Bass: 8
Yeah! Percussive Click: 9 Low Drum: 7 Synth: 8 Strings: 8
Freedom of the Road Bass: 9 Guitar: 6 Piano: 8 Drums: 9
What a Wonderful World Strings: 7 Guitar: 8 Drums: 6
Beauty of the Sea Saxes: 5 Synth: 8 Synth: 4 Synth: 3
Thugamar Fein an Samhradh Linn Guitar: 5 Cello: 8 Bagpipes: 8
The Child is Gone Strings: 6 Piano: 9 High Hat: 8
Mama Cita (Instrumental) Piano: 5 Drums: 9 Percussion: 6
Citi Na GCumman Guitar’s Bass Notes: 8 Guitar’s Midrange Notes: 6
Summertime Strings: 6 Drums: 9
Goodies Sign Wave Noise: 7 Drums: 8 Vocals: 5
In the Mood High Hat: 8 Bass: 7 Saxes From 10-12 sec: 8
Squeeze Rhythm Guitar: 6 Drum Kit: 8 Electric Guitar: 7

Figure 13

Expert #2, in comparison to Expert #1, seemed to tune in more to string instruments when searching out the salient beat levels within a piece. Unlike the other Experts, Expert #2 refrained from combining several instruments into the creation of beat levels, citing only one instrument for each beat level.

Expert #3

1st Beat Level and Salience 2nd Beat Level and Salience 3rd Beat Level and Salience 4th Beat Level and Salience 5th Beat Level and Salience
Superstition Drums: 8 Trumpet: 7 Vocals: 10 Guitars: 10
Yeah! Synth: 9 Vocals: 9 Percussion: 10
Freedom of the Road Harmony Piano: 10 Guitar: 9 Drums: 10 Vocals: 10
What a Wonderful World Bass: 6 Trombone Fill: 7 Strings: 9 Vocals: 10 Guitar: 8
Beauty of the Sea Saxophones: 10 Synth: 9
Thugamar Fein an Samhradh Linn Strummed: 9 Wind Cello: 10
The Child is Gone Bass: 5 Violin: 8 Vocals: 10 Drums + Keyboard: 10
Mama Cita (Instrumental) Keyboard: 10 Percussion: 10
Citi Na GCumman Guitar Whole Note: 10 Guitar Arpeggiation: 9 Guitar Melody: 10
Summertime Strings Vib: 9 Bass: 6 Bass Answer: 8 Trumpet: 10
Goodies Bass: 8 Vocals: 10 Percussion: 9
In the Mood Bass: 5 Drums: 7 Saxes + Trumpets: 10
Squeeze Keyboards: 6 Bass: 10 Drums: 10 Guitar: 10

Figure 14

Expert #3 often neglected to identify an instrument for a first and even second beat level, focusing on faster and more salient instruments through which to identify each level. The Bass was most frequently associated with the slowest identified beat level. Like the rest of the experts, the instrumentation for “In The Mood” consists of some combination of percussion, horns, and bass.

Expert #4

1st Beat Level and Salience 2nd Beat Level and Salience 3rd Beat Level and Salience 4th Beat Level and Salience 5th Beat Level and Salience
Superstition Bass Drum: 3 Drum Set + Voice: 10 Guitar Combo: 8
Yeah! Voice (Accented Syllables): 3 Bass Drum + Clap: 9 High Hat: 6
Freedom of the Road Bass Guitar + Bass Drum: 7 Bass Drum + Snare Drum: 10 High Hat + Guitar: 5
What a Wonderful World Bass Drum: 3 Drum Set + Voice: 10 Guitar Arpeggio: 7
Beauty of the Sea Strings (Synthesizer): 6 6
Thugamar Fein an Samhradh Linn Rolled Chords: 8 Bagpipes: 6
The Child is Gone Drum: 2 Piano Chords: 10 High Hat: 6
Mama Cita (Instrumental) Chord Change: 1 Bass Drums: 10 High Pitched Instrument: 3
Citi Na GCumman Chord Changes: 4 Guitar Arpeggios: 8
Summertime Strings + Bass Chords: 5 Drum Pattern: 8
Goodies Bass Drum (Synthesizer): 2 Snare Drum: 8 Vocals: 6
In the Mood Brass Pattern: 4 High Hat: 8 Double Bass: 10
Squeeze Drums: 4 Bass Guitar: 10 Solo Guitar: 10

Figure 15

Expert #4, like Expert #1, never perceived more than 3 beat levels within a given piece, though s/he had a tendency to rate the salience of some beat levels as much lower, perceiving some beat levels only as just above having no salience whatsoever. Expert #4 noted in the comments section of the survey (the only expert that did so) that some instruments that make up a salient beat level do not come in immediately with the beginning of each song clip, and that “the fastest level seems to be a combination of faster attacks rather than being defined by a main instrument,” which is an observation that analyzing all of the experts together can show as a difficult part of the survey to be clear upon.

Expert #5

1st Beat Level and Salience 2nd Beat Level and Salience 3rd Beat Level and Salience 4th Beat Level and Salience 5th Beat Level and Salience
Superstition Bass, Kick, + Snare: 9 Clav, Brass Line, Voice: 8 Clav, Brass Line, Voice: 7
Yeah! Kick: 7 Whistle: 8 Kick: 9 Cymbals, Voice, + Synth: 9 Ring + Voice: 5
Freedom of the Road Bass + Slide Guitar: 9 Kick + Snare: 10 Guitar, High-Hat, + Voice: 9 Hi-Hat, Voice, + Bass: 7
What a Wonderful World Harmony: 7 Kick, Snare, Horns, + Strings: 10 Guitar + Hi-Hat: 9 Voice: 5
Beauty of the Sea Phrasing: 2 Implication of Organ: 9 and 10 Low Hum: 9 Organ: 9
Thugamar Fein an Samhradh Linn Plucked String: 5 Cello: 8 Pipes: 4 Drone: 2 Drone: 0
The Child is Gone Phrasing: 2 Electric Guitar: 7 Drums, Bass, + Piano: 10 Strings: 2 Cymbal + Voice: 4
Mama Cita (Instrumental) Keyboard: 7 Kick + Bass: 9 Melody + Bass: 7 Cabasa: 9
Citi Na GCumman Phrasing Emphasis: 2 Bass Register: 7 Strum: 10 Chordal Rhythm: 1 Melody: 9
Summertime Harmony: 4 Strings: 9 Bass: 9 Trombone: 7 Brushes: 6
Goodies Synth Whistle: 0 Synth Whistle: 0 Kick + Snare: 8 Vocals: 9
In the Mood Sax Line: 8 Drums + Bass: 9 Sax Solo: 8
Squeeze Phrasing: 6 Bass: 8 Drums: 9 Rhythm + Solo: 8

Figure 16: Figures 12 – 16 show each experts’ perceived instrumentation and their salience within each song, in order of tempo slowest to fastest.

Expert #5 perceived the widest range of beat levels, and often perceived significantly more beat levels than the other experts, hearing 4 or 5 beat levels much more frequently. However, they also had much lower levels that other experts present, even listing in “Goodies” that the first two beat levels were composed of a synth whistle with a salience of “0”, and can be assumed to be stating the presence of the synth as auditory stimulation without contributing to the tempo.

 

Reformulated Research Question

Question: Does movement accompanying an action help to remember that action?

Katinka Dijkstra, Michael P. Kaschak, Rolf A. Zwaan, (January 2007) ‘Body posture facilitates retrieval of autobiographical memories,’ Cognition, Volume 102, Issue 1, Pages 139-149, ISSN 0010-0277, http://dx.doi.org/10.1016/j.cognition.2005.12.009.

Abstract: “We assessed potential facilitation of congruent body posture on access to and retention of autobiographical memories in younger and older adults. Response times were shorter when body positions during prompted retrieval of autobiographical events were similar to the body positions in the original events than when body position was incongruent. Free recall of the autobiographical events two weeks later was also better for congruent-posture than for incongruent-posture memories. The findings were similar for younger and older adults, except for the finding that free recall was more accurate in younger adults than in older adults in the congruent condition. We discuss these findings in the context of theories of embodied cognition.”

Bettina Bläsing, Beatriz Calvo-Merino, Emily S. Cross, Corinne Jola, Juliane Honisch, Catherine J. Stevens, (February 2012) ‘Neurocognitive control in dance perception and performance,’ Acta Psychologica, Volume 139, Issue 2,  Pages 300-308, ISSN 0001-6918, http://dx.doi.org/10.1016/j.actpsy.2011.12.005.

Abstract: “Dance is a rich source of material for researchers interested in the integration of movement and cognition. The multiple aspects of embodied cognition involved in performing and perceiving dance have inspired scientists to use dance as a means for studying motor control, expertise, and action-perception links. The aim of this review is to present basic research on cognitive and neural processes implicated in the execution, expression, and observation of dance, and to bring into relief contemporary issues and open research questions. The review addresses six topics: 1) dancers’ exemplary motor control, in terms of postural control, equilibrium maintenance, and stabilization; 2) how dancers’ timing and on-line synchronization are influenced by attention demands and motor experience; 3) the critical roles played by sequence learning and memory; 4) how dancers make strategic use of visual and motor imagery; 5) the insights into the neural coupling between action and perception yielded through exploration of the brain architecture mediating dance observation; and 6) a neuroesthetics perspective that sheds new light on the way audiences perceive and evaluate dance expression. Current and emerging issues are presented regarding future directions that will facilitate the ongoing dialog between science and dance.”

Grafton, S. T. (2009), ‘Embodied Cognition and the Simulation of Action to Understand Others. Annals of the New York Academy of Sciences,’ 1156: 97–117. doi: 10.1111/j.1749-6632.2009.04425.x

“Understanding the goals or intentions of other people requires a broad range of evaluative processes including the decoding of biological motion, knowing about object properties, and abilities for recognizing task space requirements and social contexts. It is becoming increasingly evident that some of this decoding is based in part on the simulation of other people’s behavior within our own nervous system. This review focuses on aspects of action understanding that rely on embodied cognition, that is, the knowledge of the body and how it interacts with the world. This form of cognition provides an essential knowledge base from which action simulation can be used to decode at least some actions performed by others. Recent functional imaging studies or action understanding are interpreted with a goal of defining conditions when simulation operations occur and how this relates with other constructs, including top-down versus bottom-up processing and the functional distinctions between action observation and social networks. From this it is argued that action understanding emerges from the engagement of highly flexible computational hierarchies driven by simulation, object properties, social context, and kinematic constraints and where the hierarchy is driven by task structure rather than functional or strict anatomic rules.”

Group Project Citiations

Embodied Cognition and Kinesthetic Motion Literature Review

Slow, fast, fluid – these adjectives can be applied to the facets of rhythm, tempo, and articulation of either movement or music. In fact, there is not much dispute that the auditory and vestibular systems are, in fact, linked. Human movement studies have been involved in everything from pedagogical approaches to memory and entrainment. This literature review addresses how physical body movements can be linked to music, touching upon embodied cognition, physical movement and motion capture technology, how movement to music affects beat perception, developmental studies about rhythmic performance, and substrates behind rhythm affection motor behavior. Not only are brain areas traditionally assumed to only be associated with performing kinesthetic actions now being linked to auditory beat perception, these neuroscience studies are being used alongside behavioral studies that show how body movements can help parse the metric structure of music (Toivianen 2010).

Leman (2008) focuses on the presence of goal-directed action in music perception, embodied cognition thus assuming interaction between an organism and its environment. Leman also mentions Hanslick’s theory of moving sonic forms; just as dance is an undefined structure of form relationships, so is music. An organism’s reaction to the moving sonic form of music is corporeal, providing support to embodied cognition being shaped by aspects of the body. Under the impression that movement can enhance listening, a study attempting to measure vestibular influence on auditory metrical interpretation (Phillips-Silver Phillips-Silver & Trainor, 2008) found that movement of the head, but not legs, affects meter perception. Drawing upon previous work that showed that body movement could help distinguish between metrically ambiguous rhythmic sound patterns, Phillips-Silver & Trainor (2005, 2007) were able to both isolate the vestibular system and test without any vestibular input with the end result of proving that the vestibular system and auditory information are indeed integrated in perception.

Ranging from spontaneous to deliberate body movements, dance is a form of corporeal interpretation of music that can be captured by various technological methods. Eerola et al. (2006) investigated the corporeal movement of toddlers to music using a high-resolution motion capture system. Toiviainen et al. (2010) applied kinetic analysis, body modeling, dimensionality reduction, and signal processing to data acquired by attaching reflectors to 28 joint markers on participants’ bodies. Eigenmovements, according to Alexandrov et al. (2001), are “movements along eigenvectors of the motion equation.”

A high-resolution motion capture system was used in the 2010 Toiviainen study to identify the most typical movement patterns, or eigenmovements, synchronized to different metrical, or beat, levels. PCs (principal components) are a reduced group of uncorrelated variables transformed into a large group of variables, the first five pertaining to the rotation of the upper torso, lateral swaying of the body, mediolateral arm movement, (four does not vary significantly) and vertical arm movement. The beat-level data can be summarized as follows: The one-beat level corresponded with mediolateral and vertical arm movements, the two-beat level with mediolateral arm movements and rotation of the upper torso, and the four-beat level with lateral swaying of the body and rotation of the upper torso. This observation was in line with their hypothesis that “faster metric levels are embodied in the extremities, and slower ones in the central parts of the body.” The torso’s significant mass, and thus kinetic energy, can be thought of in terms of the previously mentioned study’s focus on vestibular motion (in connection to the torso). Even a relatively dated study using motion capture like a virtual dance and music environment at UC Irvine (Beliaqua et al., 2001) used a data stream from placement of acceleration sensors on strategic body parts in order to transform motion into sound.

Mitchell et al. (2001) postulates that similar emotions generated from music and dance can be a means of matching them, accordingly suggesting that their simultaneous presentation might increase the chances of a match even with few similarities. The cross-modality mainly taken into account is emotion, presented as a representation of visual, auditory, or kinesthetic imagery that could potentially serve as a connector in memory between “temporally dissociated visual observations of a dance and auditory experience of the music that inspired it.” There may be a correlation of movement to ‘groove,’ as well, keeping in mind that some rhythms may only be inhibited by adding the additional stimuli of movement (Petr et al., 2011).

The auditory and dorsal premotor cortices were activated for longer tap times (louder tones) in a study (Zatorre et al., 2006). First hypothesized was that the more salient meters would most affect movement entrainment, also modulating brain regions driven by these auditory–motor interactions. Five parametric levels of metric saliency were created to test the hypothesis by increasing the contrast in sound intensity between accented and unaccented notes. Ultimately, the posterior STG and dPMC showed the most function connectivity in auditory-motor interactions. However, these findings can also be applied to neural components such as “mirror neurons,” due to the muscle memory enhanced by repetition of movement, for example by drummers reproducing the exact same sound at the same tempo.

In conclusion, a clear distinction needs to be made between what kind of movement is being integrated with auditory stimuli. Movements follow a hierarchical organization depending on the proximal/distal characteristic of the limb used (Peckel et al., 2014), and can even depend on loudness of tone as well. Music “has a pervasive tendency to rhythmically engage our body,” (Dalla Bella et al., 2013), but we still are not able to fully pin down the neural substrates involved, not only because the cross-modality of areas like the pre-motor cortex are involved in so many bodily functions. Current studies are focusing on modeling hierarchically organized temporal patterns induced by external rhythms. Questions to take away from this can include that if new temporal patterns are presented, do they have basis in past, known, patterns, and can movements be applied to this same exploration?

References

 

Switch to #3- Saliency and Groove

Does the accessability/saliency of different beats/levels enhance peoples’ perceptions of groove?

Because we are just switching to this topic, I wanted to make sure we have a firm grasp on what was suggested to us in class today and the topic as a whole;

We will be using the groove ratings from the Janata study in our readings, first getting a panel of music majors to analyze our song choices and determine how many subdivisions (levels) they can find. Next, we will have subjects perform a task of saying how many subdivisions they find present, comparing these answers to the original findings of the panel and their relation to how groovy the music is.

Does the addition of kinesthetic motion while listening to rhythms affect performance on memory tasks of reproducing those same rhythms?

1. Phillips-Silver, Jessica (2009) ‘On the Meaning of Movement in Music, Development and the Brain,’ Contemporary Music Review, 28: 3, 293-31

Because Phillips-Silver approaches the neuroscience of music from a multi-disciplinary approach, her exploration of beat perception and synchronization explicitly applies to my interest in sensorimotor development and how such movements are cultivated.

2. Iyer, Vijay (2002) ‘Embodied Mind, Situated Cognition, and Expressive Microtiming in African-American Music,’ Music Perception: An Interdisciplinary Journal, Vol. 19, No. 3 pp. 387-414

This article references how music correlates to bodily motions with data of what rhythmic pulses pertain to what kinds of vestibular motions. Beats are described as being felt, while bars counted; could the movements that we naturally feel help with subconsciously count bars, too?

3. Petr Janata, Stefan T. Tomic, and Jason M. Haberman (2011) ‘Sensorimotor Coupling in Music and the Psychology of the Groove,’Journal of Experimental Psychology, Vol. 141, No. 1, 54–75

[This week’s readings actually included things right up my alley,], this article discusses the urge to move when listening to music, relevant to my qualifying question of whether ; there may be a correlation to ‘groove,’ as remembering some rhythms may only be inhibited by adding the additional stimuli of movement.

4. Phillips-Silver & Trainor (2008), “Vestibular Influence on Auditory Metrical Interpretation,” Brain and Cognition 67, 94–102

Phillips-Silver & Trainor discuss the bias that vestibular influence can have on interpreting patterns, literally delving into the cognitive aspect of my project.

5. Mathieu Peckel, Thierry Pozzo and Emmanuel Bigand (2014) ‘The impact of the perception of rhythmic music on self-paced oscillatory movements,’ Front Psychol. 5:1037.

One of many findings on how characteristics of limbs used impacts the perception of rhythm- this could possibly create problems for my definition of ‘motion,’ and how trials differentiate.

6. Simone Dalla Bella, Anita Białuńska, and Jakub Sowiński (2013) ‘Why Movement Is Captured by Music, but Less by Speech: Role of Temporal Regularity’ PLoS One; 8(8): e71945.

This article mostly caught my attention because it supports my idea that music “has a pervasive tendency to rhythmically engage our body,” but that pitch also plays a role, not just rhythm, in dictating our comfortability in syncing to it (as opposed to speech). Applied to my question, I would have to think carefully about participants and what music/auditory stimuli I would use. 

7. McNeill WH (1995) Keeping together in time: Dance and drill in human history. Cambridge, MA: Harvard University Press

This book addresses the theoretical aspect of the motion-oriented part of my question.

8. Zatorre RJ, Chen JL, Penhune VB (2007) ‘When the brain plays music: Auditory-motor interactions in music perception and production.’ Nat Rev Neurosci 8: 547-558.10.1038/nrn2152 PubMed: 17585307 [PubMed]

These auditory-motor interactions are referring to playing instruments themselves in relation to music, which makes me think about if certain musicians are then already primed by certain movements. Is a clarinetist aided by moving certain fingers along with rhythms; is a drummer hindered if both arms and legs are not allowed to move?

9. Zatorre RJ, Chen JL, Penhune VB (2006) ‘Interactions between auditory and dorsal premotor cortex during synchronization to musical rhythms’

This study for Zatorre et al, too, wants to “elucidate the neural correlates of these auditory–motor interactions,” yet I have not found a single study that approaches the construct of memory from music through dance. Even experiments that use movement to help encode memory do so for visual or speech patterns, not rhythmic ones. 

10. Robert W. Mitchell and Matthew C. Gallaher (2001) ‘Embodying Music: Matching Music and Dance in Memory,’ Music Perception: An Interdisciplinary Journal, Vol. 19, No. 1 (pp. 65-85)

Dances were decidedly matched to certain musics, again returning to the idea that distinct movements may not accompany just particular tempos, but also musical styles and modes.

Embodiment, Rhythm and Cognition

 

 I want to begin to better approach the understanding between the kinesthetic embodiment of rhythm through the means of cognition, specifically memory.  Many tasks of experimenting with metric entrainment involve some sort of tapping, usually hand rather than foot for data-recording purposes. This phenomenon, linked to my understanding of the learning West African dance-drumming, made me question this method of learning rhythms and how our bodies react to hearing them, especially from a Western cultural background.

Part of Vijay Iyer’s work discusses some of these same tropes that I am interested in pursuing. He draws links between the claim that perhaps music is meant to be moved to with the Anlo-Ewe culture of Ghana, West Africa.

What I want to address is the fact that sometimes in West African dance-drumming (the fact that the meaning of the word in Ewe is interchangeable between dance and the music only strengthens this argument of the different cultural approach of movement pertaining to music), one starts to move in order to recall the drum language, or rhythms that dictate those movements. In designing an experiment, would subjects be able to better remember rhythms depending on whether they learned movements alongside those same rhythms?

It is unusual in Western culture to learn rhythms based on movement, but what if we used a set of movements to help remember a rhythm, or even lyrics? We learn dance from melodies or in a counting manner (or learn concepts set to music), and I want to explore the other direction of that correlation; using movement to help trigger rhythmic recall. Subjects (without certain experience, dance, etc) would be given certain patterns to learn, accompanied by movements or alone- just the movements too- and then try to reproduce those rhythms, being allowed to move along with their attempts or not. Does this movement have to be synchronized with the beats, or dance at all? Do certain parts of the body elicit better ability of memorization? Would this method work best with polyrhythms, and only serve to confuse subjects struggling to learn movements along with a simple pattern?

Dalcroze eurhythmics is an example of possible applications of addressing these questions. While it does not answer any of them specifically, this method of teaching music to students using embodiment underlines the importance of kinesthetic movement reinforcing neural circuits involving memory, and gives us only more reason to want to fully understand its effectiveness.