PhD Defended

On June 21st, 2018, I successfully defended by doctoral dissertation, Detection of Respiratory Phase Adaptation to Heard Music.  Without a doubt, listeners do subtly and subconsciously adjust when they breathe to fit with music, lining up specific respiratory phases to specific moments, but this happens under limited conditions. Only some moments of music draw respiratory phase alignment, and some people show stronger susceptibility to music’s coordinating influence.

With the extra three months granted by my committee, my quantitative analysis of listener respiration was extended with qualitative analysis of alignment patterns in repeated response studies and audience experiments. Activity analysis identified moments of exceptional phase alignment and music theory enriched my interpretation of the corresponding stimulus. Out of 36 pieces of music, 21 provoked identifiable moments of alignment and out of these arose four theories of how listeners’ breathing could be drawn or cued by what they heard:

  • Embodied perception/motor imagery: Some listeners toke inspirations when they might have, were they performing the music. This happens to vocal music, whether or not the performers’ breaths could be heard in music recordings. Examples from one case study participant can be seen in the attached figure, with inspirations (blue stars on chest expansion measurements) coinciding with performer inspirations during this a cappella folk song (highlighted in red on sound wave).
  • Inspiration suppression for attentive listening: The noise of inspiration and expiration can get in the way of auditory attention and there are (rare) moments in music when listeners seem to delay breathing in or out so as to hear better. A moment like this is also in the attached figure, with post-expiration pauses extended from 97.4 s.
  • Respiratory marking of salient moments: Listeners would sometimes breath in our out with recurring elements of musical motives, as if acting with something important or familiar. This was more common in structurally complex music and moments of strong affect, such as powerful lyrics, increasing tension, or exceptional aesthetics.
  • Post-event respiratory reset: In a few cases, well timed respiration cycles occurred after events, like after the last line of a song. This is reminiscent of relaxing sighs and similar actions through to help the respiratory system reset back to normal relaxed quiet breathing.

Causal mechanisms for these four theories are suggested by current respiration and music cognition research, however they each require further exploration on experimental data beyond what was studied here. And it is also possible they might arise more frequently than could be captured by these statistics, limited as they are to behaviour that co-occurs with the music at least 20-40% of the time. Between a theorize mechanism and well designed experiments, it may yet be possible to detect these deviation in action, giving us further clues into how listeners are engaging with the music they hear.

More details to come in the shape of my final dissertation document. To be completed in the next month or so.

About that dissertation

I’d been keeping quiet about my thesis research prior to running experiments, but an update is now long over due.

My dissertation is on the measurement of changes in respiration during music listening, capturing when and how a (seated, attentive) listener’s breathing changes. It’s messy measurements from multiple data sets, and musical stimuli of many genres, and lots of heavy non-parametric statistics, and it makes me smile every time I work on it. Considering the respiratory cycle is not terribly difficult to track passively, the more information we can gather via this discrete signal, the better, right? There are a lot of potential uses, a lot of different types of information we might glean from this signal, but I am hesitant to write out these possibilities before more I’ve completed a few more tests.

From Matlab to Python

After years of unexplicable failure, I’ve finally gotten numpy and scipy to play nice on my computer. (Anaconda finally installed properly once I moved to Yosemite; who knows what was breaking the system before…) So now I can finally start converting my matlab toolbox for Activity Analysis to open source python, and in the process, and share analyses online with ipython notebook. Whose excited? well I am. I think it will make it easier to follow what the calculations and inferences, particularly to those who aren’t inclined to download a toolbox and run a demo on their own machines.

Dissertation topic: Listener’s Respiratory Adaptation to Music

I’m only part way through the dissertation proposal process, but I’m very excited about it, so below is a description of the topic. Illustrative examples of respiratory coordination can be found on the Solo Response Project Blog. Also here is a PDF of the below.  (and yes, I really should edit more. My apologies.)

Continue reading “Dissertation topic: Listener’s Respiratory Adaptation to Music”