One clue as to how we feel while listening to music is in how we move, from dance-like motions to brows furrowed in concentration. Surface electromyography sensors (sEMG) are very useful in picking up muscle contractions which generate these expressive behaviours. In the solo response project, two typical sensors on the face to capture brow furrowing and smiling (see the set-up post) and an additional sensor tracking contractions of the trapezius muscle up the back of my neck, to capture tension in the back and head nodding. While there is much to say about each signal, this post is about what these sensors measured of my responses to Varuo, by Sigur Ros.
In the analysis post, I mentioned that the B section, starting around 220s, was really intense, and often overwhelming. These sensors agree with that, all shwing more instances of high muscle activity across sessions from 250s to 350s. Sublime face, yawning, and an explanation of how to read this graph below the cut.
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Note: music review posts discuss the stimulus selection, structure, and general response patterns, giving an overview of the stimulus before digging into the responses.
Stimulus number 1, numbered for convenience not playlist order–each session’s music was randomly ordered–was a track from 2012 Sigur Rós album Valtari. I’d been enjoying the band’s previous work in the last year, taken by the textures, slow builds, and dramatic extremes of intensity. When I saw a new album had been release a month or two before the scheduled response recordings, I chose a track to reserve for the experiment. The 24 response session are my first 24 full listenings to this epic piece of music, and it was a really intense experience almost every time. Read the rest of this entry »
I’ve finally got a version of these responses in a format I’m willing to post. The responses, organised by stimulus as matlab data structures, are being added now on figshare. I’m also planning on releasing them in a more flexible format, but I’mnot yet decided how (suggestions welcome). In its current format, the responses and all interesting extracted features accumulate to nearly 3 gigs of numbers, this isn’t a data pile for the faint of heart.
In my own files, I have other structurings of the data, specifically sensor-wise structures which allow for between stimulus comparisons and downsampled versions to look at everything at once, but these are not necessarily in sharable good shape.
(This is a repost from April 2013, as I was preparing to bring this experimental data to conferences)
In the coming months, I’ll be taking results from the solo response project to several conferences, and reviewer feedback has me worried about people dismissing this data because I collected the data from myself. I keep getting distracted by these imaginary confrontations with suspicious researchers so it’s time I lay down some concisely-expressed arguments to appease the hypothetical skeptics.
Problem 1: One subject = bad empirical research
I don’t like this one because the premise is wrong, but I’ve gotten it a lot already, so here goes. Read the rest of this entry »
(This is a repost from during the recording sessions in July 2012)
How else does a grad student get 40+ hours of experimentation time over 20 sessions from one human subject?
I thought someone might have an issue with my idea of using myself, but apparently not. The NYU ethics review office didn’t even want to hear about what responses I’d be recording, or what protocols I’d be following. Maybe they assumed that music is a harmless stimulus, or that I knew I wasn’t allergic to the glue we use to attached electrodes, or that I was very unlikely to sue the school because of research I’d planed myself. I hope they didn’t care because the data collection is happening at another institution, in collaboration with someone who’s standing ethics certificate does cover this type of experiment.
(This is a repost, from March 1st, 2013)
Before I forget everything, let me get down the setup details of the experiment I ran last summer (2012). Besides selecting the 25 pieces and working out where I was going to run the experiment, there was a lot of other relevant details. The following descriptions are for the purpose of documenting the experiment’s methodology; I hope anyone interested in employing these methods will seek higher authorities for instructions of best practices.
Stephen McAdams was kind enough to let me borrow some CIRMMT equipment (Thought Technologies’ ProComp Infiniti and a pile of sensors) and occupy some of his lab space for a month. Though a little casual by some standards, I made myself a cubical out of spare sound absorption panels in a large room that was usually unoccupied while I was recording. To get data from the ProComp in a useful format, Bennet Smith helped sort out some of his old scripts that conveniently time stamped the physiological sensor data and packaged it as UDP messages. That left me with getting some system set up run the experiment, provide a behavioural response interface, and save the recorded responses in a reliable fashion.
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Music effects human listeners. It can make us dance, cry, relax, and remember times past. We like making music and hearing it, working hard to play it well and earning money to buy concert tickets, albums, and audio equipment. We like sharing it with others, for collective pleasure and understanding, and to declare our cultural and individual identities. We also like listening to it privately, exploring our feelings or pushing them to a new state. Despite all that we know of music’s role and effect in modern life, it is very difficulty to understand how music works and what gives it this power. What is in these streams of sound that gives us a rush, or makes us sigh?
The Solo Response Project is a music cognition research project to explore these issues through a particularly tight experimental lens. Some of what we know of musics effects come from direct experimental study: putting sensors on peoples bodies, playing them music, and looking at the numbers to see what has changed. The Solo Response Project uses these same measurement techniques, but with some key distinctions: the responses of one person (me!) were recorded again and again (24 sessions), the musical stimuli were selected to cover a very wide range of styles and genres (25 pieces), the analyses of the measured responses are tailored to explore moments in time (Activity Analysis!), and the subjective experience of the listener is enhanced with post-listening notes and all that I can remember from the experience.
This blog is my repository of analyses of these data. Most of the posts will focus on individual stimuli (all 25), looking at patterns of responses which recurred in the subjective and physiological measurements. Some posts will share results pertaining to theories of music cognition and specific measurement techniques by combining results from the different pieces. Other posts will point to where the data and code used for these analyses can be picked up by others who are curious to look at these results direction.
Thanks for checking out this record of my research. Any questions can be added as comments or sent to me directly: finn (at) nyu (dot) edu