{"id":142,"date":"2021-10-30T17:39:02","date_gmt":"2021-10-31T00:39:02","guid":{"rendered":"https:\/\/blogs.ubc.ca\/ddperrott\/?p=142"},"modified":"2021-10-30T17:40:00","modified_gmt":"2021-10-31T00:40:00","slug":"task-8","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/ddperrott\/2021\/10\/30\/task-8\/","title":{"rendered":"Task 8 – Golden Record Curation"},"content":{"rendered":"

What a daunting task Sagan and his team undertook when they set out to chose 90 minutes of music to \u201cmemorialize the human species\u201d (Taylor, 2019) as part of the Voyager Golden Record. This assignment was the first time I had heard of this intergalactic record that is currently 23 trillion kilometers away from earth (Nasa, n.d.). Several of the songs, such as the Bach and Beethoven pieces, were selected due to their mathematical qualities (Taylor, 2019). Many were added because they were songs they felt were beautiful, which is a very subjective selection method. Dr. Smith Rumsey (Brown University, 2017) discussed subjectivity as related to the challenges of what materials are preserved digitally and which will be lost.\u00a0 Not all communities may be represented, just as not all languages and cultures were represented within the Golden Record\u00a0<\/span><\/p>\n

In deciding on how I would curate the original list of 27 songs to only 10, I ended up using a subjective method myself. I listen to a lot of music on Spotify, where algorithms are used to suggest playlists and artists that I may enjoy based on what I have listened to in the past. There are also playlists available that use mood as a filter. An example of this type of algorithm was created by Bhat, A.S. et al. (2014) who used measurements of intensity, timbre, pitch and rhythm to denote the mood of a musical piece.\u00a0 <\/span><\/p>\n

\"\"<\/p>\n

\u00a0“<\/b>Moods classified in accordance to audio features” (Bhat, A.S. et al., 2014)\u00a0<\/span><\/p>\n

\u00a0Being unable to use the complicated algorithms developed by Bhat, et al. (2014) or music streaming companies such as<\/span> Pandora\u2019s Music Genome Project<\/span><\/a>, I decided to use the chart from Bhat as a guide to help me classify the songs by mood. I first categorized the songs by continent and whether it was instrumental or included vocals. I listened to the songs again and added a mood to each based on my limited music knowledge of pitch, rhythm, timbre and intensity.\u00a0 I picked one song for each mood and as I did, I checked to be sure that I had a variety of\u00a0 vocal and instrumental pieces as well as one from each continent.<\/span><\/p>\n

The songs highlighted in blue are the ten that I curated. As you listen to the songs, do any of the classifications match with a mood you would selected?\u00a0<\/span><\/p>\n

\"\"<\/p>\n


\n<\/b>References\u00a0<\/b><\/p>\n

Bhat, A.S., et al. (2014). An Efficient Classification Algorithm for Music Mood Detection in Western and Hindi Music Using Audio Feature Extraction. <\/span>2014 Fifth International Conference on Signal and Image Processing<\/span><\/i>, 2014, pp. 359-364.\u00a0\u00a0<\/span><\/p>\n

Brown University. (2017, July 11). <\/span>Abby Smith Rumsey: \u201cDigital Memory: What Can We Afford to Lose?\u201d<\/span><\/i> [Video]. YouTube. https:\/\/www.youtube.com\/watch?v=FBrahqg9ZMc<\/span><\/p>\n

NASA Jet Propulsion Laboratory. (n.d.) <\/span>Voyager. What\u2019s happening now?<\/span><\/i>\u00a0 Retrieved October 29, 2021 from <\/span>https:\/\/voyager.jpl.nasa.gov\/<\/span><\/a><\/p>\n

Taylor, D.\u00a0 (Host). (2019, April 22). Voyager Golden Record. <\/span>\u00a0<\/span>[Audio podcast episode]. In<\/span> Twenty Thousand Hertz.<\/span><\/i> Defacto Sound. https:\/\/www.20k.org\/episodes\/voyagergoldenrecord<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

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