My 2018 Year in Music: Data Analysis and Insights

This past year has been pretty eventful in music for me. I’ve attended a couple new festivals, seen shows while traveling, and discovered plenty of new bands. I want to examine the data available to me and contrast it with my memories of the past year.

I’ve been using Splunk to analyze my music data for the past couple years. You can learn more about what I’ve learned from that in the past in my other posts, see Reflecting on a Decade of Quantified Music Listening and Best of 2017: Newly-Discovered Music. I also wrote a blog post for the Splunk blog (I work there) about this too: 10 Years of Listens: Analyzing My Music Data with Splunk.

Comparing Spotify’s Data with Mine

Spotify released its #2018wrapped campaign recently, sharing highlights from the year of my listening data with me (and in an ad campaign, aggregate data from all the users). As someone that uses Spotify but not as my exclusive source of music listening, I was curious to compare the results with my holistic dataset that I’ve compiled in Splunk. 

Top Artists are Poolside, The Blaze, Justice, Born Ruffians, and Bob Moses. Top Songs are Beautiful Rain, For the Birds, Miss You, Faces, and Heaven. I listened for 30.473 minutes, and my top genre was Indie.

Spotify’s top artists for me were somewhat different from the results that I found from the data I gather from Last.fm and analyze with Splunk software.  Spotify and my holistic listening data agree that I listened to Poolside more than anyone else, and was also a big fan of Born Ruffians, but beyond that they differ. This is probably due to the fact that I bought music and when I’m mobile I switch my primary listening out of Spotify to song files stored on my phone. 

Table showing my top artists and their listens, Poolside with 162 listens, The Vaccines with 136, Young Fathers with 124, Born Ruffians with 102 and Mumford and Sons with 99 listens.

In addition, my top 5 songs of the year were completely different from those listed in Spotify. My holistic top 5 songs of the year were all songs that I purchased. I don’t listen to music exclusively in Spotify, and my favorites go beyond what the service can recognize.

Table showing top songs and the corresponding artist and listen count for the song. Border Girl by Young Fathers with 35 was first, followed by Era by Hubert Kirchner with 32, Naive by the xx with 29, Sun (Viceroy Remix) by Two Door Cinema Club with 27 and There Will Be Time by Mumford & Sons with Baaba Maal also with 27 listens.

Spotify identified that I’ve listened to 30,473 minutes of music, but I can’t make a similarly reliable calculation with my existing data because I don’t have track length data for all the music that I’ve listened to. I can calculate the number of track listens so far this year, and based on that, make an approximation based on the track length data that I do have from my iTunes library. The minute calculation I can make indicates that I’ve so far spent 21,577 minutes listening to 3,878 of the 10,301 total listens I’ve accumulated so far this year (Numbers to change literally as this post is being written).

Screen capture showing total listens of 10,301 and total minutes listened to itunes library songs as 21,577 minutes.

I’m similarly lacking data allowing me to determine my top genre of the year, but Indie is a pretty reliable genre for my taste. 

Other Insights from 2018

I was able to calculate my Top 10 artists, songs, and albums of the year, and drill down on the top 10 artists to see additional data about them (if it existed) in my iTunes library, like other tracks, the date it was added, as well as the kind of file (helping me identify if it was purchased or not), and the length of the track.

Screen capture displaying top 10 artists, top 10 songs, top 10 albums of the year, with the artist Hubert Kirchner selected in the top 10 song list, with additional metadata about songs by Hubert Kirchner listed in a table below the top 10 lists, showing 3 songs by Hubert Kirchner along with the album, genre, rating, date_added, Kind, and track_length for the songs. Other highlights described in text.

There are quite a few common threads across the top 10 artists, songs, and albums, with Poolside, Young Fathers, Gilligan Moss, The Vaccines, and Justice making consistent appearances. The top 10 songs display obsessions with particular songs that outweigh an aggregate popularity for the entire album, leading other songs to be the top albums of the year.

Interestingly, the Polo & Pan album makes my top 10 albums while they don’t make it to my top 10 artist or song lists. This is also true for the album Dancehall by The Blaze. I’m not much of an album listener usually, but I know I listened to those albums several times.

The top 10 song list is more dominated by specific songs that caught my attention, and the top 10 artists neatly reflect both lists. The artists that have a bit more of a back catalog also reveal themselves, given that Born Ruffians managed to crack the top 10 despite not having any songs or albums make the top 10 lists, and Hey Rosetta! makes the top artist and album lists, despite having no top songs.

Screen capture that says Songs Purchased in 2018. 285 songs.

I purchased 285 songs this year, an increase of 157 compared to the year before. I think I just bought songs more quickly after first hearing them this year, and there are even some songs missing from this list that I bought on Beatport or Bandcamp because they weren’t available in the iTunes Store. While I caved in to Spotify premium this year, I still kept up an old promise to myself to buy music (rather than acquire it without paying for it, from a library or questionable download mechanisms) now that I can afford it. 

A Year of Concerts

Screen capture of 4 single value data points, followed by 2 bar charts. Single value data points are total spent on concerts attended in 2018 ($1835.04), total concerts in 2018 (48), artists seen in concert in 2018 (116 artists), and total spent on concert tickets in 2018 ($2109). The first bar chart shows the number of concerts attended per month, 2 in January, 3 in February, 2 in March, 6 in April, 4 in May, 2 in June, 3 in July, 8 in August, 4 in September, 6 in October, 5 in November, and 3 so far in December. The last bar chart is the number of artists seen by month: 5 in Jan, 10 in Feb, 3 in March, 14 in April, 8 in May, 3 in June, 8 in July, 18 in August, 9 in Sep, 22 in Oct, 10 in Nov, 6 in December.

I’ve been to a lot of concerts so far this year. 48, to be exact. I spent a lot of money on concert tickets, both for the shows I attended this year and for shows that went on sale during 2018 (but at this point, might be happening in 2019). I often will buy tickets for multiple people, so this number isn’t very precise for my own personal ticket usage.

I managed to go to at least 2 concerts every month. By the time the year is over, I’m on track to go to 51 different shows. Based on the statistics, there are some months where I went to many more than 1 show per week, and others where I didn’t. Especially apparent are the months with festivals—February, August, and October all included festivals that I attended. 

Many of those festivals brought me to new-to-me locations, with the Noise Pop Block Party and Golden Gate Park giving me new perspectives on familiar places, and Lollapalooza after shows bringing me out to Schubas Tavern for the first time in Chicago.  

Screen capture listing venues visited for the first time in 2018, with venue, city, state, and date listed. Notable ones mentioned in text, full list of venue names: Audio, The New Parish, San Francisco Belle, Schubas Tavern, Golden Gate Park, August Hall, Noise Pop Block Party, Bergerac, Great American Music Hall, Cafe du Nord, Swedish American Hall.

If you’re reading this wondering what San Francisco Belle is, it’s a boat. That’s one of several new venues that electronic music brought me to—DJ sets on that boat as part of Goldroom and Gigamesh’s tour, plus a day party in Bergerac and a nighttime set at Audio other times throughout the year.

Some of those new venue locations brought newly-discovered music to me as well.

Screen capture showing top 20 artists discovered in 2018, sorted by count of listens, featuring a sparkline to show how frequently I listened to the artist throughout the year, and a first_discovered date. List: Gilligan Moss, The Blaze, Polo & Pan, Hubert Kirchner, Keita Sano, Jude Woodhead, Ben Böhmer, Karizma, Luxxury, SuperParka, Chris Malinchak, Mumford & Sons and Baaba Maal, Jon Hopkins, Yon Yonson,  Brandyn Burnette and dwilly, Asgeir, The Heritage Orchestra Jules Buckley and Pete Tong, Confidence Man, Bomba Estereo, and Jenn Champion.

The 20th-most-popular artist I discovered this year was Jenn Champion, who opened for We Were Promised Jetpacks at their show at the Great American Music Hall. I started writing this assuming that I hadn’t heard Jenn Champion before that night, but apparently I first discovered them on July 9, but the show wasn’t until October 9. 

As it turns out, I listened to what is now my favorite song by Jenn Champion that day in July, likely as part of a Spotify algorithm-driven playlist (judging by the listening neighbors around the same time) but it didn’t stick until I saw them play live months later. The vagaries of playlists that refresh once a week can mean fleeting discoveries that you don’t really absorb.

Screen capture showing Splunk search results of artist, track_name, and time from July 9th. Songs near Jenn Champion's song in time include Mcbaise - Le Paradis Du Cuir, Wolf Alice - Don't Delete the Kisses (Tourist Remix) and Champyons - Roaming in Paris.
Other songs I listened to that day in July

Because of how I can search for things in Splunk, I was also curious to see what others songs I heard when I first discovered Hubert Kirchner, a great house artist.

Songs listened to around the same time as I first heard Hubert Kirchner's song Era.... I listened to Dion's song Dream Lover, Deradoorian's song You Carry the Dead (Hidden Cat Remix) followed by Hubert Kirchner, then listened to Miguel's song Sure Thing, How to Dress Well with What You Wanted, then listen to Rihanna, Love on the Brain, Selena Gomez with Bad Liar, and Descendents with I'm the One. I have no idea how I got into this mix of songs.

I have really no idea what playlist I was listening to that might have led to me making jumps from Sofi Tukker, to Tanlines, to Dion, to Deradoorian, then to Hubert Kirchner, Miguel, How to Dress Well, Rihanna, Selena Gomez, and Descendents. Given that August 24th was a Friday, my best guess is perhaps that it was a Release Radar playlist, or perhaps an epic shuffle session. 

Repeat of earlier screen capture showing top 20 artists discovered in 2018. Sorted by count of listens, featuring a sparkline to show how frequently I listened to the artist throughout the year, and a first_discovered date. List: Gilligan Moss, The Blaze, Polo & Pan, Hubert Kirchner, Keita Sano, Jude Woodhead, Ben Böhmer, Karizma, Luxxury, SuperParka, Chris Malinchak, Mumford & Sons and Baaba Maal, Jon Hopkins, Yon Yonson,  Brandyn Burnette and dwilly, Asgeir, The Heritage Orchestra Jules Buckley and Pete Tong, Confidence Man, Bomba Estereo, and Jenn Champion

For the top 20 bands I discovered in 2018, many of them I started listening to on Spotify, but not necessarily because of Spotify. Gilligan Moss was a discovery from a collaborative playlist shared with those that are also in a Facebook group about concert-going. I later saw them at one of the festivals I went to this year, and it even turned out that a friend knew one of the band members! Their status as my most-listened-to discovery of this year is very accurate.

 Polo & Pan was a discovery from a friend, fully brought to life with a playlist built by Polo & Pan themselves and shared on Spotify. Spent some quality time sitting in a park listening to that playlist and just enjoying life. They were at the same festival as Gilligan Moss, playing the same day, making that day a standout of my concerts this year.

Karizma was a discovery from Jamie xx’s set at Outside Lands. I tracked down the song from the set with the help of several other people on the internet (not necessarily anyone I knew) and then the song that was from the set itself wasn’t even on Spotify itself (Spotify, however, did help me discover more of the artist’s back catalog, like my other favorite song ‘Nuffin Else) Apparently I was far behind the curve hearing the song from the set, since it came out in 2017 and was featured in a Chromebook ad, but Work It Out still made me lose my mind at that set. (For the record, so did Take Me Higher, a song I did not manage to track down at all, and have so much thanks for the person that messaged me on Facebook ages later to send me the link!)

Similarly, Luxxury was a DJ I first spotted on a cruise that I went on because it featured other DJs I had heard of from college, Goldroom and Gigamesh, whom I’d discovered through remixes of songs I downloaded from mp3 blogs like The Burning Ear.

~ Finding Meaning in the Platforms ~

Many of these discoveries were deepened by Spotify, or had Spotify as a vector—through a collaborative playlist, algorithmically-generated one, or the quick back-catalog access for a new artist—but don’t rely on Spotify as a platform. I prefer to keep my music listening habits platform-adjacent. 

Spotify, SoundCloud, iTunes, Beatport and other music platforms I use help make my music experiences possible. But the artists making the music, performing live in venues that I have the privilege to live near and afford to visit, they are creating what keep my mind alive and energized.

The social platforms too, mediate the music-related experiences I’ve had, whether it’s with the people I share music and concert experiences with in a Facebook group, the people I exchange tracks and banter with in Slack channels, or those of you reading this on yet another platform. 

I like to listen to music that moves me, physically, or that arrests my mind and takes me somewhere. More now than ever I realize that musical enjoyment for me is an intense instantiation of the continuous tension-and-release pattern that exists in so many human art forms. The waves of neatness that clash and collide in a house music track, or the soaring crescendos of harmonies. 

It’s become clear to me over the years that I can’t separate my enjoyment of music from the platforms that bring me closer to it. Perhaps supporting the platforms in addition to the musical artists, performers, and venues, is just another element of contributing to a thriving music scene.

Reflecting on a decade of (quantified) music listening

I recently crossed the 10 year mark of using Last.fm to track what I listen to.

From the first tape I owned (Train’s Drops of Jupiter) to the first CD (Cat Stevens Classics) to the first album I discovered by roaming the stacks at the public library (The Most Serene Republic Underwater Cinematographer) to the college radio station that shaped my adolescent music taste (WONC) to the college radio station that shaped my college experience (WESN), to the shift from tapes, to CDs, (and a radio walkman all the while), to the radio in my car, to SoundCloud and MP3 music blogs, to Grooveshark and later Spotify, with Windows Media Player and later an iTunes music library keeping me company throughout…. It’s been quite a journey.

Some, but not all, of that journey has been captured while using the service Last.fm for the last 10 years. Last.fm “scrobbles” what you listen to as you listen to it, keeping a record of your listening habits and behaviors. I decided to add all this data to Splunk, along with my iTunes library and a list of concerts I’ve attended over the years, to quantify my music listening, acquisition, and attendance habits. Let’s go.

What am I doing?

Before I get any data in, I have to know what questions I’m trying to answer, otherwise I won’t get the right data into Splunk (my data analysis system of choice, because I work there). Even if I get the right data into Splunk, I have to make sure that the right fields are there to do the analysis that I wanted. This helped me prioritize certain scripts over others to retrieve and clean my data (because I can’t code well enough to write my own).

I also made a list of the questions that I wanted to answer with my data, and coded the questions according to the types of data that I would need to answer the questions. Things like:

  • What percentage of the songs in iTunes have I listened to?
  • What is my artist distribution over time? Do I listen to more artists now? Different ones overall?
  • What is my listen count over time?
  • What genres are my favorite?
  • How have my top 10 artists shifted year over year?
  • How do my listening habits shift around a concert? Do I listen to that artist more, or not at all?
  • What songs did I listen to a lot a few years ago, but not since?
  • What personal one hit wonders do I have, where I listen to one song by an artist way more than any other of their songs?
  • What songs do I listen to that are in Spotify but not in iTunes (that I should buy, perhaps)?
  • How many listens does each service have? Do I have a service bias?
  • How many songs are in multiple services, implying that I’ve probably bought them?
  • What’s the lag between the date a song or album was released and my first listen?
  • What geographic locations are my favorite artists from?

As the list goes on, the questions get more complex and require an increasing number of data sources. So I prioritized what was simplest to start, and started getting data in.

 

Getting data in…

I knew I wanted as much music data as I could get into the system. However, SoundCloud isn’t providing developer API keys at the moment, and Spotify requires authentication, which is a little bit beyond my skills at the moment. MusicBrainz also has a lot of great data, but has intense rate-limiting so I knew I’d want a strategy to approach that metadata-gathering data source. I was left with three initial data sources: my iTunes library, my own list of concerts I’ve gone to, and my Last.fm account data.

Last.fm provides an endpoint that allows you to get the recent tracks played by a user, which was exactly what I wanted to analyze. I started by building an add-on for Last.fm with the Splunk Add-on Builder to call this REST endpoint. It was hard. When I first tried to do this a year and a half ago, the add-on builder didn’t yet support checkpointing, so I could only pull in data if I was actively listening and Splunk was on. Because I had installed Splunk on a laptop rather than a server in ~ the cloud ~, I was pretty limited in the data I could pull in. I pretty much abandoned the process until checkpointing was supported.

After the add-on builder started supporting checkpointing, I set it up again, but ran into issues. Everything from forgetting to specify the from date in my REST call to JSON path decision-making that meant I was limited in the number of results I could pull back at a time. I deleted the data from the add-on sourcetype many times, triple-checking the results each time before continuing.

I used a python script (thanks Reddit) to pull my historical data from Last.fm to add to Splunk, and to fill the gap between this initial backfill and the time it took me to get the add-on working, I used an NPM module. When you don’t know how to code, you’re at the mercy of the tools other people have developed. Adding the backfill data to Splunk also meant I had to adjust the max_days_ago default in props.conf, because Splunk doesn’t necessarily expect data from 10+ years ago by default. 2 scripts in 2 languages and 1 add-on builder later, I had a working solution and my Last.fm data in Splunk.

To get the iTunes data in, I used an iTunes to CSV script on Github (thanks StackExchange) to convert the library.xml file into CSV. This worked great, but again, it was in a language I don’t know (Ruby) and so I was at the mercy of a kind developer posting scripts on Github again. I was limited to whatever fields their script supported. This again only did backfill.

I’m still trying to sort out the regex and determine if it’s possible to parse the iTunes Library.xml file in its entirety and add it to Splunk without too much of a headache, and/or get it set up so that I can ad-hoc add new songs added to the library to Splunk without converting the entries some other way. Work in progress, but I’m pretty close to getting that working thanks to help from some regex gurus in the Splunk community.

For the concert data, I added the data I had into the Lookup File Editor app and was up and running. Because of some column header choices I made for how to organize my data, and the fact that I chose to maintain a lookup rather than add the information as events, I was up for some more adventures in search, but this data format made it easy to add new concerts as I attend them.

Answer these questions…with data!

I built a lot of dashboard panels. I wanted to answer the questions I mentioned earlier, along with some others. I was spurred on by my brother recommending a song to me to listen to. I was pretty sure I’d heard the song before, and decided to use data to verify it.

Screen image of a chart showing the earliest listens of tracks by the band VHS collection.

I’d first heard the song he recommended to me, Waiting on the Summer, in March. Hipster credibility: intact. Having this dashboard panel now lets me answer the questions “when was the first time I listened to an artist, and which songs did I hear first?”. I added a second panel later, to compare the earliest listens with the play counts of songs by the artist. Maybe the first song I’d heard by an artist was the most listened song, but often not.

Another question I wanted to answer was “how many concerts have I been to, and what’s the distribution in my concert attendance?”

Screen image showing concerts attended over time, with peaks in 2010 and 2017.

It’s pretty fun to look at this chart. I went to a few concerts while I was in high school, but never more than one a month and rarely more than a few per year. The pace picked up while I was in college, especially while I was dating someone that liked going to concerts. A slowdown as I studied abroad and finished college, then it picks up for a year as I get settled in a new town. But after I get settled in a long-term relationship, my concert attendance drops off, to where I’m going to fewer shows than I did in high school. As soon as I’m single again, that shifts dramatically and now I’m going to 1 or more show a month. The personal stories and patterns revealed by the data are the fun part for me.

I answered some more questions, especially those that could be answered by fun graphs, such as what states have my concentrated music listens?

Screen image of a map of the contiguous united states, with Illinois highlighted in dark blue, indicating 40+ concerts attended in that state, California highlighted in a paler blue indicating 20ish shows attended there, followed by Michigan in paler blue, and finally Ohio, Wisconsin, and Missouri in very pale blue. The rest of the states are white, indicating no shows attended in those states.

It’s easy to tell where I’ve spent most of my life living so far, but again the personal details tell a bigger story. I spent more time in Michigan than I have lived in California so far, but I’ve spent more time single in California so far, thus attending more concerts.

Speaking of California, I also wanted to see what my most-listened-to songs were since moving to California. I used a trellis visualization to split the songs by artist, allowing me to identify artists that were more popular with me than others.

Screen image showing a "trellis" visualization of top songs since moving to California. Notable songs are Carly Rae Jepsen "Run Away With Me" and Ariana Grande "Into You" and CHVRCHES with their songs High Enough to Carry You Over and Clearest Blue and Leave a Trace.

I really liked the CHVRCHES album Every Open Eye, so I have three songs from that album. I also spent some time with a four song playlist featuring Adele’s song Send My Love (To Your New Lover), Ariana Grande’s Into You, Carly Rae Jepsen’s Run Away With Me, and Ingrid Michaelson’s song Hell No. Somehow two breakup songs and two love songs were the perfect juxtaposition for a great playlist. I liked it enough to where all four songs are in this list (though only half of it is visible in this screenshot). That’s another secret behind the data.

I also wanted to do some more analytics on my concert data, and decided to figure out what my favorite venues were. I had some guesses, but wanted to see what the data said.

Screen image of most visited concert venues, with The Metro in Chicago taking the top spot with 6 visits, followed by First Midwest Bank Ampitheatre (5 visits), Fox Theater, Mezzanine, Regency Ballroom, The Greek Theatre, and The Independent with 3 visits each.

The Metro is my favorite venue in Chicago, so it’s no surprise that it came in first in the rankings (I also later corrected the data to make it its proper name, “Metro” so that I could drill down from the panel to a Google Maps search for the venue). First Midwest Bank Ampitheatre hosted Warped Tour, which I attended (apparently) 5 times over the years. Since moving to California it seems like I don’t have a favorite venue based on visits alone, but it’s really The Independent, followed by Bill Graham Civic Auditorium, which doesn’t even make this list. Number of visits doesn’t automatically equate to favorite.

But what does it MEAN?

I could do data analysis like that all day. But what else do I learn by just looking at the data itself?

I can tell that Last.fm didn’t handle the shift to mobile and portable devices very well. It thrives when all of your listening happens on your laptop, and it can grab the scrobbles from your iPod or other device when you plug it into your computer. But as soon as internet-connected devices got popular (and I started using them), listens scrobbled overall dropped. In addition to devices, the rise of streaming music on sites like Grooveshark and SoundCloud to replace the shift from MediaFire-hosted and MegaUpload-hosted free music shared on music blogs also meant trouble for my data integrity. Last.fm didn’t handle listens on the web then, and only handles them through a fragile extension now.

Two graphs depicting distinct song listens and distinct artist listens, respectively, with a peak and steady listens through 2008-2012, then it drops down to a trough in 2014 before coming up to half the amount of 2010 and rising slightly.

Distinct songs and artists listened to in Last.fm data.But that’s not the whole story. I also got a job and started working in an environment where I couldn’t listen to music at work, so wasn’t listening to music there, and also wasn’t listening to music at home much either due to other circumstances. Given that the count plummets to near-zero, it’s possible there were also data issues at play.  It’s imperfect, but still fascinating.

What else did I learn?

Screen image showing 5 dashboard panels. Clockwise, the upper left shows a trending indicator of concerts attended per month, displaying 1 for the month of December and a net decrease of 4 from the previous month. The next shows the overall number of concerts attended, 87 shows. The next shows the number of iTunes library songs with no listens: 4272. The second to last shows a pie chart showing that nearly 30% of the songs have 0 listens, 23% have 1 listen, and the rest are a variety of listen counts. The last indicator shows the total number of songs in my iTunes library, or 16202.

I have a lot of songs in my iTunes library. I haven’t listened to nearly 30% of them. I’ve listened to nearly 25% of them only once. That’s the majority of my music library. If I split that by rating, however, it would get a lot more interesting. Soon.

You can’t see the fallout from my own personal Music-ocalypse in this data, because the Library.xml file doesn’t know which songs don’t point to actual files, or at least my version of it doesn’t. I’ll need more high-fidelity data to determine the “actual” size of my library, and perform more analyses.

I need more data in general, and more patience, to perform the analyses to answer the more complex questions I want to answer, like my listening habits of particular artists around a concert. As it is, this is a really exciting start.

If you want more details about the actual Splunking I did to do these analyses, I’ll be posting a blog on the official Splunk blog. That got posted on January 4th! Here it is: 10 Years of Listens: Analyzing My Music Data with Splunk.

Taylor Swift and Being Between Stars

Taylor Swift has been blowing up the music industry lately, first by surprising everyone with the beauty of her latest album. SNL dubbed it a result of Swiftamine, and I can certainly say I’m under the spell.

Then, pre-release, she removed her entire discography from Spotify. The Atlantic reflects on this decision by pointing out, “Owning music outright, instead of renting it through a streaming service, would be better for listeners and artists in the long run. Indeed, it would be better for just about everyone except Spotify.”

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The Evolution of Music Listening

Pitchfork recently published a great longform essay on music streaming. It covered the past, history, and present of music streaming, and brought up a lot of great points. These are my reactions.

The piece discussed how “the “omnivore” is the new model for the music connoisseur, and one’s diversity of listening across the high/low spectrum is now seen as the social signal of refined taste.” It would be interesting to study how this omnivority splits across genres, age groups, and affinities. I find myself personally falling into omnivore status, as I am never able to properly define my music taste according to genre, and my musical affinities shift daily, weekly, monthly, with common themes.

Also discussed is the cost of music, whether it be licensing, royalties, or record label advances. Having to deal with the cost of music is a difficult matter. I wonder if I would have been such a voracious consumer of music if I hadn’t grown up with so many free options with the library, the radio, and later, music blogs. Now that I’m older, I make the effort to purchase music when I feel the artist deserves it, but as I distance myself (incidentally, really) from storing music on my computer, that effort becomes less important to expend.

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Autobiography through (Musical) Devices (Part Rogue)

Inspired in part by Cyborgology’s Autobiography through Devices series

Autobiography Through Devices (Part 1)

Autobiography Through Devices (Part 2)

I grew up surrounded by music. Dancing wildly in the living room to REM’s Don’t Go Back to Rockville and Rusted Root’s Ecstasy with my siblings as we were toddlers remain fond childhood memories of mine. As I grew older I kept listening to my parents’ music, including an entrenched eighties phase, and as I left Junior High, I owned a Train tape, a Cat Stevens Classics CD, and Motion City Soundtrack’s first album, I Am The Movie, among others. I shied away from the popular music of my peers in Junior High, and avoided Alkaline Trio, System of a Down, and Blink 182 (this was a mistake, I might add).

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