Diving Into My Spotify Collection (Also The Science Behind Spotify)

Hello nerds.

Today we will be exploring my Spotify library with some data analytics.

I got this idea as I was trying to figure out a way to get artist information from your Spotify for another idea that I had, when I came across this site, https://exportify.net/. Using this site, anyone can log in to their Spotify account, and export playlists as a .csv file. The author of this site, Pavel Komarov, then set up a fancy dancy webpage (Jupyter notebook) where one can upload their exported playlist and do some neat analysis on it. So what I have done is added all of my liked songs (total of 6,195) into a separate playlist titled “TheCollection” and then exported it using exportify, uploaded it to the notebook, and followed his code, with some slight modifications, to produce some cool graphs. Most of the heavy lifting is done for you already so if you yourself are curious about your own musical tastes, the process shouldn’t be that hard to replicate, the only thing you would need to do is upload your own playlist!

Along the way I take two tangents and take a look behind the curtains of Spotify to see how some of the data that is analyzed is collected and what exactly they do with this data.

Let’s dive in.

First up is songs per artist.

You can see that a large proportion of my library is artists for whom I’ve only liked one or two songs. I think this reflects how I often listen to music, being more explorative of different genres and artists and rarely immersing myself in a complete discography. However, there are a handful of artists for which it seems like I’ve liked their entire collection. I had no trouble guessing the top two artists with the most liked songs, however I was surprised with some others, mainly Kanye West and Kendrick Lamar.

This graph really highlights those handful of artists that I’ve spent a good month or two really listening to their complete collection. Back in the early days of my Spotify, I went into a deep dive of Sublime’s discography (as every 20 something college kid does) and was liking virtually every song from their albums. Nowadays, I rarely listen to Sublime, but still enjoy a one off song here and there. The Beatles is the same scenario, in the early days of the pandemic (April 2020) I listened to all 13 of their albums all the way through. Naturally, my Top Songs of 2020 also had around 15 Beatles songs. Kanye West and Kendrick Lamar are a different story, I never did a deep dive for these artists as I did with the others so I am surprised with how many songs of theirs I have in my library. My only possible explanation is that they are frequent collaborators on a ton of other artist’s songs, suggesting I am more of a fan of mainstream rap/hip-hop than Kanye and Kendrick specifically. I would still listen and enjoy these artists (Sublime, The Beatles, Kanye West, Kendrick Lamar), but I wouldn’t say they are within my top 10 favorite artists. Something which I am able to say for the likes of Toro y Moi, STRFKR, Vampire Weekend, and Childish Gambino. Lets expand the ranking to see if who I think are my favorite artists make an appearance.

Looks like some of my favorite artists just miss the top 10 (Chance the Rapper, Isaiah Rashad, Hockey Dad) while some others are further down the line (Neon Indian, Fleet Foxes, Future Islands). Again some surprises (ASAP Rocky, Calvin Harris, Black Eyed Peas) and some humorous glimpses into my past musical taste (I had a weird fun. obsession in high school).

Now onto the genres and our first tangent of the day. If you use Spotify, you must have wondered at some point what the hell are all those weird genres and why are there so many of them on Spotify? Well it turns out that a culprit responsible for this madness can be singled out and his name is Glenn McDonald.

Meet the man classifying every genre of music on Spotify — all 1,387 of  them | The Star

Glenn is an employee at Echo Nest (something you will learn about later, but briefly it’s what powers everything that Spotify does) and his job is to basically create genres of music.

Not in reality, but he does get to name new genres whatever he wants which makes for some funny reading. What Spotify actually does, again more information later on in the post, is they keep track of certain features for every song in it’s database. These features are music related (i.e. tempo), but also include other things such as where in the world the artist is from and the context to which that song was listened to (what was played before it, time of day it was played, etc.). All this information is combined and using some high level computer science and math, tracks and artists are attempted to be put into clusters based off of their similarity to one another.

Gene networks offer entry point to unraveling autism | Spectrum | Autism  Research News
Cluster Map for genes with implications in autism

It is from this clustering that Spotify genres are born. When a group of songs or artists start to form a recognizable and independent cluster away from others, they can be technically called their own genre. This is where Glenn comes in with his naming. His job is to listen to the music and come up with an appropriate name for that genre. A genre that appears in my collection, “Escape Room” can be described as artists who feel “connected to trap sonically,” but are more related to “experimental-indie-r&b-pop” than they are to traditional trap. Glenn says “it just sort of felt like it was solving and creating puzzles.”. Right on Glenn.

Example of the escape room genre

So now that we know how genres are formed and why there are so many of them (5,507 as of today), I want to introduce you to Glenn’s little side project, a website that attempts to show all genres in a 2-D scatter plot of the genre space. Take a look:


The site is pretty cool. It is generally organized as those things closer to the top being more mechanical, down is more organic, left is denser and more atmospheric, right is spikier and bouncier. You can click on any genre to hear an example track that represents that genre as well as click the arrows to the right of any genre to see a map of the artists that fall into that genre. If you were ever curios what the genre of fussball (which just seems to be songs and artists singing about German football) looks like here it is:

Kinda catchy, ngl

As you can see, you can click on the buttons at the top to be taken to a variety of playlists for that genre, playlist being the master playlist for that genre, intro being a shorter playlist, pulse being what fans of that genre are currently rotating, 2020 and new being pretty self explanatory.

The site also has a lot more to offer. If you click on the other things button at the top you will be brought to a list of various off shoots of the website. If you’re bored you can spend a good couple hours exploring but here are a few of my highlights:

Every Place at Once: thousands of playlists for individual cities that are filled with songs that distinguish that city from every other city. An example for Bellingham:

Some Shakey Graves, STRFKR, Hippocampus, Babe Rainbow. Lots of good stuff. Wonder if we had anything to do with that?

Every School at Once: same idea but for universities around the world. WWU’s playlist:

The Needle: This machine basically attempts to find songs that are “rising from the depths of obscurity”. Its divided into three playlists based on the depth the machine attempts to find songs. Current is the shallowest search, while underground is the most extensive and obscure.

Back to the graphs

Turns out I have 1147 unique genres within my collection, representing 20.82 % of Spotify’s genres. Similar to my artists, there is a large proportion of genres with only one or two songs and a handful of genres with multiple hundred songs.

Not really surprised by my top ten genres, seems pretty cut and dry.

Some more obscure genres start to pop up when we look at the top 50. Some of those being “mellow gold”, “escape room” and “vapor soul”. Maybe there are some strange ones down at the bottom.

Definitely some unique names.

Looking at what songs these actually are:

Song – Artist Genres:
Collarbone – Fujiya & Miyagi Genres: alternative dance, brighton indie, electronica, neo-kraut, new rave
Feelin’ It (Salva Remix) – Umii,Salva Genres: portland indie, glitch hop, purple sound
Electric Pow Wow Drum – The Halluci Nation Genres: canadian indigenous, canadian indigenous hip hop, escape room, ottawa rap
Surrender – Josh White Genres: christian uplift
Les Champs-Elys̩es РJoe Dassin Genres: chanson, chanson paillarde, french pop
Make It Wit Chu – Queens of the Stone Age Genres: alternative metal,alternative rock,blues rock,modern rock,nu metal,palm desert scene,rock,stoner metal,stoner rock
Bad (feat. Vassy) – Radio Edit – David Guetta,Showtek,VASSY Genres: big room, dance pop, edm, pop, pop dance, classic hardstyle, electro house, euphoric hardstyle, progressive electro house, progressive house, australian dance
Switzerland – The Last Bison Genres: hampton roads indie, stomp and holler
Old Love – Joe Hertler & The Rainbow Seekers Genres: michigan indie
Okan Bale – Angelique Kidjo Genres: afropop, beninese pop, world
Varúð – Sigur Rós Genres: bow pop, dream pop, ethereal wave, icelandic rock, melancholia, nordic post-rock, post-rock
Calabria 2007 (feat. Natasja) – Enur,Natasja Genres: reggae fusion,classic danish pop, danish hip hop, danish pop

As I mentioned earlier, Spotify’s algorithms creates genres based off where that artist is from (hampton roads indie, michigan indie, beninese pop, danish hip hop) which is kind of disappointing. I would agree that this makes sense for larger geographical regions (i.e, nordic post rock, australian dance) or for certain genres such as rap and hip hop where where you’re from does have a large say in the style (southern hip hop, chicago rap). However narrowing it down to a city like Brighton just seems strange. I doubt every indie band from Brighton has the exact same sound.

Spotify also has a Popularity value that it assigns to every single song, ranging from 0 to 100, with 100 being most popular. My library has a pretty normal distribution of popularity values if you ignore the large number of songs with 0 popularity. I took a closer look at some songs with 0 popularity and I believe some of them have been misclassified or simply haven’t had an appropriate value assigned to them yet showing errors within Spotify’s framework. There are some Drake songs with 0 popularity which just doesn’t make sense.

The popularity rating also seems to have a bias for songs that are popular right now, meaning that #1 hits from decades ago will most likely have a popularity rating around ~50 which I guess makes sense. Taking a look at my top ten most popular songs confirms this.

                     Track Name                        Artist Name(s)  Popularity
       Leave The Door Open  Bruno Mars, Anderson .Paak, Silk Sonic   95
            Blinding Lights                            The Weeknd   94
                  telepatía                            Kali Uchis   92
           Save Your Tears                            The Weeknd   91
                 Rasputin                     Majestic, Boney M.   91
                        BED          Joel Corry, RAYE, David Guetta   90
   Head & Heart (feat. MNEK)                       Joel Corry, MNEK   89
                    Circles                           Post Malone   88
                  bad guy                         Billie Eilish   87
               Dance Monkey                           Tones And I   86

In order to analyze the rest of the metrics that Spotify tracks and the algorithms it uses, a little history lesson is needed.

The story starts in 2005 with the doctoral dissertations of two graduate students in the Media Arts and Science department at MIT, Tristan Jehan and Brian Whitman.

Echo Nest deal gives Spotify a local presence - The Boston Globe
Tristan Jehan
Spotify alums create Canopy content suggester that won't steal your data |  TechCrunch
Brian Whitman

These two, as I think they should forever be referenced as, gods of music discovery shared a deep interest in combining science and music during their graduate studies. In general terms, they were both attempting to break down and simplify an extremely complex language, music, into something a computer could understand and utilize.

Specifically, Tristan was interested in building tools for audio analysis, with the end goal being using these tools to create a computer that was capable of synthesizing novel pieces of music. Immediately, my mind jumped to a scenario where you come home from work, take off your shoes, pour yourself a stiff one and your robot sitting on your kitchen counter crafts up and plays you the modern day equivalent of Fur Elise while you heat up some frozen chicken nuggets in the microwave. As Tristan put it in his thesis, “Our goal is more specifically to build a machine that defines its own creative rules, by listening to and learning from musical examples.”. I am no computer science expert (side note: all theses should include a section that is limited on technical jargon and is written for the general population explaining what the thesis is about), but it is my understanding that the system Tristan built takes a ginormous database of songs, uses the tools that Tristan created to analyze each song and learn what makes a song a song, and then chops up various songs and puts it together in a novel fashion. For an example of this technology in action, visit his personal website and click on audio example:


However, the real goldmine wasn’t this computer that was capable of stitching together various bits and pieces of music, it was the tools the computer used to learn about music. Tristan realized that the tools could be used to make music more personal by understanding on a deeper level why a certain song is enjoyed by a certain person.

This particular paragraph from his thesis explaining the motivation behind his research points to this realization.

“The motivation behind this work is to personalize the music experience by seamlessly merging together listening, composing, and performing. Recorded music is a relatively recent technology, which already has found a successor: synthesized music, in a sense, will enable a more intimate listening experience by
potentially providing the listeners with precisely the music they want, whenever they want it. Through this process, it is potentially possible for our “metacomposer” to turn listeners—who induce the music—into composers themselves. Music will flow and be live again. The machine will have the capability of monitoring and improving its prediction continually, and of working in communion with millions of other connected music fans.”

It is no wonder why this guy made it with that kind of entrepreneurial thinking.

It is important to note that the “deeper level of understanding” is key here. It turns out that it is quite easy for a computer to listen to a song and extract information such as tempo, pitch and loudness. This is what Tristan and Brian call low level information. What really sets Tristan’s audio analysis tools apart, is that it combines this low level information using machine learning (taking a huge set of data and telling the computer to separate data into appropriate classifications, repeat using information learned about the accuracy of the first go, see above) to create high level information about songs such as danceability, acousticness, valence (mood). These metrics can more accurately describe a song compared to low level information such as tempo. For example, just because two songs have a medium-high tempo, does not necessarily mean they are both danceable.

A good example:

Lazy Eye – Silversun Pickups Tempo: 127 beats per minute

Thriller – Michael Jackson, Tempo: 127 beats per minute

Although if a song has a tempo of 127 it is typical for it to also be a song with high danceability (see three tracks below). Therefore, tempo has a large influence on the danceability rating but it is not everything. Instrumentation, key, even how the song is structured (no large stretches of downtempo and irregularity) which can be tracked with these tools also have effects on the danceability of a song.

Tempo: 127 bpm
Tempo: 127 bpm
Freshman Year Banger, Tempo: 127 bpm

So Tristan is the bee’s knees, but what about this Brian guy?

Well, his thesis was titled “Learning the meaning of music”. Big ask.

Much like Tristan, he was also attempting to better understand music on a deeper level. However, where their interests differs is the approach they took to understand the music. While Tristan trained a computer to listen to music to understand it, Brian trained a computer to read about music to understand it. An excerpt from his personal website explains it better than I ever could:

Can a computer really listen to music? A lot of people have promised it can over the years, but I’ve (personally) never heard a fully automated recommendation based purely on acoustic analysis that made any sense – and I’ve heard them all, from academic papers to startups to our own technology to big-company efforts. And that has a lot to do with the expectations of the listener. There are certain things computers are very good and fast at doing with music, like determining the tempo or key, or how loud it is. Then there are harder things that will get better as the science evolves, like time signature detection, beat tracking over time, transcription of a dominant melody, and instrument recognition. But even if a computer were to predict all of these features accurately, does that information really translate into a good recommendation? Usually not – and we’ve shown over the years that people’s expectation of “similar” – either in a playlist or a list of artists or songs – trends heavily towards the cultural side, something that no computer can get at simply by analyzing a signal.

This was the motivation behind Brian’s research, he believed that what people were saying about the music could tell you just as much if not more information than the information within the music itself. An interesting example of this ties back to my listening habits. Tristan’s audio analysis tools can tell you a lot about one particular song, but what about multiple songs arranged in an album, or taking it a step further an artist’s complete discography? It would be hard to pin down Toro y Moi as an artist (and any artist for that matter) with a handful of quantitative metrics. Firstly, his music can vary in sound quite dramatically, and secondly, saying he has a valence of 0.63 just doesn’t really make sense. Instead, it would be easier to pin down his music with words such as “mellow”, “groovy”, and “eclectic”. In this way you can learn more information about the music as there are often various connections and influences to other music, i.e. the culture, that will not be captured using audio analysis. So, using a lot of techniques and terms that are way over my head, he basically built a way to scour the internet for any mentions of albums, songs, artists. When any mentions are found, what is being said about the music is also picked up. This is not limited to the basic-ass generic adjectives that I used (“mellow”), but will include anything and everything that one could write about music. A funny example that kept on popping up through his thesis was:

“reminds me of my ex-girlfriend”

These terms and phrases are what Brian calls “cultural vectors”. Important to note is that when a cultural vector is written, it is picked up and given a weight score that basically says how likely it would be to use that to describe a piece of music. So for example, if I said Justin Timberlake sounds like an alley cat eating a tuna sandwich, the cultural vector “cat eating a sandwich” would (hopefully) get an extremely low weighted score because what the fuck does that sound like and who describes music like that. This is beneficial as this cultural vector is essentially useless in attempting to make connections to other pieces of music, which is really the end goal. Rather, if millions of people tweeted about how “hot” Tyler, the Creator’s new album is (tis hot), he would be associated with the term hot and connections to similar artists can be made by looking for those artists also having “hot” as one of their top cultural vectors. Each artist and track has thousands of cultural vectors which change daily as the conversation around the music changes meaning connections can change drastically as an artist evolves.

Another excerpt from his personal website that gave me hope that we are making a difference out there!

“At the time I was a member of various music mailing lists, USENET groups and frequent visitor of a new thing called “weblogs” and music news and review sites. I would read these voraciously and try to find stuff based on what people were talking about. To me, while listening to music is intensely private (almost always with headphones alone somewhere), the discovery of it is necessarily social. I figured there must be a way to take advantage of all of this conversation, all the excited people talking about music in the hopes that others can share in their discovery – and automate the process. Could a computer ‘read’ all that was going on across the internet? If just one person wrote about my music on some obscure corner of the web, then the system could know that too.”

Fuck yeah. This guy is also the bee’s knees.

So these two legends met and decided to merge their ideas and form a startup called The Echo Nest.

The Echo Nest does exactly what I have just attempted to describe, it combines Tristan’s audio analysis tools with Brian’s text mining for the culture associated with the music to generate highly personalized music recommendations. And it is the best at what it does. This is due to something that has been a mainstay in Echo Nest since day one and what Tristan and Brian call “Care and Scale”.

Scale is what it sounds like and is pretty straightforward, how big is the database from which you learn about music. If you only listened to and knew about Lady Gaga, you wouldn’t be able to accurately recommend someone Mumford and Sons. Even more importantly, if you don’t know about some small time girl with a guitar who just released a banging EP from her garage, how are you going to recommend her? Echo Nest at the time (early 2010s) was novel as it had a collection of 30 million songs that it had information for. Spotify today has an extensive database (70 million songs) that is automatically updated daily (60,000 new songs per day).

Care is what they describe as being useful for both the musician and listener and is a bit more difficult to describe. An example they provide which helps in envisioning care is to use various music discovery platforms and look at similar artists for The Beatles. Most often, the similar artists will be individual band members (John, Paul, George, Ringo) and their associated acts following the breakup of The Beatles. Is that what the listener really wants when they ask for similar artists to The Beatles? Not really, if you’re a fan of the Beatles you are most likely already going to know who John, Paul, George, and Ringo are and their various projects. Care is really where Brian’s work on the culture surrounding music comes in. Rather than using purely computer based science and mathematics to tell people who they should listen to, it really makes sense to factor in what actual humans have to say. By providing more “care” and quality assurance than any other music discovery platform, Echo Nest is able to minimize it’s “WTF count”, instances where you are in disbelief why some particular music has been recommended to you.

Get shit on Pandora and Amazon

So fast forward ten years to the early 2010s and Echo Nest has now become the de facto leader in music discovery. It is being used by clients such as MTV, Island Def Jam Records, BBC, Warner Music Group, VEVO, Nokia, and SiriusXM. In March of 2014, Spotify splashed out 100 million dollars and bought the company outright. Today, Echo Nest and the algorithms that Tristan and Brian have developed are what makes Spotify such a great music discovery and streaming service.

Now let’s take a look at the full list of the metrics that can be tracked:

Acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.

Danceability: how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.

Duration: duration of the track in milliseconds

Energy: a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.

Instrumentalness: Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.

Key: The key the track is in. Integers map to pitches using standard Pitch Class notation . E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on.

Liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.

Loudness: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db

Mode: Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.

Speechiness: detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks

Tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.

Time signature: An estimated overall time signature of a track. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure).

Valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

Here is the distribution of these metrics for the songs in my library (left) compared to the entirety of Spotify’s database (right). For all graphs shown, the distributions are largely similar suggesting my collection is representative of Spotify’s library.

This image has an empty alt attribute; its file name is music_params.png

One can rank their playlist by each of these metrics to discover some of the most extreme songs in their library.

Fastest song: The Way We Touch – WE ARE TWIN 218 bpm

Slowest Song: Tennessee Whiskey – Chris Stapleton 49 bpm

Most Acoustic: Avril 14th – Aphex Twin Acousticness: 0.996

Least Acoustic: So Tired – Hockey Dad Acousticness: 0.000001

Bottom ten were all similar Australian punk rock songs, maybe there is something about that genre that results in a low acoustic rating.

I was curious at how well the liveliness metric held up and it turns out it does just alright. In the top ten most “live” songs, 4 of them were actual live recordings. The most live song was not a live recording, Momma Told Me – EARTHGANG (0.989), but just beat out the second most live song Trenchtown Rock – Live At The Lyceum, London/1975 – Bob Marley and the Wailers (0.97)

Fantastic live version btw

Valence was another intriguing metric to look at and see how it compared to my own personal opinion. Here are the most “sad” songs in my collection.

Sansevieria GreenHouse0.0193
S.T.A.Y. Hans Zimmer0.0250
Nite RiteSTRFKR0.0290
GravitySynthetic Epiphany0.0315
MulticellularEnrico Sangiuliano0.0327
Beach 77B770.0352
L$DA$AP Rocky0.0352
Extreme Northern Lights Majeure 0.0353
The Finishing – Original Mix Stavroz 0.0363
Annika’s Theme MATRiXXMAN 0.0365

Most of these are just electronic ambient songs that I wouldn’t necessarily agree stir up any negative emotions (with some exceptions, watch Interstellar to get a good cry), rather, I would say they are devoid of emotion if that makes sense.

On the contrary, I would agree that the top ten most positive songs do fit that billing.

Pressure DropToots & The Maytals0.985
What a Fool BelievesThe Doobie Brothers0.985
The Ghost InsideBroken Bells0.983
O Sol (feat. Tita Lima)The Echocentrics, Tita Lima0.981
SeptemberEarth, Wind & Fire0.981
Demon’s CaveRoy Irwin0.981
Two Extra Pumps Of BlissEnjoy0.979
RydSteve Lacy0.979
Twist and ShoutChaka Demus & Pliers, Jack Radics, The Taxi Gang0.977
Cocaine BluesEscort0.976

Most Energetic:

Satisfaction – Benny Benassi Energy: 0.999

Least Energetic:

Clair de Lune, L. 32 – Claude Debussy,Martin Jones Energy: 0.00532

Danceability was another one of these higher order metrics which I was curious if could hold up.

Top ten most danceable songs according to Spotify:

Poor RelationsMr Twin Sister0.984
FreelanceToro y Moi0.975
Gronlandic Editof Montreal0.971
Turning AroundBreakbot0.966
Shake ThatEminem, Nate Dogg0.964
One Night In Bangkok – Single Version Björn Ulvaeus, Tim Rice, Benny Andersson, Murray0.962
So High – Acoustic Version Rebelution0.961
Blow My High (Members Only) Kendrick Lamar0.959
H.S.K.T. Sylvan Esso0.959
Island Girl Psychic Mirrors0.958

To my disappointment, the danceability metric performs just alright as well. There are some catchy tunes in the top ten but there are also some WTF instances, most notable being So High by Rebelution. Not a danceable song at all. I was surprised that none of my songs that fall under the actual “dance” genre (besides Toro y Moi) do not appear on the list. It seems like the indie pop/dance genre has more danceability. according to Spotify.

Least Danceable Songs:

Paw PrintsTango In The Attic0.0883
Clair de Lune, No. 3 Claude Debussy, Isao Tomita0.0942
VaruoSigur Ros0.12
S.T.A.Y.Hans Zimmer0.12
I ❤ U SOCassius0.1260
Giant TortoisePond0.1330
When I’m With YouBest Coast0.1350
109Toro y Moi0.1370
Hey Now!Oasis0.1400
I Don’t Wanna DieJeff Rosenstock0.148

Toro y Moi shows up again, guy has so many different sounds and nails all of them.

So there you have it. I hope you learned something today, I know I did while writing this. Now that I am aware of these metrics, I hope to test out some of my own data analysis chops and do some higher order analysis on them in the future, so stay tuned for more!

My 2020 Playlist

What up everybody

Just a bit late (3 months) but I think I can still say happy new year and cheers to making it to 2021!!! We all know that 2020 sucked major balls for the majority of us out there. However, some industries and livelihoods have been more effected than others. Musicians have been hit especially hard, specifically some of these lesser known artists that rely on small gigs to make ends meet. With that in mind, I present my long anticipated 2020 end of year playlist. I have been wanting to do a version of this playlist ever since Ryan and I first thought about blogging so I am excited that I finally found a way to make it work.

The idea for this playlist was to highlight some lesser known artists out there that made some bangers in 2020 and are on the path to superstardom. In the playlist, you can find one song from the artist’s 2020 discography and one song from an earlier piece of work (or current if they are new) that also showcases their sweet sweet sound. With an eye on the future, as things start to open up and shows resume, keep an eye out for any artists that strike your fancy. If you are lucky enough to have them stop in your city, show them some love and support and go see them do their thing. As you get groovy and boogie your butt off, remember to be appreciative of where you are and never take anything for granted!

To keep things easy for you fellas out there, I have made a mini list of 10 artists whose music from this past year I am especially fond of. Check these out first and then take a good long listen through the rest! Hope you enjoy!

1. Ivy Sole

Ivy Sole Delivers Soulful Performance at Noontime Concert - The Underground

Heavy – Southpaw EP, 2020

A track and artist that I really wanted to put first. This song about a breakup has some haunting lyrics that sums up the feelings of 2020 pretty damn well. Certified banger.

All Mine (Eden, 2016)

2. Peach Fur

Aliens | Peach Fur

Preloved – Awake EP, 2020

Favorite song of 2020, excited to see if this band makes it over to North America anytime soon

Funkn Oath (Awake)

3. Kowloon

Come Over by Kowloon ⋆ Independent Music Reviews
Yet to release an EP or album, watch out for when he does

Come Over (Single, 2020)

Wake Up (Single, 2020)

4. Scribz Riley

Scribz Riley Lyrics, Songs, and Albums | Genius
A Grammy winning producer out of London so not technically unknown but his debut solo work is not getting the plays it deserves.

Introduce Myself – Wish Me Luck, 2020

Eastside (Wish Me Luck)

5. Small Forward

Listen to "Tearjerker" by Small Forward | The Wild Honey Pie
Their self-titled album is definitely worth a listen through, another band I hope to catch in the future

Most of You – Small Forward, 2020

Out of Luck (Small Forward)

6. Babeheaven

Listen to Babeheaven's loose, dreamy new song 'Circles' | News | DIY

November – Home for Now, 2020

Seabird (Single, 2019)

7. Sault

Straight away you realise you’re in the presence of something special ... album artwork for Untitled (Rise).
Interestingly, this band has never appeared publicly and no one is sure who is actually behind the SAULT name

Wildfires – Untitled (Black Is), 2020

Why Why Why Why Why – (5, 2019)

8. Q

Prepare to Swoon Over Q's “Take Me Where Your Heart Is” – The Hidden Hits
Big fan of this guy, surprised the name Q wasn’t taken already

Take Me Where Your Heart Is – The Shaving Experiment EP, 2020

Lavender – (Forest Green 2019)

9. Surfliner

Q&A: Surfliner "Psychedelic Blues" — The Luna Collective
With their easy going surf rock, I find it really hard to believe this gang is from Massachusetts

Vertigo – Single, 2020

Surfliner – (Kiska, 2019)

10. Astro Heart

Astro Heart | Spotify
Indie Folk for the soul

Dear – Pen Pals, 2020

Astro Heart (Pen Pals)

Here is the full playlist with the above artists included. Enjoy!

Click on the link for the full playlist

Changing of Seasons (Part 2 of 2)

Hello again and happy autumn! Today is the first day of the fall season and to commemorate the occasion, I thought there would be no better way to celebrate than by making a blog post! This post is extra special as it is the much anticipated second part to my previous Quarantine Jams post (see below). Originally, I had the idea of making a playlist that touched on the highs and lows of higher education (see Campus – Vampire Weekend) as I have recently decided to get educated myself. However, this proved both difficult and boring so I scrapped that idea. My apologies if you were one of the few who read this and could not function until you saw that playlist. In the process of coming up with an alternate post, it struck me that I should try something besides a playlist! So to put this thinking into action and celebrate the changing of the seasons, I have somewhat cornily assembled a small, elite ranking of the artists that are guaranteed to get you in the mood. The mood for trudging your way through seas of orange and red leaves and visiting your nearest pumpkin patch only to get your appropriately colored tan cashmere sweater dirty with mud. As always, enjoy!

Honorable Mention:

The Head and the Heart

Subpop may just be a theme in this post

Gregory Alan Isakov

Ray LaMontagne

Kishi Bashi

Death Cab for Cutie

Wild Child

This video is not what I expected


The Shins


Artist to watch

Rainbow Kitten Suprise









3. Bon Iver

We kick off our rankings with the one and only Bon Iver, fronted by singer-songwriter Justin Vernon. I am gonna assume that almost everyone reading this knows who Bon Iver is so I will spare you from all the details of his past. However I do think it is important to point out the background for his first album, For Emma, Forever Ago as it is pretty legendary. Prior to the recording of this album, Justin was having a rough time at things. He was diseased and sick, possibly due to complications from excessive drinking, recently broke up with girlfriend at the time (Emma? No, Christie.), and did not have any future plans to make music. Feeling discouraged, it is said Justin packed up everything he owned and drove through the night strait to his hometown of Eu Claire, Wisconsin. He then traveled even further, eventually settling at his father’s hunting cabin isolated in the Wisconsin woods. Arriving at the dawn of winter in November, Justin has said that he spent the first three weeks just drinking and watching tv. During his time at the cabin, which lasted until February, Justin hunted his own food, and went into the local town to trade venison for money. After a couple weeks, he began to feel some sort of motivation and started off on crafting the tracks that would eventually make up his first album. I think this background is pretty essential to keep in mind when listening to the album. It is a lonely dark album, but it is also a genuine work of art.

Like I said, there are some incredible tracks in this album, the highlight of which is probably Bon Iver’s most famous song, Skinny Love. I can only imagine there is a bunch of anger and sadness in that song directed at his breakup. The other two aren’t bad either. This remains, for me, the best of Bon Iver so it is worth your time to stick around here for awhile. Nothing really does compare to For Emma, Forever Ago.

Here we see a glimpse of Bon Iver’s future, ditching the low fi sound of For Emma, Forever Ago in favor of grand multi-layered productions. Indeed, after the release of his first album, Justin began getting attention from artists across the musical spectrum. Most notable of those was Kanye West who invited Justin to collaborate and aid in the production of his universally acclaimed album My Beautiful Dark Twisted Fantasy. This thrust Justin’s name even more into the spotlight and he continues to work as a producer for various other artists including future Kayne collaborations.

Bon Iver’s second album, Bon Iver, is the album that really cemented the band as a name in the music industry. The album produced a total of four nominations at the 2012 Grammys, with the lead single, Holocene, being nominated for both Record and Song of the Year, and the album as a whole being nominated for and winning Best Alternative album. The band also picked up the Best New Artist award. Not bad at all for the second go at things.

Unfortunately, our time with Bon Iver ends at their third album. As hinted before, both Justin and the band, decided to change the sound of the band after the 3 year hiatus they took following the second album. What emerged from that was a vastly different Iron and Wine, favoring a more electronic and experimental sound. This can be seen in the one track I enjoy from this album, 22 (OVER Soon). From my brief personal experience, (I saw Bon Iver once at a music festival), I think the band favors their new music more than their more famous and well received earlier work so more power to them. I will say however that even if the sound changed, the emotional underpinnings that made the band famous in the first place still remain. So maybe the later albums are worth a listen if that is what you’re looking for.








2. Iron and Wine

The man himself, Samuel Beam. Surprised he is not number one? Me too. Iron and Wine has been adorning our lives with his music since the release of his first studio album, The Creek Drank the Cradle, in 2002 through Subpop records (hint, hint: this Seattle based record label also gave the number one ranked group on this list their start). My first experience with Iron and Wine I am ashamed to say was the inclusion of their hit song “Flightless Bird, American Mouth” in the original Twilight movie during Bella and Edward’s first dance at prom. Watch below if you dare.

This is peak cringe.

Actually, I take that back, in reality I love those movies and I live for ABC’s Twilight weekends, the perfect fall activity. Back to the music. Iron and Wine has, to date, released a total of six studio albums, all very well received, as well as a handful of EPs and singles. He has done numerous collaborations with other artists and has been a staple in the indie folk scene for some time now. He got his start in music later than normal, completing an undergraduate degree and getting a job as a filmography professor before recording his first album. A native of small town South Carolina and currently residing in the foothills of North Carolina, Iron and Wine often draws inspiration from his Appalachian roots in his music, the banjo and harmonica appearing throughout his discography. You probably have come across Iron and Wine tracks before and/or know who this guy is so enough of that, let’s hear some music. As his discography is huge, I won’t go through each album, instead handpicking tracks which I think are essential listening or are hidden gems. To start:

The triple threat trio right from the start.

All three of these tracks come from Iron and Wine’s first studio album, The Creek Drank the Cradle. As none of these appear on Iron and Wine’s top 10 tracks on Spotify, I have designated them as hidden gems/essential listening. These are the tracks that got me into Iron and Wine originally and they perfectly demonstrate what makes his sound so appealing. Starting with Faded from the Winter, the plucked (I’m gonna say) banjo riff that ends the song is magical. Promising Light is none the lesser, again showing off Sam’s relaxing vocals. Upward Over the Mountain, which touches on the relationship between mothers and their son, will make you cry depending on your current emotional state. It really hammers home the sound of early Iron and Wine releases.

Another track which highlights the sound of early Iron and Wine, I’ve classified this as essential listening. Interestingly, the opening guitar sounds extremely similar to the guitar in the more popular track, Naked as We Came. There is probably a relation there, seems like the Each Coming Night guitar is just slowed down, but I do not know enough music theory to say anything substantial. Carry on.

To kick off his rocking third studio album, Iron and Wine goes for a slightly different sound from his previous two releases and it’s awesome. The added percussion and bass are an appreciated touch which drives this track forward and give it some substance. Overall, this entire album is worth a listen through as it highlights the versatility of Iron and Wine’s sound.

Cover time

Although not his own songs and not released on any official studio album, I do love Iron and Wine’s various covers he has recorded through the years. These three are my favorites, with his cover of the Postal Service’s Such Great Heights coming close. Coincidently, all three cover acts from the 80s, Love Vigilantes-New Order, This Must Be The Place-The Talking Heads, and Time After Time-Cyndi Lauper. He must really like the 80s. Theses tracks are easy listening at it’s best and are a nice return to the simplicity of Iron and Wine’s earlier work.

I wanted to end with these tracks as I think it really is a nice homage to the evolution of Iron and Wine’s sound through the year. I am not particularly fond of his fourth and fifth albums, and I think this sixth album really returns to what makes Iron and Wine great but with a slight twist. He brings back the pleasant guitar strumming and mellow vocals we all know and love but has gotten rid of the stripped down, lo-fi production from his earlier work resulting in a matured sound. And we are all the better for it.










Hopefully you’ve enjoyed the post so far and are eager to continue on. If not, that’s okay, you’ve already clicked on the post so that’s enough for me. So without further ado, I give you the number one ranked artist:











1. Fleet Foxes

For me, the quintessential autumn band. Come every October, my Spotify is filled with wonderful tunes from this Pacific Northwest based band. Personal memories of runs through dense fall foliage have the folky sound of Fleet Foxes playing in the background. The band name Fleet Foxes just sounds like something fall related. In actuality, lead singer and band founder Robin Pecknold chose the name as it was “evocative of some weird English activity like fox hunting”. It works for me.

Inspiration for the name Fleet Foxes

The band’s two earliest members, Robin Pecknold and Skyler Skjelset, met while attending high school in the Seattle suburb of Kirkland. They quickly became friends over their shared taste in music and began playing music together. As time went on, the two added three other members to the band and began officially going by Fleet Foxes in early 2006. To date, actually as of literally today, they have released four studio albums, each one to universal acclaim. Their second studio album, Helplessness Blues, went on to receive a Grammy nomination for Best Folk Album at the 2012 Grammy’s, ultimately losing out to the Civil Wars first studio album Barton Hollow. Let’s take a look at the numerous highlights from their illustrious discography.

Fleet Foxes, June 2008

After two EPs which garnered the band both local and international recognition, the Fleet Foxes came onto the indie folk music scene with the release of their self titled debut album in the summer of 2008. I’ve always enjoyed the album art for this album as at first glance it seems like a normal painting depicting your very typical medieval village. Take a closer look, and the scene is senseless. The real life painting from which the album art is derived is supposed to be an expression of various Dutch proverbs and idioms from the 15th century so there appears to be actual meaning behind the madness. Nonetheless, the music is what we are here for. If I could link every single track from this album, I would, but that would make for a very long blog post so I’ll do my best to pick and choose. I strongly recommend giving the album a full listen through, especially on a lazy rainy day, you won’t be the same!

The first song I ever heard from this band and perhaps their most well known. Melodic vocals and tambourines, it does not get much better.
A stripped down acoustic performance by Robin Pecknold. A song about murder and death (I think ?), I’ve always wondered if the Seattle local chose the name Tiger Mountain Peasant Song in reference to Tiger Mountain in Issaquah, Washington where the notorious serial killer Ted Bundy buried some of his victims. Spooky.
One of the less popular tracks from the album but one of my personal favorites. Humorous observation, Robin did not change jackets from the last performance to the filming of this music video, looks like he is going for the smelly, haven’t showered in weeks look.

Helplessness Blues, May 2011

Just as good as the first one. Yes. Better than the first one? Possibly. After extensive touring to promote the release of their first album, the band eventually settled down in New York to record their sophomore effort. Some changes were made to the lineup between this album and the last, with the addition of a new bass player who doubles as the woodwind instrumentalist. At this time the band’s drummer, John Tillman, better known as Father John Misty, was also beginning to break out and put attention on more solo pursuits, putting strain on the band and the recording process. Despite the internal strife, the band pulled through and produced their highest charting album to date. Some interesting tidbits I found while doing some research, the band wanted a less poppy, more imperfect sound for this album, so supposedly, all the tracks’ vocals were recorded in one take, taking inspiration from the Van Morrison album Astral Weeks. Also to note, the attention that lead singer, Robin Pecknold, had to give to making this album led his then girlfriend to break up with him. However, after hearing the finished product, she decided to get back together with the lead singer! To be a musician…

A live performance from 2011 just after the release of the album, really enjoy the lyrics in this one as well as the images they evoke.
An enjoyable light and whimsical tune. The harmonious vocals are again a major appeal, as well as the rough twangy sounding violin? fiddle? that appears here and there throughout the track.
The title track deals with the transition from childhood to adulthood and trying to find one’s place in this crazy world. The lyrics are especially poignant and contain some of my personal favorite lines from all the work Fleet Floxes has ever done. To list a couple:
“If I know only one thing, it’s that everything that I see
Of the world outside is so inconceivable often I barely can speak”
“If I had an orchard, I’d work ’til I’m sore
And you would wait tables and soon run the store
Gold hair in the sunlight, my light in the dawn
If I had an orchard, I’d work ’til I’m sore”

Crack Up, June 2017

After the release and touring of Helplessness Blues and their drummer, John Tillman, leaving, the band took a three year hiatus. During this time major changes occurred, both to the band and its members. Lead singer, Robin Pecknold, decided to get educated himself and moved to NYC to start his undergraduate degree at Columbia. He states that he was inspired to go to college by meeting interesting people outside his musical bubble that shared their unique perspectives. This experience was very illuminating for him in which he was able to develop an idea of where he wanted to take the Fleet Foxes sound. During this time, the band also changed labels, transitioning to Nonesuch Records. For me, this album definitely drifts away from the medieval and organic sound than the Fleet Foxes from the previous two projects. I believe there is more emphasis on production with the tracks coming off as more well produced I guess. Ever present is the harmonious vocals we’ve grown to love through our time with the band, so do not fret, the old Fleet Foxes are still there.

The longest Fleet Foxes track I’ve come across this double whammy starts off the third album with a rollercoaster of intensity and emotion. As mentioned before, this more put together sound was a nice break from the endless folk tracks in the previous albums. Robin really shows off his vocals on this one, which is always a treat.
One of my personal favorites, this mellow track just sounds nice. I am unsure of the meaning behind the lyrics, but I do know, if you need to, you can keep time on me.
Another personal favorite, this song highlights the production I’ve been mentioning that has become a signature of later Fleet Foxes releases. Specifically, the transition at 2:05 from the stripped down, mellow sound to the grooving and rhythmic later portion.

Shore, September 2020

Which brings us to the current album of interest, Shore. I’ve only done a once through on this album and my consensus is still not made up so I’ve decided that it is now you, the reader, who gets to say something. Give the album a listen and comment at the end of this post your thoughts on the album or any tracks you particularly enjoyed. Go on don’t be shy!




So that’s it. Hopefully this post has been halfway enjoyable and has inspired you to get into the mood of the season and listen to some of your own favorite autumn artists. Go out there, jump in the leaves, put on your sweaters, harvest that corn, enjoy all this wonderful season has to offer. Bye for now.

Quarantine Jams (Part 1 of 2)

First off, apologies for the extended period of time between blog posts! With the world grinding to a halt in March, I’ve just been to busy sitting at home and staring at my phone these past 5 months to come up with a post! Jokes aside, the loyal contributors to this blog, Ryan and myself, have both recently made courageous, fiery and stupid? (TBD) leaps into the depths of graduate education in our respective fields amidst the chaos that is the global pandemic. Without giving too much away for the sake of personal privacy, Ryan has packed up and moved cross country, primarily to attend a storied institution of law, but also equally important, to engage in the local custom that is hoeing down in your cowboy boots every Friday night (expect more country to be showing up in his posts). I myself meanwhile, have haphazardly and carelessly thrown everything I own into my four door sedan and moved myself a mere hour north to the other side of a lowland river delta. Just so happens that I also crossed international boundaries and find myself in a large, vibrant new city that more or less shares the same culture I’ve lived in the past 15 years. To commemorate both our academic endeavors and the federally mandated 14 day quarantine I currently am experiencing, I have come up with a two part playlist set that will hopefully prevent you from both, banging your head against a wall from boredom and banging your head into a textbook from mental fatigue! Such fantastic times we live in! As always, happy listening.

Best Albums of the ’10s

As a new decade emerges, another fades into darkness. To celebrate the terrible teen years, here are what I consider to be the 20 best albums released from January 1, 2010 to December 31, 2019 (2013 was quite the year).  Because I am incapable of being decisive, ten additional albums are included in the honorable mention category. As I have no business compiling these into a ranked list, I have arranged albums by release date, taking you on a musical nostalgic train ride.  Below each entry is a track from the album that I particularly enjoyed, so naturally you should enjoy it too. A playlist with all these tracks compiled can be found at the end. Enjoy!

February 17, 2010

Tourist History – Two Door Cinema Club

Highlight track: Undercover Martyn

May 18, 2010

Brothers – Black Keys

The Black Keys - Brothers.jpg

Highlight track: Never Gonna Give You Up

November 22, 2010

My Beautiful Dark Twisted Fantasy – Kanye West

File:My Beautiful Dark Twisted Fantasy.jpg

Highlight Track: Runaway

May 23, 2011

Torches – Foster the People

Torches foster the people.jpg

Highlight Track: I Would Do Anything For You

May 25, 2012

An Awesome Wave – alt-J

Alt-J - An Awesome Wave.png

Highlight Track: Tessellate

November 9, 2012

Flume – Flume

Purple album artwork

Highlight Track: Sleepless

January 15, 2013

Anything in Return – Toro y Moi

Toro y Moi - Anything in Return.png

Highlight Track: So Many Details

February 19, 2013

Miracle Mile – STRFKR

Miracle Mile album cover.jpg

Highlight Track: Kahlil Gibran

April 30, 2013

Acid Rap – Chance the Rapper

Chance the rapper acid rap.jpg

Highlight Track: Lost

May 14, 2013

Modern Vampires of the City – Vampire Weekend

Vampire Weekend - Modern Vampires of the City.png

Highlight Track: Hannah Hunt

May 31, 2013

Settle – Disclosure

Disclosure - Settle.png

Highlight Track: Together

October 8, 2013

Melophobia – Cage the Elephant

Cage the Elephant Melophobia.jpg

Highlight Track: Come a Little Closer

March 24, 2014

Singles – Future Islands


Highlight Track: Like the Moon

July 17, 2015

Currents – Tame Impala

Tame Impala - Currents.png

Highlight Track: Let It Happen

October 16, 2015

VEGA INTL. Night School – Neon Indian

Highlight Track: Dear Skorpio Magazine

January 15, 2016

Malibu – Anderson .Paak


Highlight Track: The Season | Carry Me

September 2, 2016

The Sun’s Tirade – Isaiah Rashad

Highlight Track: Rope // rosegold

December 2, 2016

“Awaken, My Love!” – Childish Gambino

Highlight Track: Stand Tall

January 13, 2017

I See You – The xx

The xx I See You Album Cover.jpg

Highlight Track: Replica

July 21, 2017

Flower Boy – Tyler, The Creator

Tyler, the Creator - Flower Boy.png

Highlight Track: Garden Shed

Honorable Mention:

This is Happening – LCD Soundsystem Highlight Track: Dance Yrself Clean


Broken Bells – Broken Bells Highlight Track: October

A pink spherical Chinese paper lantern

Random Access Memories – Daft Punk Highlight Track: Doin’ it Right

File:Random Access Memories.jpg

Helplessness Blues – Fleet Foxes Highlight Track: Bedouin Dress

FleetFoxesHelplessness Blues2011.jpg

Some Rap Songs – Earl Sweatshirt Highlight Track: Ontheway!

Some Rap Songs.jpg

Barchords – Bahamas Highlight Track: Caught Me Thinkin

Image result for barchords bahamas

AM – Arctic Monkeys Highlight Track: No. 1 Party Anthem

File:Arctic Monkeys - AM.png

Landmark – Hippo Campus Highlight Track: Western Kids

Image result for landmark hippo campus

Down To Earth – Flight Facilities Highlight Track: Crave You

Flight Facilities - Down to Earth.jpg

Light Upon The Lake – Whitney Highlight Track: No Woman


Favorite First Tracks on Debut Albums

To celebrate the first official post of this wondrous blog, my compadre Ryan came up with the playful idea to make a playlist of our favorite first tracks on first albums. We decided to exclude any extended plays (EPs) and only include full length debut albums. This knocked out many songs I wanted to share with the void by relatively lesser known artists, but do not worry, I got something up my sleeve for that later. So without further ado, here are my favorite 25 first tracks on debut albums by artists I love to listen to. Criticism is encouraged.