It’s been a long time since the Hottest 100 of 2016 was aired. Unfortunately, I never really got around to publishing some analysis I performed on the prediction results. Fortunately, I managed to find some time recently!
Looking from afar, the results don’t look fantastic (when you compare them to my results from 2015 at least). The prediction unfortunately predicted the top two places out of order, however did manage to predict the third place correctly.
Lets take a look at the Top 10 of Triple J’s list and match it up with 100 Warm Tunas:
Looking at this we see most predictions we can find some learnings:
The average error for the top ten rank was 1.9 rank positions.
If 100 Warm Tunas ignored rank and simply guessed the top ten, it would have predicted 8 of the top 10 songs.
If 100 Warm Tunas ignored rank and simply guessed the top 3 songs to win, it would have predicted all 3 songs. Woo!
Lets dive into a chart that shows error for all ranks:
From this chart, we can deduce that the further away from position 1 we become, the higher the error. This information alone isn’t very useful. We can get a better understanding of error by finding the average for each ranking group:
As we get closer to rank 1, the results become more and more accurate, however they are not perfect. This is more obvious if we use a scatter plot to compare Triple J ranks against Warm Tunas predictions:
It’s clear now that as we get closer to rank 1, the 100 Warm Tunas prediction gets better and converges upon the actual rankings played out on the day. However, unfortunately this year the difference between rank 1 and rank 2 was way too close to call - just 0.67% of voting volume was separating the two. A difference that was not enough to provide an accurate prediction of the winner.
Overall, whilst 100 Warm Tunas 2016 did get the two top positions out of order, it’s understandable as to why this happened. Hopefully this year there is a greater difference between ranks, giving further ability to predict the winner in position #1.
I’ve been doing a fair bit of DIY home automation hacking lately across many different devices - mostly interested in adding DIY homekit integrations. A couple of months ago, my dad purchased a bulk order of RAEX 433MHz RF motorised blinds to install around the house, replacing our existing manual roller blinds.
Note: If you are based in Australia, you can purchase these in bulk or individually via www.raexaustralia.com (Full disclosure – my father runs the site).
The blinds are a fantastic addition to the house, and allow me to be super lazy opening/closing my windows, however in order to control them you need to purchase the RAEX brand remotes. RAEX manufacture many different types of remotes, of which, I have access to two of the types, depicted below:
R Type Remote (YRL2016)
X Type Remote (YR3144)
Having a remote in every room of the house isn’t feasible, since many channels would be unused on these remotes and thus a waste of $$$ purchasing all the remotes. Instead, multiple rooms are programmed onto the same remote. Unfortunately due to this, remotes are highly contended for.
An alternate solution to using the RAEX remotes is to use a piece of hardware called the RM Pro. This allows you to control the remotes via your smartphone using their app
The app is slow, buggy and for me, doesn’t fit well into the home-automation ecosystem. I want my roller blinds to be accessible via Apple Homekit.
In order to control these blinds, I knew I’d need to either:
Reverse engineer how the RM Pro App communicated with the RM Pro and piggy-back onto this
Reverse engineer the RF protocol the remotes used to communicate with the blinds.
I attempted option 1 for a little while, but ruled it out as I was unable to intercept the traffic used to communicate between the iPhone and the hub. Therefore, I began my adventure to reverse engineer the RF protocol.
I purchased a 433MHz transmitter/receiver pair for Arduino on Ebay. In case that link stops working, try searching Ebay for 433Mhz RF transmitter receiver link kit for Arduino.
Initial Research
A handful of Google searches didn’t yield many results for finding a technical specification of the protocol RAEX were using.
I could not find any technical specification of the protocol via FCC or patent lookup
Emailed RM Pro to obtain technical specification; they did not understand my English.
Emailed RAEX to obtain technical specification; they would not release without confidentiality agreement.
I did find that RFXTRX was able to control the blind via their BlindsT4 mode, which appears to also work for Outlook Motion Blinds.
After opening one of the remotes and identifying the micro-controllers in use, I was unable to find any documentation explaining a generic RF encoding scheme being used.
Once my package had arrived I hooked up the receiver to an Arduino and began searching for an Arduino sketch that could capture the data being transmitted. I tried many things that all failed, however eventually found one that appeared to capture the data.
Once I captured what I deemed to be enough data, I began analysing it. It was really difficult to make any sense of this data, and I didn’t even know if what had been captured was correct.
I did somefurtherreading and read a few RF reverse engineering write-ups. A lot of them experimented with the idea of using Audacity to capture the signal via the receiver plugged into the microphone port of the computer. I thought, why not, and began working on this.
This captures a lot of data. I captured 4 different R type remotes, along with 2 different X type remotes, and to make things even more fun, 8 different devices pairings from the Broadlink RM Pro (B type).
From this, I was able to determine a few things
The transmissions did not have a rolling code. Therefore, I could simply replay captured signals and make the blind do the exact same thing each time. This would be the worst-case scenario if I could not reverse engineer the protocol.
The transmissions were repeated at least 3 times (changed depending on the remote type being used)
Zooming into the waveform, we can see the different parts of a captured transmission. This example below is the capture of Remote 1, Channel 1, for the pairing action:
Zooming in:
In the zoomed image you can see that the transmission begins with a oscillating 0101 AGC pattern, followed by a further double width preamble pattern, followed by a longer header pattern, and then by data.
This preamble, header and data is repeated 3 times for R type remotes (The AGC pattern is only sent once at the beginning of transmission). This can be seen in the first image.
Looking at this data won’t be too useful. I need a way to turn it digital and analyse the bits and determine some patterns between different remotes, channels and actions.
Decoding the waveform.
We need to determine how the waveform is encoded. It’s very common for these kinds of hardware applications to use one of the following:
Raw? high long = 11, high short = 1, low long = 00, low short = 0?
By doing some research, I was able to determine that the encoding used was most likely manchester encoding. Let’s keep this in mind for later.
Digitising the data
I began processing the data as the raw scheme outlined above (even though I believed it was manchester). The reason for this is that if it happened to not be manchester, I could try decode it again with another scheme. (Also writing out raw by hand was easier than doing manchester decoding in my head).
I wrote out each capture into a Google Sheets spreadsheet. It took about 5 minutes to write out each action for each channel, and there were 6 channels per remote. I began to think this would take a while to actually get enough data to analyse. (Considering I had 160 captures to digitise)
I stopped once I collected all actions from 8 different channels across 2 remotes. This gave me 32 captures to play with. From this much data, I was able to infer a few things about the raw bits:
Some bits changed per channel
Some bits changed per remote.
Some bits changed seemingly randomly for each channel/remote/action combination.
Could this be some sort of checksum?
I still needed more data, but I had way too many captures to decode by hand. In order to get anywhere with this, I needed a script to process WAV files I captured via Audacity. I wrote a script that detected headers and extracted data as its raw encoding equivalent (as I had been doing by hand). This script produced output in JSON so I could add additional metadata and cross-check the captures with the waveform:
Once verified, I tabulated this data and inserted it into my spreadsheet for further processing. Unfortunately there was too many bits per capture to keep myself sane:
I decided it would be best if I decoded this as manchester. To do this, I wrote a script that processes the raw capture data into manchester (or other encoding types). Migrating this data into my spreadsheet, it begins to make a lot more sense.
Looking at this data we can immediately see some relationship between the bits and their purpose:
6 bits for channel (C)
2 bits for action (A)
6 bits for some checksum, appears to be a function of action and channel. F(A, C)
Changes when action changes
Changes when channel changes.
Cannot be certain it changes across remotes, since no channels are equal.
1 bit appears to be a function of Action F(A)
1 bit appears to be a function of F(A), thus, G(F(A)). It changes depending on F(A)’s value, sometimes 1-1 mapping, sometimes inverse mapping.
After some further investigation, I determined that for the same remote and channel, for each different action, the F(A, C) increased by 1. (if you consider the bits to be big-endian.).
Looking a bit more into this, I also determined that for adjacent channels, the bits associated with C (Channel) count upwards/backwards (X type remotes count upwards, R type remotes count backward). Additionally F(C) also increases/decreases together. Pay attention to the C column.
From this, I can confirm a relationship between F(A, C) and C, such that F(A, C) = F(PAIR, C0) == F(PAIR, C1) ± 1. After this discovery, I also determine that there’s another mathematical relationship between F(A, C) and A (Action).
Making More Data
From the information we’ve now gathered, it seems plausible that we can create new remotes by changing 6 bits of channel data, and mutating the checksum accordingly, following the mathematical relationship we found above. This means we can generate 64 channels from a single seed channel. This many channels is enough to control all the blinds in the house, however I really wanted to fully decode the checksum field and in turn, be able to generate an (almost) infinite amount of remotes.
I wrote a tool to output all channels for a seed capture:
My reasoning behind generating more data was that maybe we could determine how the checksum is formed if we can view different remotes on the same channel. I.e. R0CH0, R1CH0, X1CH0, etc…
Essentially what I wanted to do was solve the following equation’s function G:
F(ACTION_PAIR, CH0) == G(F(ACTION_PAIR, CH0))
However, looking at all Channel 0’s PAIR captures, the checksum still appeared to be totally jumbled/random:
Whilst looking at this data, however, another pattern stands out. G(F(A)) sits an entire byte offset (8 bits) away from F(A). Additionally the first 2 bits of F(A, C) sit at the byte boundary and also align with A (Action). As Action increases, so does F(A, C). Lets line up all the bits at their byte boundaries and see what prevails:
Colours denoting byte boundaries
Aligned boundaries
From here, we need to determine some function that produces the known checksum based on the first 4 bytes. Initially I try to do XOR across the bytes:
Not so successful. The output appears random and XOR’ing the output with the checksum does not produce a constant key. Therefore, I deduce the checksum isn’t produced via XOR. How about mathematical addition? We’ve already seen some addition/subtraction relationship above.
This appeared to be more promising - there was a constant difference between channels for identical type remotes. Could this constant be different across different type remotes because my generation program had a bug? Were we not wrapping the correct number of bits or using the wrong byte boundaries when mutating the channel or checksum?
It turns out that this was the reason 😑.
Solving the Checksum
Looking at the original captures, and performing the same modulo additions, we determine the checksum is computed by adding the leading 4 bytes and adding 3. I can’t determine why a 3 is used here, other than RAEX wanting to make decoding their checksum more difficult or to ensure a correct transmission pattern.
I refactored my application to handle the boundaries we had just identified:
typeRemoteCodestruct{LeadingBituint// Single bitChanneluint8Remoteuint16Actionuint8Checksumuint8}
Looking at the data like this began to make more sense. It turns out that F(A) wasn’t a function of A (Action), it was actually part of the action data being transmitted:
Additionally, the fact there is a split between channel and remote probably isn’t necessary. Instead this could just be an arbitrary 24 bit integer, however it is easier to work with splitting it up as an 8 bit int and a 16 bit int. Based on this, I can deduce that the protocol has room for 2^24 remotes (~16.7 million)! That’s a lot of blinds!
My remote-gen program was good for the purpose of generating codes using a seed remote (although, incorrect due to wrapping issues), however it now needed some additional functionality.
I needed a way to extract information from the captures and verify that all their checksums align with our rule-set for generating checksums. I wrote an info command:
Running with --validate exits with an error if the guessed checksum != checksum. Running this across all of our captures proved that our checksum function was correct.
Another piece of functionality the tool needed was the ability to generate arbitrary codes to create our own remotes:
./remote-gen create --channel=196 --remote=54654 --verbose
00010001101111110101010111111111010011001 Action: PAIR
00010001101111110101010110011111101101000 Action: DOWN
00010001101111110101010111011111111101000 Action: STOP
00010001101111110101010110111111100011000 Action: UP
I now can generate any remote I deem necessary using this tool.
Wrapping Up
There you have it, that’s how I reverse engineered an unknown protocol. I plan to follow up this post with some additional home-automation oriented blog posts in the future.
From here I’m going to need to build my transmitter to transmit my new, generated codes and build an interface into homekit for this via my homebridge program.
As mentioned above, if you are based in Australia, you can purchase these blinds and associated accessories in bulk or individually via www.raexaustralia.com (Full disclosure – my father runs the site)
Results are collected, optimised, and processed multiple times per day. Instagram images tagged with #hottest100 and a few others are included for counting.
Happy voting!
You can read about the process last year here. However, vote collection is a fair bit more accurate this year.
As a casual bike rider, I enjoy tracking my rides with Strava
so I can take a look at how my ride went and how well I performed throughout.
However, very rarely the Strava tracking application randomly
crashes, or gets killed by iOS on my phone, during the ride. This means
that the data was never recorded between the point at which the app died and
the point when I became aware the app had died.
If we plot this type of failure, it looks something like this:
Fortunately in this case, there wasn’t too much missing data. However, I was
still determined to learn about the GPX format and see if I could patch up the
GPX file programatically.
In the specific case of the above map, I was riding north west, and at a point
Strava crashed. Between this point and when I pulled out my phone to check my
progress, no points were plotted. Google maps interprets this lack of data as
a straight line between to the 2 points (as per GPX specification).
If we crack open the GPX file and take a look, we can see exactly what this looks
like:
In it’s simplest form, a GPX file is an XML document that contains a sequence
of GPS points (with associated metadata like elevation, and other depending
on the tracker). This makes it reasonably simple for us to get our hands
dirty and begin fixing the data set.
In order to add the missing data back into the GPX file, we need 3 things:
The last coordinate recorded before the app crashed
The coordinate when the app was revived
A list of points of the track we want to use for our data points.
Fortunately, I was able to obtain a list of coordinates for the missing data
since I travelled the same path on the return journey (As can be seen on the
map above).
The other 2 app state points of interest are reasonably easy to find - just
find 2 data points that have a (reasonably) large time distance between them.
In order to process the data, I used a python library called gpxpy which
provided some good utilities for reading and processing a GPX file.
With this library, I was able to find the crash point, the revival point, and
the list of the points of the track. With this data, I interpolated the start/end
times of the crash points onto the track data, and spliced it back into the
dataset.
After exporting the data set, we achieve a map that looks like:
Quite clearly, this has a few limitations, for example, the calculated velocity
through all of the data points is simply an average. However, this did provide
me with an improved dataset which I could re-upload to Strava.
You can find all the source for this script
on my github
In 2014, a prediction was accurately made for the Hottest 100 of 2013. The results were posted on warmest100.com.au.
The author of the prediction in 2014 managed to acquire accurate results because Triple J featured a social share button on their voting page, which posted your votes to your Facebook in text form. The author scraped results from public Facebook posts and aggregated all the votes. They managed to obtain 1.3% (1779 entries) of the expected total vote.
Consequently, voting for the Hottest 100 2014 and 2015 did not contain such a feature. Fortunately, voters still felt the need to share these results with their friends, and taking a screen shot or a photo of their screen and posting to social media was a concrete alternative. Using these images posted to Instagram, I was able to accurately predict the results of Triple J’s Hottest 100 of 2015.
~4900 images contained the words “vote/votes/voting”
My Top 3 Results were 100% accurate
You’ll probably find this article interesting, but if you’re super eager, you can Skip To The Results.
Taking Advantage of Social Media
I decided to only target votes that were posted to Instagram, since a high majority of the pictures hashtagged with #hottest100 were in fact votes, and there was a reasonably high volume of them, and most publicly accessible.
I required means to acquire all pictures that had been posted to Instagram. Instagram have an official API, however you are required to have your API app usage approved before it can interface with non-sandbox users. Additionally, Instagram impose a rate limit on non-approved apps, as well as approved apps. I did not have time to waste, and wanted results immediately, so I found an alternative.
Fortunately, Instagram exposes a non-public API through their website ajax loading when you browse to a hashtag. By imitating the web browser with a simple python script using the requests library I managed to download all images from the latest until a cut off date that I specified (the day voting opened).
After scraping the hashtag #hottest100, I expanded my search to #hottest1002015 and #triplejhottest100.
Processing Images
After downloading 7191 images from Instagram, I needed to find an accurate way to filter out the images that were not votes.
I’ve had previous experience with using PIL in Python, so using PIL, I wrote a simple script to sort the photos into 2 categories; photos that appeared white-ish, and photos that were not.
A good vote looked like this:
Unfortunately, not every image ended up in the right folder, and I ended up with both false negatives and false positives, however I wasn’t too concerned about false positives, as my OCR processing step would exclude them. Instead, I was more concerned about false negatives.
As the image processing and sorting continued, I manually moved false negatives to the positives folder. I calculated about 5% of the non-matching photos were incorrectly classified, however this was due to them being pictures taken of computer screens, similar to the photo below:
Some image statistics:
7191 images collected initially
1662 images categorised as non-votes
5529 images categorised as votes
~4900 images contained the words vote/votes/voting
Improving OCR Performance
After experimenting on raw photos from Instagram, I found that OCR accuracy was not very accurate. To remediate this, I utilised Imagemagick to flatten image definition to improve text results.
Bringing in Tesseract (OCR)
After weeding out the junk, I still needed to turn these images into readable text.
Unfortunately, due to the layout of the Hottest 100 voting website the two columns were broken up inconsistently over the results.
Some were processed as:
...
Flight Facilities
Hayden James
Hermilude
Major Lazer
RUFUS
Weeknd, The
ZHU x Skrillex x THEY.
Jarryd James
Disclosure
Kendrick Lamar
Heart Attack {FL Owl Eyes)
(Radio Edit)
Something About You
The Buzz (Ft. Malaya/Young
Tapz}
Lean On (Ft. Mé/DJ Snake}
Innerbloom
...
And others processed as:
...
Lucky Luke 1 Day
Mosquito Coast Call My Name
Tn ka Right By You
Tuka L.D.T.E.
Half Moon Run Trust
Spring King City
Tame Impala Let It Happen
Saskwatch I‘ll Be Fine
Jungle Giants. T Kooky Eyes
he
...
And others just did not process at all, due to resolution, colour, skewing, or simply because they were a photo of a computer screen:
'VHotllne Bling
Regardless (Ft. Julia Stone)
Parsing the Results
I processed the results line by line, and call these “terms”. These such terms could contain a single song title, a single artist, an artist name with song name, or just junk overhang from a previous line. Initially there were 31062 uncategorised terms.
I processed each term and aggregated number of results for each. This worked really well for songs with short names that were less prone to error, such as Hoops, however did not correctly capture terms where artist name and song name occurred on the same line, or where the OCR library interpreted a few characters incorrectly.
OCR Inaccuracy & Levenshtein
Even with photo enhancements, the OCR accuracy was somewhat subpar for some votes. Some l’s were interpreted as t’s, i’s as l’s, etc. Additionally, the longer the name of the song, the more prone to error it was.
Fiesh Without Blood
L D R U Keepmo Score Fl Pavqe IV)
Yam: unpala The Les I Knew The Bauer
The Tlouble Wilh US
A technique that can be used to fix these spelling errors of single/multi character errors is the Levenshtein algorithm for edit distance. Using this algorithm, we can compare 2 strings and determine how many edits need to be made to make the strings equal each other.
In order to perform this kind of matching, we needed an accurate list of songs that were released this year, along with a list of artists that released music this year.
Using Spotify To Help
To acquire an accurate list of songs released this year, I used Spotify and crawled various playlists from 2015. These included Spotify Charts, Triple J Hitlist, and various other genre-alike playlists.
In the end I ended up with a songs list with 1781 songs, and an artists list with 1229 artists. After the Hottest 100 aired, I compared the results of the countdown to the songs found in my list, and only 6 songs that occurred in the hottest 100 were not in my “truth” list.
During list gathering, I made sure to convert all unicode characters to their ASCII counterparts, so that characters with accents and similar would be matched correctly.
Continuing Processing
Now carrying reasonably accurate artists and songs lists we continue categorisation and processing. The processing algorithm worked in the following way:
Load all terms from every image’s .txt OCR result. Every line is a “term”.
Clean all the terms by turning them into lowercase and stripping whitespace.
Loop through each term:
If term exists in our known songs list, move the term to the songs aggregation and count the votes.
If term exists in our known artists list, move the term to the artists aggregation and count the votes.
If couldn’t find it in either of those:
Loop through all artists in our artist known artist list.
Check if the term starts with the current artist. If it does split it into artist and unknown term. Add the votes to the artist aggregation.
If matched artist, check if the new unknown term exists in the songs list, if it does, add it to the songs aggregation. If not, add it back to the unknown. break loop.
If it didn’t have a prefixed artist, just add it back to the unknown terms.
At this stage, we have a reasonably accurate aggregation of results. We have not yet used Levenshtein string matching. We now have 27294 uncategorised terms, down from 31062 uncategorised terms. So far our results:
== Results ==
1 Hoops 998
2 King Kunta 765
3 Lean On 750
4 The Buzz 646
5 Like Soda 568
6 Never Be 484
7 Let It Happen 476
8 Magnets 465
9 Do You Remember 409
10 Ocean Drive 405
== 853 unique terms ==
== Top Unknown Terms ==
1 Your Hottest 100 Votes: 2279
2 Your Votes 2127
3 } 320
4 Hottest Io 248
5 V 231
6 Throne 222
7 Triple J? 209
8 D] Snake 203
9 The Less | Know The Better 203
10 Asap Rocky 199
== 27294 unique terms ==
However, we still haven’t aggregated any votes that had spelling errors due to OCR inaccuracies.
Employing the Levenshtein algorithm, we continue to process the unknown terms. I configure matching to allow lenience based on the length of the term - the maximum edits that were allowed was 2/5 * length of term. The process continues:
For all unknown terms:
Check term length > 3. Break if <= 3. Can’t match a short string.
Match Songs:
Loop through all songs in known songs list:
Compare current song to current term. Get edit distance.
If edit distance == 1, move votes for this term to the guessed song in our songs aggregation, then continue to the next term.
Add distance to a dictionary of value/distances
Using our value/distances dictionary, find the closest match that satisfies our 2/5 * len(term) rule. If it matches, move the votes for this term to the guessed song in our songs aggregation, then continue to the next term.
Match Artists using the same method.
Some of the results of string matching, providing some reasonably accurate re-matching.
[A] weekncl, the -> weeknd, the with distance 2
[A] mm m. -> ms mr with distance 2
[S] km; kunta -> king kunta with distance 3
[A] macklelllore ex ryan lewis -> macklemore & ryan lewis with distance 5
[A] eulsch duke) -> deutsch duke with distance 3
[A] bloc pany -> bloc party with distance 2
[S] nommg's forevev -> nothing's forever with distance 5
[S] t he hllns -> the hills with distance 3
[S] emocons -> emoticons with distance 2
[S] better off without you -> better with you with distance 7
[S] - the less | know the better -> the less i know the better with distance 3
[S] vancejoy fire and the fiood -> fire and the flood with distance 10
[S] too much me togglhu -> too much time together with distance 6
[A] of mons-us and m. -> of monsters and men with distance 5
[S] gmek tragedy -> greek tragedy with distance 2
[S] marks to prove 1t -> marks to prove it with distance 1
[A] rlighx facilities -> flight facilities with distance 2
[A] gang 01 youth: -> gang of youths with distance 3
[A] fka lwlgs -> fka twigs with distance 2
[S] hoine bling -> hotline bling with distance 2
After performing this additional processing, I ended up with 18509 uncategorised terms, down from 27294 uncategorised terms.
That means we were able to successfully categorize 8785 terms via the Levenshtein distance algorithm!
== Results ==
1 Hoops 1011
2 King Kunta 1008
3 Lean On 793
4 The Buzz 667
5 Let It Happen 637
6 Like Soda 617
7 The Less I Know The Better 602
8 Magnets 521
9 Never Be 520
10 The Trouble With Us 501
== 1143 unique terms ==
== Top Unknown Terms ==
1 Your Hottest 100 Votes: 2279
2 } 320
3 Hottest Io 248
4 V 231
5 Throne 222
6 Triple J? 209
7 Thanks For Voting! 174
8 Tapz) 170
9 Suddenly 155
10 Once 140
== 18509 unique terms ==
Quite an improvement, however still not great. Some of the terms there weren’t able to be categorised which caught my attention included:
9 Suddenly 155
16 Big Jet Plane 123
17 Heart Attack 120
18 True Friends 114
23 Rumour Mill 107
35 The Less | Know The 76
63 & Chet Faker The Trouble With Us 46
Paying special attention to The Less | Know The, if I were to add it’s sum to our results, it would have placed 4th, however, the results we already have look reasonably accurate.
Final Results
== Results ==
1 Hoops 1011
2 King Kunta 1008
3 Lean On 793
4 The Buzz 667
5 Let It Happen 637
6 Like Soda 617
7 The Less I Know The Better 602
8 Magnets 521
9 Never Be 520
10 The Trouble With Us 501
11 Do You Remember 480
12 Ocean Drive 463
13 Can'T Feel My Face 457
14 You Were Right 444
15 Middle 423
16 Magnolia 381
17 Young 380
18 The Hills 369
19 Hotline Bling 356
20 Keeping Score 321
21 Embracing Me 319
22 Mountain At My Gates 318
23 Loud Places 300
24 Run 298
25 I Know There'S Gonna Be 287
26 Some Minds 287
27 Say My Name 283
28 Fire And The Flood 280
29 Visions 275
30 Greek Tragedy 274
31 Long Loud Hours 272
32 Shine On 254
33 Asleep In The Machine 249
34 Leave A Trace 242
35 Like An Animal 235
36 Something About You 224
37 Dynamite 224
38 All My Friends 218
39 Deception Bay 217
40 Downtown 210
41 Ghost 200
42 Son 196
43 Hold Me Down 196
44 No One 196
45 Kamikaze 196
46 Puppet Theatre 192
47 Vice Grip 191
48 Forces 185
49 Better 185
50 Counting Sheep 184
== 1143 unique terms ==
Some Notes
Run appeared so high on the leaderboard because both Seth Sentry and Alison Wonderland released similar tracks titled RUN/Run. Since I lowercased all comparisons and removed special characters, these votes merged.
Improving the Analysis
After reviewing the method used for analysis, I have identified a few places for improvement that could possibly improve the results.
Improved Levenshtein Algorithm. The Levenshtein algorithm is great for calculating edit distance, however I could not weigh edits of similar characters such as t’s, i’s and l’s less, thus improving matching due to OCR inaccuracies. I expect that string matching could have been significantly improved if this was explored.
Songs that had long titles, such as The Less I Know The Better generally were split across multiple lines. This caused their aggregation to not sum correctly. It would be good if I could determine if a song was split across two lines.
Songs that were in the format of artist song and were spelt incorrectly were most likely not picked up by string matching, as we only matched against songs and artists individually. In order to improve matching for this, an additional list for joined songs/artists could have been used and compared against for remaining terms.
Some Cool Stats
Triple J Tallied 2094350 Votes (209435 Entries)
I collected a sample size of ~2.5% of all entries
I collected 7191 images collected initially
I categorised 5529 images as votes
~4900 images contained the words “vote/votes/voting”
