My inspiration of this project became from Neil Harbssion, who could not see colors. His world is black and white, but now he is an artist. Harbssion describes him as a cyborg. Dangling over his forehead is an antenna that curves up and over from the back is his skull. The device, which he calls “eyeborg”, helps him connect him and the color by detect the light frequencies of color hues and translate it into sound frequencies.

Harbission’s artwork burs the boundaries between sight and sound. He can listen to color, and also see sounds.

Here is the how Harbssion sees Biber’s Baby and Beethoven’s Fur Elise.

These made me wonder if normal people without an eyeborg could also participate into this fun with the help of software technology.

We have many phone games that are able to transform the tones, beats, and melody into colors, such as Cosmic DJ, Synesthetic, InSong, which means there is a technology to establish my assumption.

I found Harbssion’s match sheet between sounds and colors from his cyborg company and hope if there is someway I can match the color and sound frequency myself.

In the beginning when I asked around my friend, nobody seemed have this kind of experience. They said the assumption sounds doable, but they do not how to do it. Finally I reached my friend’s friend, who is an artist with CS skills, she said:

“His (Neil Harbssion’s) mapping of tone to color is actually really complex, and he uses frequency to detect the nuances of colors, which is a very laborious and intense process. If you want to do it from a scientific approach of parsing frequency to color, it is not an easy process and you won’t be able to finish in time.”

However, she said, I can also fake it by assigning the color with very basic tone.

I tried several music software and finally came up with one called Mixed in Key 6.

This is a DJ software that simplifies a DJ technique called harmonic mixing. It analyzes MP3 files and determines the musical key of every file.

The basic key of each song from Mixed in Key is showing as a specific number with letter, such as 12B as E major.

My datasets includes, Billboard Hot 100 from 2014 as representation of Western pop music, Melon top 100 from 2014 as representation of Korean pop music, Li Yundi’s Tokyo Concert as representation of classical music, and the sound tracks from Jersey Boys as representation of the Broadway music. By using Mixed in Key 6, I can generate the basic key into letter and match back to basic key in musical notes.

After getting all the basic keys of my music, I use the tableau Public to generate the date into columns and pies.

If we distribute the number of songs with specific notes, we can see that the 4 groups of music have their own “favorable” notes.

(from left to right: billboard, korean, classical, and jersey boy )

For billboard, it will be C minor, D major, D flat Major, D flat minor, and G major.

For Korean music, it will be A minor.

For classical music, it will be C minor and some B-flat minor, D flat, and F sharp major.

For Broadway music, it will be A major, B-flat major, E major, and F minor.

We can have a simple conclusion that note C and A seems apparent more frequent than others.

In the beginning, I used specific color to draw the pie; the color distribution looks very complex and uneven.

Then I started thinking that the match of Harbssion’s mapping has many color in similar tones, and it may be because the color has similar hue tone also reflect to the keys that sound similar for people to hear.

I modified the pies and use the same shade of the color to indicate keys also share similar hues from the map. The distributions look much better now. (from left to right: billboard, korean, classical, and jersey boy )

Looking at the second color pie, it seems all four groups have more even distribution in music tones. And because some color is more brighter, it looks more obvious, such as the orange, representing F#. There is no trend showing that people will be more in favor of one color tone. However, by looking at the pie, we can see that classical music use more F than other genres, and broadway music uses more F#. Theses four genres share similar distribution of note A (green) and D (magenta). And classical music may have more common with broadway music.

There are some limitations of this data project:

1. Mixed in Key 6 is not a software with high accuracy to determine the basic key of a music
2. It may be to broad or rough to only look at the basic key of a music and may mislead the interpretation of a song.
3. Whether the dataset is representative enough

To determine whether is a real trend of music that people like to hear, further research of this project is, indeed, needed.

And it will be on my project proposal.

Purposefully running against technology and methods of analysis in order to assert the presence of a “unique”, human validating form.

I am interested in “cultural analytics”. I also really like this music. The effects if produces as well as the mindset of the creators: It is possible to dismiss as misogynistic and hateful. True claims. But it matters to people (specifically men from 15-25)*. Unique following emerges defined through the Internet and networks of connectivity. Sometimes this manifests into real-life encounters (such as saving money to fly to California). Many times the “mainstream” websites cannot carry the videos and audio of producers of music such as this. They then take alternative routes and it would be my supposition that their creation runs through and by the eternal promise of running in opposition to the mainstream methods of distribution. Although Youtube and other content “Providers” envision themselves as at the cutting edge and key to liberality of a democracy, there is new things popping up; a contingent of dissension which demands new forms of distribution through the unacceptability of their current content. Anti-censorship. It becomes a game of searching for clues: you prowl through the Internet looking for signs, hieroglyphics really, of the wormholes around which this content congregates.

One of those areas could be the comments sections of videos. This is the video: https://www.youtube.com/watch?v=ijSoJQmLcgI. I used Voyant for analysis and the data was all the comments in the comments section of the video.

It has been said that comment sections of certain websites may be more illuminating than the content itself. The networked opinion of the hive-mind; the idea being not to “dominate” and/or provide the final say and exist as a standalone object of critical opinion, but to stir and funnel discourse through associated channels defined in the video. What I am analyzing is not an article: It is a music video. The comments clustered around the video are not surprising. They seem to mirror and/or repeat what is said within the content of the lyrics itself.

Any conclusions I reached seemed redundant and patterned on my perceptions of the lyrics as displayed from the progenitors of the video itself. Perhaps a larger dataset consisting of all comments and all related videos (as defined subjectively by me) would provide a more useful and illuminating dataset. The one thing which did stand out to me was the preponderance of the word “parents” in the comments section. The comments section can be seen as a form of metadata about whatever content is itself displayed. They all express concern for who the parents of Mike Dece are and where they came from. Thus the word “Brazil” pops up. Someone has gained access to this information through unique insider information and then spreads through channels such as this. This alters your experience of the video. You learn he is 16 and his parents are from Brazil. It is known he currently lives and works in Miami. This all becomes opinion enshrined in the hive-mind and a knowledge of which is presupposed from the “right” to comment on the video. Somehow the experience of the video is changed by knowing his parents live in Brazil and the artist is 16. Humanoid elements are added to what can on first glance seem ridiculous and/or unworthy of analysis. You become closer to the artist and others who share this exclusive knowledge with you. You feel part of a “community”.

Conclusion: the metadata contained within comments sections can alter and change a user’s experience of the content itself – bringing them closer inside the “networked circle” they seek to/have defined themselves as part of by taking the time and effort to provide a commentary.

* Subjective figure

Hi Y’all,

I hope everyone’s project proposals and papers are wrapping up nicely. I’m sure some of you have been following this twitter trend, but I thought this would be interesting for those who expressed interest in twitter activism: http://www.bbc.com/news/blogs-trending-30479306

I hope everyone has a great break and happy holidays!

Basically, DH field was foreign to me because I don’t have computer science background. Throughout the class discussion, reading materials, and workshops, I ended up having a better understanding of DH. I want to share additional information from my paper.

Overall, DH has been globally stretched.
The basic methodologies of DH have been applied in East Asian Digital Humanities Institutions regarding mostly cultural and historical contents. e.g.) Taiwan, Japan, Korea

1. Taiwan.
Taiwanese DH scholars organized historical and political events through data mining and the DH scholars focused on organizing the historical materials relating to the people of China and Taiwan in the Qing Dynasty.
In addition, Taiwan founded the Digital Libraries/ Museums Program (DLMP) in 1998. This organization’s goal was to digitalize the cultural heritage and created a new program called National Digital Archives Program (NDAP).

2. Japan.

There is a proper example of how social network service worked efficiently during the natural disaster in Japan. During the 3.11 Tohoku earthquake disaster in 2011, Japanese SNS users at that time gathered the precise locational information on Twitter and were able to alarm the relief of tsunami victims.

3. Korea

Korea is benefited from data visualization in history department. In particular, data visualization help organizing family trees during Joseon Dynasty.

These figures illustrates the transformation form paper work to digitized form.

Korea, in fact, is known as a strong IT country, but the researches on humanity fields are largely marginalized. Overall, Korean academic puts more emphasis on Science and Technology field. DH in Korea should be balanced between computation and humanity to maximize useful information.

Great job on the presentations, everyone! Really interesting stuff– and so diverse in topics and approaches.

I wanted to share this article that I just read in The Wall Street Journal:  http://www.wsj.com/articles/when-the-art-is-watching-you-1418338759

The article discusses the use of visitor tracking information in the museum to help make curatorial decisions. We’ve been seeing this a lot lately, using technology to track what is popular to reproduce it. It makes sense in terms of profit, but it really doesn’t leave much room for creativity and the artistic spirit, which tends to be counterculture before becoming mainstream.

Cindy and I, who are not tech savvy at all, have been working with what feels like a million data sets.

Our process is outlined below:

– What we were looking for?

– How we were going to explain it?

-How it related to our research?

How were we going to visualize it?

The first question was the easiest. We knew what we wanted to share, we did not know how to share it visually. Vilem Flusser says, “Changing image to text is magical”, but I tend to think text to image can also be very magical.

Of course Step 1: attending a DH Fellows Presentation ( if you have no  clue about the software, this is the way to go)

Step 2: Working with the software to understand how it will tell your story

Step 3: going to the Data sets. the one for us was https://nycopendata.socrata.com/

Ste 4: Finding the right program for our story. For us it ended up being CartoDB, I mentioned neatline in another post,but as you will see in  the next step that did not work.

Step 5: Exporting the data in a way that it would map without a lot of commands. We tried exporting a number of data sets that did not work for us. It would import as a polygon or with null and  who knew how to georeference that? Then it would not map. Only the shape files seemed to map easily.

Step 6 : Find more data that would tell the story

Step 7: Once we found the data, finally mapping the data

Step 8: Making the data make sense for the viewer

Today in class we will show our final  project of geomapping

How ICT impacts student learning? Does income and location impact whether students can achieve?

Our project will be displayed in cartodb.

Stay Tuned

Taking a scan of where we as a group began and where we stand today, I am enamored with the skills that are developing around me. From Mary Catherine’s awe-inspiring visualization of Icelandic Sagas to Martha Joy’s splintering proposal ideas, this group has evolved into a community of valuable thinkers, but more importantly valuable workers.

While I work through my own project proposal, I find more and more areas where I will need help executing each stage of development. I should be discouraged that the staff and the skillsets that are required for the success of my project is only expanding as I think through it more and more. Instead, I am excited to consider not only closer friends in the course as assets, but also people who I have yet to really chat with one on one as potential teammates.

When NYPL went around the room asking us about what we were working on and what we were going to propose, I have to admit, I went with a lame cop out answer. I hadn’t had the heart to blurt out what I really was thinking of proposing. Instead I went with some idea about a content series or something of that sort.

I have gone with a much more exciting proposition. It involves not only the study of an unexplored corpus, but also the development of a new platform for studying a particular type of media. I will explain more in my presentation tomorrow, but I thought this would be a good time and outlet to reflect on our group, our growth, and our future together.

Thank you all for being such a fantastic, collaborative, and thought-provoking amalgamation of personalities, minds, backgrounds, and insight.