Wrong numbers, wrong melodies

In this 3 minutes and 35 seconds duration  sound piece, the idea was to continue with the idea of using data to create sounds that somehow evoked the data that triggered the sound. I started with the idea of traffic sounds or “noises” as the material to be used. Then the idea was to find data that would be related to traffic. I found an interesting dataset among other interesting thing sources. It’s a dataset the city keeps about the traffic collisions.

The dataset contains details like the time of the events, the location (latitude and longitude), the borough (five boroughs), the cause of the accident, the type of vehicles involved, and the most important, the amount of injured or dead people involved in the accident. I started to think about what to do with the data.

At the beginning, I pretended to trigger sounds of cars, as I previously did with the previous experiment I called Hell-ectronic music. I thought about associating them to the boroughs where the accidents happened. I tried, but it sounded like hell and not too much like music. Another issue was not really having clear criteria about which sound to match each borough. Then I realized the most important and interesting data to use, was the number of injured people or killed people. It is the reason to be for the data. So it can be used to measure how dangerous the streets are on time. And off course to encourage changes in this regard, to make those numbers to be as closest to cero as possible.

Another aspect I wanted to play with was the equation between noise and music, and what we referred in class, as for how listenable it would be. So I started thinking about which sound should I use to relate to the event of injured people. I came back to the idea of traffic sounds, and thought about some kind of ambulance or siren sound.

Then, as I listened to a song I realized that the main melody would be good for this purpose. The song is called “wrong”, by Depeche Mode. The main melody sounds like a siren, and the song’s video is very close to the idea of accidents and traffic. It has a feeling of a nightmare. Very similar to a recurrent dream I used to have when I was a teenager. So I started to look at the song and I found different versions. I finally found the melody without any other sounds. I used this version that lasts 43 seconds in total.

I set it then to play whenever in the accidents a person or more where injured.

The result was very interesting to me. It sounds like some sort of ambulance parade, and also reminded me of the sound effect used in choirs, when people sing the same lyrics and notes in different time, so they overlap, creating some sort of atmospheric sound. But, going back to the concept and idea of the database, it made sense. It evoked the increasing number of injured people through time, in a very emotional way. Also, you can sense when the number decreases at the end of the database sample when there are less of overlapped sounds, and then none at the end of the sample.

A point that in the end turned to be interesting when listening to the result, was the idea that my friend Ayal told me to consider: the contradiction of a beautiful or pleasing sound that was revealing a tragic or unpleasant or ugly fact. I found the final sound piece to be both beautiful and pleasing, but I also feel a lot of dramatic tension when I listen to it.

That is the point a got so far. What I want to do is to learn the different sound methods that will allow me to play with the sound, such as the reverb and delay effects, of others that might make it more rich and interesting. Also to use them in a way that makes sense, in relation to the database.

Other things I would like to explore are the API’s data like real-time alerts, the current speed of the traffic, among others.

A difficulty I faced was dealing with the huge database. The file is to heavy to be easily handled, so I had to work with a piece of it, that resulted in a short piece.

https://www.youtube.com/watch?v=5J3FGxHr9iE

“For the song in good sound quality: (video doesn’t work well after some time)“The one Depeche Mode video that Youtube constantly removes , or something happens to the video. They dont want to share. Wrong .“

Close to the original version of the video:

https://www.youtube.com/watch?v=fos_5QoFaZM

“Caras vemos, corazones no sabemos”

Physiognomy’s New Clothes

by Blaise Agüera y Arcas, Margaret Mitchell and Alexander Todorov

 

https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59fdd6a 

“Caras vemos, corazones no sabemos” (Faces we see, hearths we don’t know) Popular proverb.

“The idea that there is a perfect correspondence between a person and their image is a psychological illusion fueled by our experience with familiar faces. We instantly recognize images of familiar people, and this recognition evokes our memories and feelings about them. But there is no equivalent process when we look at images of strangers. Each image generates a different and arbitrary impression.”

The misperception of the gestures of people can lead to discrimination and mistakes made by us, common pedestrian, by the police force, by judges, by hiring companies, visa expedition process, people responsible in universities in the admissions process and many other fields.

There are many examples of this kind of mistakes. A few weeks ago I was labeled as a criminal suspect while walking home. I was walking home after eleven p.m. I was walking in the same direction as a young lady of about 25-35 years old, who I never saw her face. She was walking in the same direction as I was, and also at a similar pace. She was about 15 meters ahead of me. When she noticed my presence she started looking over her shoulder very frequently. So it was clear she assumed I represented a danger to her. I felt very bad when this happened. I understand how women feel unsafe, and off course I didn’t take this personally. I have also done the same many times in my country, where you can easily get robbed in the streets. The conclusion is that this misunderstanding is very common, and if made by the authorities or people with guns, the consequences are very bad.

The problem with making assumptions about people based on their appearance is a dangerous mistake. In different cases injustices have been perpetrated because of this. Judges and jury often make decisions that change individual lives, based on how these people look, and not only on what their story is, their context they came from or their psychological profile. Criminals responsible for the same offenses often get different sentences because of how they look. Black people are common victims of this kind of injustice. It’s very common that they get more time in jail when they are sentenced for the same crimes as white people. Also when it comes for choosing between the death penalty or the life imprisonment, decisions are affected not only on the profile and expedient of the inmates but on how their faces reveal a more “dangerous nature” or “evil” one.

If we are not conscious of how we are judging people by their looks, expressions, color of their skin, the way they dress, if they have tattoos or not, their size and weight, etc., we will be training machines to perpetuate this vicious way of judging, leading to more injustice and inequality. This is very present in our human nature. The popular sentence “Tell me who you hang out with, and I’ll tell you who you are” reveals exactly that tendency.

“Our existing implicit biases will be legitimized, normalized, and amplified.”

That could happen if we are careless when we train A.I to make judgments like we do and allow computers to perpetuate our own biases and use them as we enforce them as valid and scientific fact based.

The following case is another example that reveals the dangers of using these systems of machine learning and so-called objectivity.

Predictive policing” (listed as one of TIME Magazine’s 50 best inventions of 2011) is an early example of such a feedback loop. The idea is to use machine learning to allocate police resources to likely crime spots. Believing in machine learning’s objectivity, several US states implemented this policing approach. However, many noticed that the system was learning from previous data. If police were patrolling black neighborhoods more than white neighborhoods, this would lead to more arrests of black people; the system then learns that arrests are more likely in black neighborhoods, leading to reinforcement of the original human bias. It does not result in optimal policing with respect to actual incidence of crime.”

The idea of physiognomy has survived the test of time and is a treat in our present time. The belief of the facial forms and expressions of people having a correlation with their moral qualities is a misconception that often leads to discrimination and injustice. There is a study that is taking this system of thought, and using machine learning to reinforce it, claiming accuracy and objectivity. It’s very dangerous because the law and authorities could agree on its validity and that way implements it as a tool to make decisions that will affect many peoples life.

“Wu and Zhang are able to use a variety of techniques to explore this in detail. This is especially tractable for the simpler machine learning approaches that involve measuring relationships between standard facial landmarks. They summarize,

“[…] the angle θ from nose tip to two mouth corners is on average 19.6% smaller for criminals than for non-criminals and has a larger variance. Also, the upper lip curvature ρ is on average 23.4% larger for criminals than for noncriminals. On the other hand, the distance d between two eye inner corners for criminals is slightly narrower (5.6%) than for non-criminals.” [7]

We may be able to get an intuitive sense of what this looks like by comparing the top row of “criminal” examples with the bottom row of “non-criminal” examples, shown in the paper’s Figure 1:

 

Figure 3. Wu and Zhang’s “criminal” images (top) and “non-criminal” images (bottom). In the top images, the people are frowning. In the bottom, they are not. These types of superficial differences can be picked up by a deep learning system.

 

Figure 4. Stereotypically “nice” (left) and “mean” (right) faces, according to both children and adults.

Another interesting case was the misconception of women being bad at math’s, during the nineteen century and before. Philippa Fawcett managed to get the top score at an advanced math exam, “Cambridge Mathematical Tripos exam”, in 1890 in England. This was perceived, assumed as an error, an exception to the rule. People’s beliefs in Victorian England could not believe and accept that the elite of the British gentleman where beaten by a very intelligent woman. If we think about this case in terms of numbers, we can get their point. As there were practically no women attending universities and academic contexts, there was no data that could reveal the skills or lack of skills of women in math’s, as a group. There could be statistics without numbers. So when this result was obtained, it was seen as an anomaly.

This could happen if we trained a computer without the amount of information we need it to have, in order to get results that come close to the average, what we sometimes call the truth. If these men had allowed more women to learn math’s and to take exams, they would have thought in a very different way as they did. If we think of them as the computer making decisions, and having to choose one person for a job that required high mathematical skills among the people doing the exam, the computer would have chosen any other than the most suited one for the job.

Hell-ectronic music

ride#3

Hell-ectronic music is the result of a combination of data produced from the interaction of a cyclist and a bicycle on a bike simulator and three sound samples produced by cars.

The inspiration sources are the train music by Pierre Scaffer and the Ufo Sightings piece by  Hanna Davis.  I wanted to bring these ideas along to the idea of noise vs music that I started to think about when I did the traffic jam, an “Instrument of torture” to produce and listen to car sounds. You can play different sounds of cars at the same time, producing a chaotic and sometimes relaxing sound piece.

I produced the data making different sessions of 5 minutes each on the bike simulator, trying to create different rhythms int he variable I used: speed, pedaling cadence and heart beats per minute.

This variable where taken into the P5 code. They affect the looped samples from car sounds, so they create rhythms and different pitches of them as they also affect the duration of the looped samples.  I used the P5 Library  play sound.rate for this purpose.

The result was very noisy and interesting in some ways. But I think I need to work it a lot more to make it more inclined into the listenable spectrum.

I’m also thinking about possible ways of taking data from cars.

I want to implement other functions from the library as Reverb to make the sound more interesting and dynamic.

 

ride#3
Ride#3 (Riding hard mode)

Aural Fractals

Aural fractals 

Artist: Landscape Windscreen

I stumbled upon this piece on Youtube, with no idea about the author or how it was made. It came to me by searching for generative music. I’m glad it did.

The piece gave me a mix of positive feelings like peace and a relaxing sensation. The sounds it was made of where al digital, and not so many notes. I Heard a lot of Sharp and short notes, that had also a reverberation effect. The rhythm was very slow. Around those Sharp shrilled notes, I Heard some kind of arpeggios and also a sound in the lower spectrum of sound, that was creating some kind of surrounding space for all the rest to exist into that space. It really was effective in producing a sense of space. My guess would be that the interaction between the shrill notes, the reverberation effects, the low sounds and the arpeggios, along with a very slow rhythm (or lack of a beat) gave way to that sense of being inside some kind of watery cave or magical cathedral.

The duration of the piece was five minutes approximately. It was more than listenable. I fully enjoyed the five minutes, and at the end, I wished it was longer, but as my grandfather used to say: “If short and good, it’s twice as good”.

By the title a suppose there are some mathematical procedures for composing the piece, but as a non-mathematician as a non-musician connoisseur, I can’t try to guess how would that work. Hopefully, I can someday create something as pleasant as this piece!