All posts by Ben S

Drawing More Waves

I stumbled upon another good example of drawing audio waves, similar to the example we did in class. It’s by an artist named David Letellier and the forms he creates are absolutely spectacular. I’m not entirely sure if the all or only parts of the audio track in the background is creating the forms, but the variety of forms is still pretty amazing.

I would also recommend checking out some of his other work, most of which has to do with sound and space (architecture):

 

Assignment 5: Haptic Explorations

How fine is the resolution of the skin? What areas of the body are most sensitive and effective to learning through touch-based communication? Are vibration motors sufficient means of delivering these “messages”?

I decided to explore these questions by using Max to map the position along a circular potentiometer to the position of vibration along a round array of vibration motors. By running your finger along the potentiometer, you effectively remap your “touch” to an area of the array which, when placed on the skin, is meant to transmit the “sense” of touch.

My inspiration at least partially came from an amazing project by Scott Novich called Sensory-Substitution, where he is attempting to afford deaf individuals the sense of hearing through a different modality: touch.

IMG_5492 IMG_5493

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Assignment 5: Soundscaping

Soundscaping is the considered placement of sounds to create an acoustic environment. For my performance tool, I choose to explore the ways we interact with sound in space and in turn how we create acoustically comfortable or provocative spaces around the human body.

The idea is very simple: Create a new “sound particle” in every location you clap. From this point on, the closer your hand is to this particle, the louder it becomes. You can have many particles and you can place them wherever you wish.

This implications of this extremely simple idea are quite profound. It begins to address the relationships between chords and harmonies in a way that typical instruments cannot, since sounds are literally placed and sculpted in three-dimensional space.

To interface the Kinect v1 with my mac, I used a free program called Synapse.  If you’d like to download my code via github, you can find it here.

Main program:

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</code></pre>

And the synth program:

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Fontana Mix for Kids

There’s an ingenious game for kids called Compose Yourself that allows anyone to create beautiful musical compositions with their very own orchestra. Of course, everything’s been pre-recorded, but there’s a (close to) infinite amount of musical compositions you could make.

DSC_1499

It works in very much the same way as (and is likely modeled after) John Cage’s Fontana Mix. However, instead of layering multiple transparencies on top of each other and interpreting the result, each transparency card is a discrete compositional element—one that can be played in four orientations (front or back flip and top or bottom rotation). Here’s a video explaining it in more detail:

Fine. Maybe it’s not the most original compositional tool, but for kids who’s never touched a piece of music before? It’s magical! It gets you thinking about the components of music, and how these rhythmical and melodic components different depending on how they are spatially and temporally oriented. It truly is a masterful teaching tool, and a fun one at that. Check out their website here.

Assignment 4: Image Frequencies Inverse-FFT’d to Sound

The Fourier Analysis of images is similar to the FFT for audio, except the analysis attempts to represent all spatial frequencies as a sum of spatial cosine frequencies. The article here gives a particularly good overview of spatial FFT.

I was curious to know what an image sounds like by translating not in the color or luminance domain but in the realm of spatial frequencies. I began by first taking an incoming video feed, which I downsized to 64 by 64 pixels. Performing the FFT on an image and rendering the amplitude of the frequencies along a gradient from black to white creates a particularly striking representation of the frequencies present. Here, vertical lines are produced by horizontal lines in the video, and vice versa for horizontal lines. Then, I averaged the four quadrants of the two dimensional analysis to extract a simpler form with dimensions 32 by 32. Reading this matrix across and down results in a string of 1024 numbers, which I chose to use as the input for 1024 bins in an audio ifft~, pairing this data with a mirrored transformation of the phases.

videoFFT documentation

Every 5 seconds of this audio corresponds to a different sample video found in the max library:
0-5 sec: Basketball Clip
5-10 sec: Countdown Clip
10-15 sec: Bulldozer Clip
15 – 20 sec: OH clip
20-25 sec: Church and Birds Clip (“Track 1”)
25 – 30 sec: Wheel Clip

The result is particularly interesting. The audio responds to varying degrees of spatial frequencies, as there is no sound with black input and there are more sounds created with greater spatial variance. Depending on the activity in the clip, the audio will ramp up or down (see 5-10 sec interval in attached audio for a good example). I’m hoping to continue exploring how to analyze images with sound, perhaps in this scenario by changing the way the image is sampled to produce a 1024 sample signal.


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Bad sampling? Creative aliasing!

The promo for the 2015 Youtube Music Awards, made by a group of artists and designers led by Tarik Abdel-Gawad, features video all shot in-camera of near-hologram laser light forms using nothing but the aliasing from the lasers and the camera shutter. What’s so incredible about the work is that Tarik and team devised a way to practically utilize the system’s inherent qualities that most others would find non-ideal and detrimental to the image-making process. To be clear, the forms do not appear to the actors but can be seen and captured in video.

Assignment 3: Convolutions – Ben Snell

Convolution has the incredible power of being able to transport someone or something to a completely different place, and often, a completely different time. For my “normal” recordings, I sought to push this as far as I could by attempting to integrate myself into video in which I was never present for. This is the closest I will probably ever come to teleportation.

Below are two samples—youtube videos in which I embedded my presence using the (sometimes unintentional) pop of a balloon. The videos are very intimate, occurring in the setting of a home with young children. The result feels very invasive, not of privacy, but of cherished memories whose most accurate representation resides not within the mind but in the form of these virtual memories, or videos.

Please use the password convolve to view them. Here are the original links for Tween Balloon Pop and Birthday Balloon Pop

For my “abnormal” recordings, I experimented with two ideas: the first being presenting a score in the context for which it written. Below, I used a convolution filter with a recording of rain to “place” Vivaldi’s first movement of Winter in The Four Seasons. (find all recordings here).

In the second abnormal recording, I experimented with layering multiple recordings on top of each other by sending a sample through one filter, the result of this through another, and so on and so forth, in a sort of feedback loop. (find all recordings here). The result represents a child laughing in a frozen squash wrapped in saran wrap in a vault in a storm drain near a church.

Assignment 2 – Primitive Neural Networks for Generative Music

I seek to create a program capable of producing harmonious combinations of sounds by incorporating active learning to understand a user’s preferences for simple melodies and use those patterns to inform the creation of music. Through this feedback loop, I hope to create a man – machine interface capable of producing “music” from nothing more than a chaordic system and a responsive subject. In other words, how can a computer release the inner essence of what sounds good to me or what sounds good to you?

This assignment represents a step in this direction. I chose to simplify the problem by focusing on the relationships between consecutive notes, of which there are 5 per phrase. I also chose to ignore note duration and velocity as a factor influencing the relationship between two consecutive pitches. While this obviously has a great effect on the perceived musical qualities, I think it best to begin by studying one variable (pitch) before moving onto a second or third. Furthermore, the sample from which notes is drawn is severely limited. It represents not the population of all possible notes but a select few to study in seclusion before expanding the sample to include more options and allow for greater variability.

Unfortunately, there are still some bugs in this tangled mess, as I was unable to get complete and accurate feedback, but I hope future iterations of it, with more subtleties, will allow for greater expression. Still, the continual input of user-generated responses creates a simple neural network in the form of a matrix of connection strengths (or probabilities) that represent the harmoniousness between connected, or consecutive, pitches.

Screen Shot 2015-09-23 at 7.26.32 AM

And here is a sample of the audio generated by the patch:


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Found Systems: Mayfair

Instagram filters are great. But does MORE FILTER always equal BETTER PHOTO?

Using the Instagram App for iPhone, I uploaded a photo to Instagram with the Mayfair filter. Then, I rinsed and repeated. Three-hundred thirty three times.

Instagram-Photos-Mayfair-GIF-lowresDownsized GIF of 1st third of process

Vintage lo-fi filters are often used to enhance the aesthetics of a photograph; however, in doing so they lose some of the information inherent to the original image. This made me curious, since the disintegration of signals is usually something to be avoided (i.e. tv static, calls going “underwater”, etc.). To what extent does the disintegration of a signal add value to that signal? I sought to explore this question and others using images on Instagram.

  • At what point does the next iteration stop feeling “vintage” and start feeling “glitchy”?
  • At what point does is the image no longer a photograph or a representation of the moment “captured”?
  • Do filters get us closer to capturing our internal realities?  If so, is signal disintegration a bad thing?

Image Sequence (15fps) of all Instagram Images

As it turns out, Instagram filters not only make images more “vintage” but drastically change image qualities like global color, patterns, and position over time. Watching the full video above of 333 iterations shows the image in transition, with the most prominent effects being color thresholding at the beginning, leading to an upward left “bleeding.”

The signal has been effectively destroyed, void of all intelligible components except the artifacts of what once was.