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Project 2: Sound Artifact

Project 2: Sound Object

 

for my final project I wanted to create an object that made different noises depending on position and handling; but I wanted to take it away from the realm of functional instruments.

To do this, I used captivate touch sensing by wiring up an arduino, and sent data through serial to a maxpatch that manipulated the input into sound.

video of use

 

 

 

Audio Player

other sound

 

 

 

Construction:

 

I constructed the form out of polymer clay; which i sculpted to fit my hand; and a magnet to allow it to fit together while being able to be opened. I wanted to stay away from rapid prototyping techniques such as 3d printing and lasercutting because i felt that they take away from the organic result i was trying to achieve, and would place it into a context i wasn’t interested in.

 

I created the circuit by soldering resistors and wires onto pins that fit easily into the breadboard on the arduino. The arduino is removable but the wires and copper tape are built into the shape

 

maxpatch:

  

download

Arduino Code:

#include <CapacitiveSensor.h>

CapacitiveSensor cs_4_2 = CapacitiveSensor(4,2);
CapacitiveSensor cs_4_3 = CapacitiveSensor(4,3);
CapacitiveSensor cs_4_5 = CapacitiveSensor(4,5);
CapacitiveSensor cs_4_6 = CapacitiveSensor(4,6);
CapacitiveSensor cs_4_7 = CapacitiveSensor(4,7);
CapacitiveSensor cs_4_8 = CapacitiveSensor(4,8);
CapacitiveSensor cs_4_9 = CapacitiveSensor(4,9);
CapacitiveSensor cs_4_10 = CapacitiveSensor(4,10);
CapacitiveSensor cs_4_11 = CapacitiveSensor(4,11);
CapacitiveSensor cs_4_12 = CapacitiveSensor(4,12);

void setup(){

cs_4_2.set_CS_AutocaL_Millis(0xFFFFFFFF); // turn off autocalibrate on channel 1 – just as an example Serial.begin(9600);
cs_4_3.set_CS_AutocaL_Millis(0xFFFFFFFF);
cs_4_5.set_CS_AutocaL_Millis(0xFFFFFFFF);
cs_4_6.set_CS_AutocaL_Millis(0xFFFFFFFF);
cs_4_7.set_CS_AutocaL_Millis(0xFFFFFFFF);
cs_4_8.set_CS_AutocaL_Millis(0xFFFFFFFF);
cs_4_9.set_CS_AutocaL_Millis(0xFFFFFFFF);
cs_4_10.set_CS_AutocaL_Millis(0xFFFFFFFF);
cs_4_11.set_CS_AutocaL_Millis(0xFFFFFFFF);
cs_4_12.set_CS_AutocaL_Millis(0xFFFFFFFF);

Serial.begin(9600);
}

void loop(){

long start = millis();

long total2 = cs_4_2.capacitiveSensor(1);
long total3 = cs_4_3.capacitiveSensor(1);
long total5 = cs_4_5.capacitiveSensor(1);
long total6 = cs_4_6.capacitiveSensor(1);
long total7 = cs_4_7.capacitiveSensor(1);
long total8 = cs_4_8.capacitiveSensor(1);
long total9 = cs_4_9.capacitiveSensor(1);
long total10 = cs_4_10.capacitiveSensor(1);
long total11 = cs_4_11.capacitiveSensor(1);
long total12 = cs_4_12.capacitiveSensor(1);

//long total9 = cs_8_9.capacitiveSensor(1);

//Serial.print(millis() – start); // check on performance in milliseconds

Serial.write(99);
Serial.write(98);
printLimit(total2, 100, 2);
printLimit(total3, 100, 3);
printLimit(total5, 100, 5);
printLimit(total6, 100, 6);
printLimit(total7, 100, 7);
printLimit(total8, 100, 8);
printLimit(total9, 100, 9);
printLimit(total10, 100, 10);
printLimit(total11, 100, 11);
printLimit(total12, 100, 12);

//delay(5); // arbitrary delay to limit data to serial port

}

void printLimit(long val, int thresh,int yes){
if (val > thresh){
Serial.write(val);
} else {
Serial.write(0);
}
}

download

 

 

[will refine documentation on friday]

Project 2 – Help Wanted

The idea behind my project was to make a system that could visualize or represent posts on the subreddit /r/relationships. These posts are often pretty emotionally charged and an interesting blend between general and universal to highly specific. Keeping in mind that most people post their with the hope of solving issues within their personal life, my conception was to use Python to generate a series of sentiment analysis scores from the posts. Said scores that would then be used to modulate different parameters on instruments to produce a sound that dynamically evolves with the emotional content of each post. I overall achieved my goal, and while the sound is not quite as melodious as I might have hoped, I like to think that reflects just how emotionally charged and ambigious most relationships can be.

The project was split into two components, a Python pipeline that received new posts from /r/relationships and updated two big CSV files, and a system of Max patches that generated audio based on recieved input from those files. I’ll go into detail for my process for both below:

Python Pipeline:

The result of running posts through the pipeline generates 2 large space delimited files, one of scores and one of titles features. Each post has 6 scores, which were the 5 highest value (in terms of magnitude) sentiment scores corresponding to what was interpreted as the 5 words with the most emotion beyond it, and the overall average sentiment of the entire text, which was the 6th score. (Format: 1st highest score, 2nd highest score and so forth…, followed by Overall score) Title extraction was based on a rule of /r/relationships, which specifies every post must mention gender and age of both the poster and any people referred to within the post. Even with this in mind, this part was messy. While not all posts were simply between two people, after analysis around 83% of them were, so to avoid issues with input inconsistency later in Max, I removed posts that didn’t correspond to this two person rule. Even then however, there were issues with inconsistent formatting (not everyone follows subreddit rules) that my pipeline couldn’t parse properly, so if values seemed malformed I set up a default value (the tuple (30,-1,30,-1)) to substitute those values. (Format: Age,Gender,Age,Gender, where Male was represented by -1 and Female by -2)

A sidenote: the choice to only keep posts between two people lead to a bit of a change in focus; while not all initial posts were focused on relationships with significant others, a good amount of two-people posts were. I decided to incorporate a piano rendition of the main melody from Fatima Yamaha’s “What’s a Girl to Do”, as the track feels like it’s about the tension and ambiguity inherent in relationships, something powerful that I wanted to try and capture.

There are four files in the python zip. Before running any of them, you must set up your own reddit account and OAuth permissions and install some python modules: PRAW, reddit’s API, numpy,glob, and nltk.vader (you may have to install the whole package for nltk but it’s a lot of stuff, so I would try to only install vader first and install all of it if there are issues).To set up an OAuth token for reddit, follow this tutorial here: https://praw.readthedocs.io/en/latest/getting_started/authentication.html .
1) testq.py : go in and edit the four values in snag() to set up your own bot. Run in terminal with the destination for three output files (titles,text,url) to output a stream of the most recent posts broken into titles, text, and urls into those three different files. For reference, my command when running it looks something like this:

python3 /Users/lawrencehan/PycharmProjects/reddit/testq.py /Users/lawrencehan/Desktop/project_one/titles.txt /Users/lawrencehan/Desktop/project_one/text.txt /Users/lawrencehan/Desktop/project_one/url.txt

2) senti.py: Next, take the files you’ve just output and run them with senti. Senti should output three files, one for the title and two for the 5 scores and overall score respectively.

I ran these previous files several time before running the latter two while deciding how exactly I would get all the data into Max, which meant a lot of output that was split up into different files

3) cleanup.py:  This should output two csv’s containing title representations and scores of all files written within the directory. The second zip attached to this post contains all the non python files, with directories already generated for titles and scores. I would recommend you do all this processing within that folder. When you run the previous two python files, you’ll get 3 outputs that are consolidated in this step into files that you will always write to if you want to update them with more scores.

4) final.py: Max has a problem with recognizing commas in text input. That, along with formatting issues, meant that this file was needed within the pipeline as well. This converts csv files into regular space delimited files and ensures that the last number of each line is not rendered as a string, both issues that strongly affect how Max reads in these files.

After running each step of the pipeline, you should have two csv’s that look something like this:

Text editor view:

Excel/Numbers view:

Max/MSP:

Before beginning this process, I’d known by this point I wanted to incorporate the Yamaha track melody in same way. Because most sounds generated by Max sound pretty synthetic, I decided to render the melody with piano, to provide a counterpoint of sorts. I decided to use a kickdrum as my main percussive element and a low end bass drone as well as a high pitched reedy sounding drone for higher frequency content. I settled on these elements because they were relatively static and easier to hear the effects of modulation upon. Upon generating these instruments, I connected them all within a main patch. Each instrument took in some combination of scores and age/gender information, which were then used to modulate different interior parameters like filter resonance and distortion levels. Many of this score/gender information was ramped as it updated from one value to the next, which allowed for smoother transitions and avoid “clicks” from rapid changes.

When opening the Max Patch, load in alltitles,allscores,and the fatima piano loop in that order.

The output is very noisy and chaotic; here’s a sample of it below:

 

And here are the files you need to make this work:

reddit

project_two 2

Project 2 – Twinkle Twinkle Little Rockstar

For this project, Bo and I decided to merge some aspects of our project 1 assignments, mine being an autotuner patch and his being an audiovisualizer, to create a Rockband-esque game that tracks what note you’re singing and scores it based on if you’re being corrected by the autotuner to the correct pitch in Twinkle Twinkle Little Star.

My personal contribution to this project consisted of supplying Bo’s audiovisualizer with any necessary info about the game logic, markers, current/upcoming notes, color values to display, etc. that could be variant based on how the player is doing within the game. I also created an easy and a hard mode for the game, with easy mode correcting you to only notes in the major scale of the key you’re playing the game in and hard mode correcting you to a chromatic scale no matter what key you’re in, making your margin of error slightly larger. As mentioned, the game can be played in every different major key, so users with different vocal ranges can play the game.

In addition, any of the audio heard throughout the duration of the game is coming from my patch, be it the piano playing the MIDI notes along with you or the autotuned signal. The autotuned signal is located in the “inputs” encapsulation, which also includes the game controller bpatcher I created, and within the “inputs” encapsulation is the “twinkle” encapsulation, which processes and outputs all of the variables specific to Twinkle Twinkle Little Star in a manner that Bo’s patch can then work with and draw from.

When it comes to game logic, we score based on how many beats you sing correctly (including however many the autotuner helps you to get), and the score is displayed on the game controller. You can then start a new game in whichever key you select. Regardless of what key you choose, there is a four beat lead-in playing the first note at tempo so you can get ready before the game round begins.

To demonstrate, I put myself through the ringer on easy mode:

This folder includes my contributions:

https://drive.google.com/drive/folders/1Fx1sD51GNGYjVY1dpQXl9M3f8qIQ4ZKS?usp=sharing

This folder contains the overall project:

https://drive.google.com/drive/folders/1LLSwpto8pGiUxBWq3klx1izvc2katNzs?usp=sharing

And here is my code:

<pre><code>
----------begin_max5_patcher----------
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-----------end_max5_patcher-----------
</code></pre>
view raw inputs hosted with ❤ by GitHub

Project 2 – Particle System Tracking

For this project, I wanted to work with particle systems and attractors within it.

First, I used a few objects from the computer vision package to track a face from the incoming camera and get points from it with edge detection. I did this with cv.jit.faces to get the bounding box of a face to track, then used cv.jit.canny to extract binary edges, then cv.jit.features2track to collect the easiest points in the window to track. This works best with binary edges because of the contrast between black and white pixels.

I then separated the points into two lists: x-coordinates and y-coordinates, normalized them, and sent them to the particle system subpatcher. There are three modes the user can choose from:

0: the system as a whole attracts to several different points. For example, if there are 50 attractor points, 1/5 of the particles will attract to each point.

1: all particles jump between attractors at banged intervals.

2: particles follow the center of mass of the tracked face.

Once the list of x and y coordinates are sent, I filled a 250-cell (number of particles) matrix with the coordinates (repeatedly, so that an even number of cells has each coordinate). These were then used as another input into the jit.gen objects which calculated the force. A jit.gen object basically transforms every cell in the incoming matrices simultaneously. So a cell in the particle system (inlet 1) could take the corresponding x,y coordinate of it’s attractor in the second matrix (inlet 2).

I used the Amazing Max Stuff Particle System method of calculating new positions through f=m*a. I definitely spent a lot of time trying to understand how jit.gen works, since it is much more efficient at manipulating matrices. I had a lot of trouble figuring out how to use the x,y coordinate matrix of point positions in jit.gen. After understanding that the jit.gen object basically works on every cell of each input matrix, it became much simpler to just gibe the position matrix as another input. Before figuring that out, I tried a lot of different things using swiz and vec that involved pulling the matrices apart in the jit.gen object and trying to put them back together afterwards. This, of course, was very tedious and didn’t work well. I had to find a way to use jit.gen because before that, the patch used way too much processing power and the particles were not moving smoothly. I also attempted some javascript, but found it to be difficult to integrate with max objects.

You can also change a lot of parameters such as speed, level of attraction, rate of position change, etc. throughout the patch.

I originally wanted to have the particles attract to facial points so that you could actually recognize a face, but I think this would take outside hardware or more sophisticated algorithms for facial point detection. Even so, I definitely enjoyed the finished product.

Below I’ve uploaded three videos, one for each mode. When you first open the patch, the video and facial detection automatically start. To start the particle system, you toggle it on manually. For all modes, you can change rate at which the particles attract/retract in the particle system subpatcher. It is set automatically on a metronome.

Mode 0:

In this mode, you can see the particle system as it attracts towards the facial points. Moving your face also moves the system as a whole.

Mode 1:

In this mode, the system jumps between facial points on a metronome.

Mode 2:

In this mode, the system follows the center of mass of the facial points. You can see how they follow my face as I move.

Here’s the code for the patch:


----------begin_max5_patcher----------
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-----------end_max5_patcher-----------

IR Sensed Granular Synthesis and LEDs response- Project 2

For my final project in Twisted Signals, I thought of building upon my previous project where I used max to send data to arduino and make LEDs react accordingly. For this project, I decided to send data from sensors connected to arduino to Max. These signals then would control different aspects of the granular synthesis engine I am using(Sugar Synth by nobuyasu sakonda
http://web.mac.com/nsakonda/ ). The four aspects I intended to control were 1) Playback Speed 2) Grain Position 3) Grain Size 4) Pitch. I decided to merge my previous LED project to it as well so that changes in these aspects of the sound reflected in the LEDs. As a prototype, all 4 of my sensors were working and all 8 of LEDs were working. However, when I tried to implement it in a project box(courtesy of Professor Jesse Stiles), the wiring became quite complicated as the following picture shows-

I learnt to drill holes into the plastic project box to fit the sensors and LEDs on it. Then, with the help of my friends Mehar Goli, Caio Arajuo, and Takshsheel Goswami, we soldered the sensors and LEDs to wires. All the connections were made inside the box using these wires with the sensors and LEDs sticking out of the box. However, this setup is very fragile as the wires can get easily disconnected. This resulted in one of the sensors not working properly and one of the LEDs not working either. However, I made the project work without these. The sacrifice I had to make was control grain size and position using the same sensor just scaled at different amounts.

I made use of two Arduino boards  – one for the LEDs and one for the sensors. I couldn’t use the same board as the serial port couldn’t send and receive data between Arduino and Max simultaneously. Arduino sent the data in a specific format and using some simple commands in Max, I extracted the required numbers from the IR sensors and scaled them and fed them to various aspects of the grains.

Here is the demo video where the IR sensors sense how far my fingers are and the grains are produced accordingly. You can also see the LEDs reacting accordingly.-

Song used: Ed Sheeran-Shape Of You

Arduino code for sensors(LEDs in previous project)-

int var0;
int var1;
int var2;
int var3;
int var4;
void setup() {
// put your setup code here, to run once:
pinMode(A0,INPUT);
pinMode(A1,INPUT);//350
pinMode(A2,INPUT);//360
pinMode(A3,INPUT);//400
pinMode(A4,INPUT);//130
Serial.begin(9600);
}

void loop() {
// put your main code here, to run repeatedly:
//var0=analogRead(A0);
var1=analogRead(A1);
var2=analogRead(A2);
var3=analogRead(A3);
var4=analogRead(A4);
Serial.print(var1);
Serial.print(” “);
Serial.print(var2);
Serial.print(” “);
Serial.print(var3);
Serial.print(” “);
Serial.println(var4);
delay(100);
}

Max Patch-

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Kinect: Human Theramin

Heyooo

For our final project, Ken and I originally wanted to control a videogame by using a kinect sensor. But we ran into problems getting it to work with emulators, so we decided to keep the kinect and make a human theramin with it!

My contribution was just the getting the data, while Ken was in charge of the sound synthesis.

My first step was to use the extension dp.kinect2, which is used to get all kinds of data from the kinect to use in a maxpatch.

We made sure that we could get the skeleton data from what is infront of the kinect, and I then picked out the separate data for the head, feet, and hands. (The data itself is the body part on an XYZ axis.) I then sent the data over to Ken’s computer, and we used that to make the rest of the project!

Overall I like how it turned out, because if you’re precise you can actually make some music with our project. But we didn’t have enough time to add more to it, but if we did it probably would’ve been doing something with the video footage from the kinect as well.

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view raw Kinect Max Hand data Patch (1).maxpat hosted with ❤ by GitHub

Project 2; FacePlace

FacePlace explores the intersection of real and virtual worlds, through the examination, digital representation, and sonification of faces. Utilizing FaceOSC, interfaced through Max, we were able to capture single frames, or a picture, of guests’ faces. Although the goals of this project were mostly technical, the experience has proven to be quite thought provoking. The idea of “scanning” your face, only for it to exist in a virtual world leaves the installation’s concept very open ended.

These frames were sent to exist in a virtual world, powered by Max, and experienced in an HTC Vive. Simultaneously, the raw data received over OSC from FaceOSC was analyzed in Max, and then scaled to be appropriately used to control multiple parameters of processors.

Adrian worked on producing the VR space, creating the virtual world, and handling technical issues surrounding the VR delivery, communication between devices, as well as over-all user experience design.

Joey worked hand in hand with Adrian on user experience design, as well as splicing data from FaceOSC (supplying Adrian with matrices over jit.net.send objects), and utilizing this data to create a unique sonic environment for each face, using custom sound files. In order to process these sound files into the virtual world, the HOA library (RIP)  was used for spatialization.

First tests of a virtual world in the Vive, powered by Max.

Testing the VR world, using Seymour as our test subject.

First playtests of the installation.

Experience photos to come.

Git Repository

Project 1: Convolution Kinect

For my first project, I utilized a Microsoft Kinect to gather movement information on a person, within a space, and apply the tracking information to parameters on a granular synthesis. At the end of the pipeline, the resulting sound was then processed into a Lissajous pattern and project onto a wall behind the performance space.

To farm the information from the Kinect, I used Processing, an open-sourced graphical library, which was programmed to collect data from the Kinect, parse out only the left hand, right hand, and head X-Y Coordinates. Then, using OSC I sent this bundle of coordinates to Max MSP.

Using the UDPRECEIVE object, I received data from Processing, containing coordinates.

Next, I scaled these numbers to be useful by the granular synthesis parameters.

Scaling the data which I am receiving from the Kinect, to be useful for granular synthesis parameters.

Originally, I started with a rather bland audio file to be processed, however, I realized soon that I would need a more complex sound file to really accentuate the differences in the granular synthesis. To do this, I used a file of wind chimes, which proved to be much more textual and rewarding.

Below is a video of the piece in work, as well as the max patch and processing files.

IMG_1881

https://gist.github.com/jsantillo/9441d9acfa79fbc7548cc4aeaf5d0150.js

 

Project 1 – Vertex Extrusion Objects

For this project, I started with the simple idea of vertex extrusion after seeing a tutorial on youtube. I used incoming video for both the object itself and the texture, and rendered using jit.gl.mesh. The jit.gl.handle object allows the viewer to visually spin and change the view based on mouse control. You can change the shape of the object being rendered as well. I then created multiple of these objects and set them with changing rotation, position, and scale using jit.mo functions like we’ve done in class. The default starts with 2 objects, but every time you clap the number of objects doubles until you reach 64. At this point the number of objects goes back to 2 because jit.multiple can’t handle a huge number of objects. You can also manually change how much space there is between the objects and the position of the camera (default is 2 for the y coordinate). I then used a motion detection patch to change the rate of scaling for each object as well as sliding between MIDI frequency notes based on your motion.

Here’s the patch:


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-----------end_max5_patcher-----------

Overall, I’m pretty pleased with the results and definitely am a lot better at jitter and matrixes than before!

Project 1 – Visual Singing

For This project I wanted to find a way to combine FaceOSC with a Particle System. Most of my time was spent familiarizing myself with making and altering matrices in jit, as well as creating and recreating the system of particles through learning and understanding the physics principles, learning max functions and codes, as well as experimenting with changing parameters to influence my output.

What I have is a particle system that creates graphics based off of the movement and motion of your head and face.

 


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-----------end_max5_patcher-----------