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

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]

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

My contribution to the project was primarily on the visual end. I took in the variables that Alec’s patcher gave me and represented them using jit.gl.sketch and jit.gl.text within a js object. In addition to the point cloud that expands whenever the player sings, I modified the particle system to change the hue of the particles in correspondence with the note sung by the player. At the bottom of the screen, I added a player cursor – which has its y-position determined by the note sung by the player and a fixed-length tail that shows the past sung notes – and a scrolling bar of upcoming notes in the song. I then added a score counter and a method of state-switching between gameplay and game over screens.

This Drive folder has my contributions, including all of the javascript class files,

and this Drive folder hold all the files for our project as a whole.

Here’s a gist for the visualization patcher, although it won’t be of much use without the js files:

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:

In order to emphasize the movement of the sounds, I also have uplighting for the loudspeakers, where each sound corresponds to either red, green, or blue and the moving average amplitude of a signal coming out of a speaker dictates the color values for the lights.

The sounds that are played include sounds made from granular synthesis using parameters based on an accelerometer sent through a Raspberry Pi as well as other effects applied to audio files controlled by a Seaboard (done by Ramin Akhavijou and Rob Keller).

This Google Drive folder includes all of the Max Patches for our project.
https://drive.google.com/open?id=1WZH1nr-ARBmZOF9gPrks3_Oh1Q5mJTS8
The top-level patch that incorporates everything with a (somewhat organized) presentation view is main.maxpat. Most of my work (aside from putting together main.maxpat) is in ambisonics.maxpat, which in turn has several subpatches and sub-subpatches and so on.
ambisonics.maxpat is what receives sounds, sound position coordinates, and outputs data to speakers and lights. poly voicecontrol is where the positioning of an individual sound is handled. placement.maxpat calculates the position for a sound (using coord.maxpat) and spatialize.maxpat contains calls to the HOA Library to calculate the signals that should come out of each speaker channel. These are sent to poly light to calculate the light channel value and write it into the appropriate cell of a global matrix. The global matrix is accessed in ambisonics.maxpat to the lights.

Here’s a rough video of our project in action

Here is the python code for accelerometer:

import time
import board
import busio
import adafruit_mma8451

Initialize I2C bus.

i2c = busio.I2C(board.SCL, board.SDA)

Initialize MMA8451 module.

sensor = adafruit_mma8451.MMA8451(i2c)

Optionally change the address if it’s not the default:

#sensor = adafruit_mma8451.MMA8451(i2c, address=0x1C)

Optionally change the range from its default of +/-4G:

#sensor.range = adafruit_mma8451.RANGE_2G # +/- 2G
#sensor.range = adafruit_mma8451.RANGE_4G # +/- 4G (default)
#sensor.range = adafruit_mma8451.RANGE_8G # +/- 8G

Optionally change the data rate from its default of 800hz:

#sensor.data_rate = adafruit_mma8451.DATARATE_800HZ # 800Hz (default)
#sensor.data_rate = adafruit_mma8451.DATARATE_400HZ # 400Hz
#sensor.data_rate = adafruit_mma8451.DATARATE_200HZ # 200Hz
#sensor.data_rate = adafruit_mma8451.DATARATE_100HZ # 100Hz
#sensor.data_rate = adafruit_mma8451.DATARATE_50HZ # 50Hz
#sensor.data_rate = adafruit_mma8451.DATARATE_12_5HZ # 12.5Hz
#sensor.data_rate = adafruit_mma8451.DATARATE_6_25HZ # 6.25Hz
#sensor.data_rate = adafruit_mma8451.DATARATE_1_56HZ # 1.56Hz

Main loop to print the acceleration and orientation every second.

while True:
x, y, z = sensor.acceleration
print(‘Acceleration: x={0:0.3f}m/s^2 y={1:0.3f}m/s^2 z={2:0.3f}m/s^2’.format(x, y, z))
orientation = sensor.orientation

Orientation is one of these values:

– PL_PUF: Portrait, up, front

– PL_PUB: Portrait, up, back

– PL_PDF: Portrait, down, front

– PL_PDB: Portrait, down, back

– PL_LRF: Landscape, right, front

– PL_LRB: Landscape, right, back

– PL_LLF: Landscape, left, front

– PL_LLB: Landscape, left, back

print(‘Orientation: ‘, end=”)
if orientation == adafruit_mma8451.PL_PUF:
print(‘Portrait, up, front’)
elif orientation == adafruit_mma8451.PL_PUB:
print(‘Portrait, up, back’)
elif orientation == adafruit_mma8451.PL_PDF:
print(‘Portrait, down, front’)
elif orientation == adafruit_mma8451.PL_PDB:
print(‘Portrait, down, back’)
elif orientation == adafruit_mma8451.PL_LRF:
print(‘Landscape, right, front’)
elif orientation == adafruit_mma8451.PL_LRB:
print(‘Landscape, right, back’)
elif orientation == adafruit_mma8451.PL_LLF:
print(‘Landscape, left, front’)
elif orientation == adafruit_mma8451.PL_LLB:
print(‘Landscape, left, back’)

And here is the code for sending data from Pi to Max using wifi:

import socket

from time import sleep

from time import time

host = ‘….’

port = 5560

def setupSocket():

s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

s.connect((host, port))

return s

For the project, I used the dp.kinect2 object, along with the starter patch shared in class for the Kinect to help with getting the Kinect data. I wanted to learn more about particle systems since the visuals created with them always look super cool, so I added a hand controlled particle system as a visual, with some help from a YouTube tutorial. At this point everything visual was very fluid, so I wanted something stationary in the image as well, so I used jit.gl.mesh and jit.gl.gridshape to create a stationary shape that makes it seem like you’re in a spiral.

For the sounds, I wanted them to be simple, to contrast the visuals, but also controllable. I ended up having both hands controlling the frequencies of two different sounds, each going to a different channel. I mapped the coordinates of the hands to reasonable frequencies, and fiddled around in order to have controlling the pitch on each hand be pretty reasonable. I played around with using the head to create a tremolo effect, but I didn’t like the sound created, so I scrapped it.

Having done this, I wanted to add more to the visuals, so I had the colors of the particle system and the color of the shape change with the sound. I had different components of the sound controlling the RGB values of the particle system, and had the same components plus the position of the head control the color of the shape.

Here’s a video of how it works:

And here’s the patch:

Below is a video of me detailing my max patch extensively, and showing how to use it (save for the subpatches which can be viewed in the attached zip file at the bottom, also, audio is included in the video):

Below is a gist of the main patch:

Finally, I’ve attached a zip file with all the files you’ll need to use the patch:

seaboardfinal

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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o5Jop2b32cCfDjhThK.bSKM6yYppH0BQ9gDIRf7KS7e+y+zCOu8qI6e.8OP+
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-----------end_max5_patcher-----------