2021 Projects
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Displeremin
By Atishay Lalgudi, Elizabeth Yoon, Amanda Xu
Project Description: Inspired by the Theremin, the Displeremin will allow you to play and create music without any physical contact. Using an ultrasonic distance sensor, the Displeremin is able to create tones of different frequencies based on your distance away from it. All you need to do is move your hand in the air around it! Additional features include a button to change octaves, an LED to warn you if you are too close, a knob to adjust volume, and an LCD screen that tells you exactly what note you’re playing!
Raganet
By Tejas Bharadwaj, Paurush Pandey, Trinav Chaudhuri
Project Description: Coming from an Indian classical music background, the three of us wanted to combine this field with that of machine learning. Raganet trains a model to distinguish between different ragas, a staple of Indian classical music, which roughly translates to the scale of a certain piece. As of now, our model can distinguish between 2 ragas: Bhimpalasi and Desh.
Arteo
By Annie Hu, Daylin Atwood, Ashley Chen
Project Description: In order to combine our group’s interests in art, music, and choreography, we thought of a project that could translate from one form of art to another. Arteo would be able to convert dance movements to sound through linking Python and Pd. By mapping the sound to visuals using GEM, Arteo is able to produce a complete performance all in one. It is a multimedia experience showcasing the intersection of dance, music, and graphical art.
Speci-Chords
By Daly Gibson, Andy Wang, Nithin Chilakapati
Project Description: A generator that creates accompaniments to an input melody. This accompaniment would try to follow a chord progression pre-selected by the user, and also potentially include a bass-line and drum backing.
You Music
By Anshul Gupta, Andrew Liu, Kat Guo
Project Description: Using Pure Data, Python, and Raspberry Pi, the program will make personalized generative music based on a user’s physiological functions that suits their current need. The BPM of the music and drum machine will match and change with the heart rate of the user, based on data from a heart rate sensor attached to a Raspberry Pi through an Analog to Digital Converter. Users input a mood to change the tonality of an algorithmic chord progression generator capable of modulating keys. These inputs are continuously sent via IP using sockets to generate music using Pure Data on a local machine to allow for live-time changes in music.
Moodsic Generator
By Vivian Zhu, Jeanie Chan, Japleen Kaur
Project Description: The Moodsic Generator aims to help people feel their comfortable emotions when listening to their favorite songs during difficult times like this pandemic. A user says the name of a song to our Moodsic gadget that we built using a Raspberry Pi4. Our Moodsic Generator then recognizes the song and plays back either the original song or the style transferred version (happy, sad, or calm) based on the user’s selection using buttons on the Raspberry Pi. The LED corresponding to the selected mood lights up. We trained the three types of songs using the style transfer CNN machine learning model (modified based on Ulyanov’s code from Github). The user can also adjust the pitch to tweak the output to their liking.
Simelo
By Casey Chen, Flora Yuan, Joel Kim
Project Description: A music search engine that is able to compare songs within a directory based on set parameters, including tempo and key, using a similarity index. The program will ask for an input .wav file from the user, which will then output how similar the input song is.