Hi, I'm Chris.

I love everything to do with sound.

Welcome to my site.

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ABOUT

Personal Profile

I love everything involving music, technology, and sound. I work as a machine learning engineer researching and developing new audio technologies with a focus on deep learning and spatial audio. I have also consulted on the acoustical design of the built environment, with projects ranging from performance venues to recording studios. My work and research include developing and implementing algorithms to perform audio source separation, music sample generation, sound source detection, and binaural audio externalization. I have also developed other software to measure acoustics metrics, model wave behavior, process spatial audio, and create audio effects plugins.

When I am not focused on the design of sound and technology through my work or research, I am writing, recording, or performing music. I play guitar (electric, acoustic, bass), percussion, piano, and even dabble with the banjo. My artist name is After August (check me out on Spotify!), and I play musical styles from jazz to rock to folk and everything in between. I love all types of music.

I live in Chicago with my wife, Steph, and our little dog, Queso. Steph works as an IR Nurse at Northwestern Memorial Hospital assisting with groundbreaking operations daily and Queso takes naps on my lap daily as I work at my desk. I play in the local kickball league and love to escape to the Adirondack Mountains in NY each summer for some hiking, kayaking, and time with the family.

About

PROFESSIONAL EXPERIENCE

Background & Expertise

August 2023 - Present

Machine Learning Engineer,
Whitebalance

Developing deep learning algorithms for audio source separation and audio enhancement. Building core product technology by implementing state-of-the-art audio source separation frameworks, developing model improvements, and curating audio datasets.

August 2022 - Present

Collaborating Audio Research Scientist,
Virtuel Works

Collaboratively develop and implement a post-processing real-time binaural externalization algorithm for object-based spatial audio focused on improving immersive audio experiences without introducing timbral artifacts.

March 2020 - February 2023

Acoustics Consultant,
Threshold Acoustics

Performed acoustic design in room acoustics, sound isolation, and noise control disciplines. Collaborated on a research team developing in-house software to model wave behavior via the finite-difference time-domain method as well as tools for impulse response and general acoustics analysis.

August 2018 - March 2020

Acoustics Specialist,
Kirkegaard Associates

Performed acoustic design in room acoustics, sound isolation, and noise control disciplines. Developed auralization software using Max MSP that can encode, convolve, and decode higher-order ambisonic signals. Created a graphical user interface for client presentation and internal use.

August 2017 - August 2018

Research Assistant,
Rensselaer Polytechnic Institute

Created a novel machine-learning algorithm using Matlab that estimates the directions of arrival and relative levels of an arbitrary number of sound sources using a spherical microphone array. Produced two scientific papers published in peer-reviewed scholastic journals as well as a pending patent.

Professional Experience

Projects

PROJECTS

Recent Audio & Machine Learning Projects

Tiny Audio Diffusion

Built a lightweight system that applies waveform diffusion (1D U-Net) to generate short audio samples (such as drum sounds) to be trained and run on a low-level consumer GPU (<2GB VRAM). The purpose of this project is to provide access to waveform diffusion code for those interested in exploration but who have limited hardware resources. Model training metrics available on W&B.

Generate Samples With Tiny Audio Diffusion

Music "Demixing" (Sound Source Separation)

Built a music source separation system in Python using a band-split recurrent neural network (RNN) framework based on this paper to compete in AIcrowd's Sound Demixing Challenge 2023. Four models (voice, bass, drums, other) were trained on a Google Cloud Platform GPUs with W&B tracking and resulted in an improvement over the baseline model on the "Label Noise" track by 42%.

Harmonai AI Song Production Challenge

Wrote and produced a song using AI-generated audio by leveraging the audio diffusion tool Dance Diffusion. Utilized waveform-based (1D convolution) diffusion with unconditional generation and audio style transfer functions to produce high-quality 48kHz samples.

Music Genre Classification

A music genre classification system was created using a convolutional neural network (CNN) in Python and achieved an accuracy rate of 83.8% from a baseline accuracy of 10%. The CNN model was trained on Mel-spectrograms of 3-second audio samples. Additional data augmentation and regularization methods were implemented to optimize performance and mitigate overfitting.