Skip to content
View crlandsc's full-sized avatar
🏈
Go Bills!
🏈
Go Bills!
64 followers · 23 following

Achievements

Achievement: QuickdrawAchievement: Starstruckx2Achievement: Pull Sharkx2Achievement: YOLO

Achievements

Achievement: QuickdrawAchievement: Starstruckx2Achievement: Pull Sharkx2Achievement: YOLO

Organizations

@wb-video

Block or report crlandsc

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
crlandsc/README.md

Hi there 👋 my name is Chris!

I am an audio machine learning engineer and researcher working on advancing audio AI/ML and spatial audio capabilities.

My recent work has focused on binaural externalization, audio waveform diffusion for generative audio, and audio source separation for music "demixing".

I also make music under the name 🎶After August.

Please reach out if you have any questions, or if you are interested in chatting about audio, music, AI/ML, spatial audio, or all of the above!

Follow my work, writing, and music on:

My Website | Medium | LinkedIn
YouTube | Spotify | Facebook | Instagram

Go Bills! Buffalo Bills Logo

Pinned Loading

  1. tiny-audio-diffusion tiny-audio-diffusion Public

    A repository for generating and training short audio samples with unconditional waveform diffusion on accessible consumer hardware (<2GB VRAM GPU)

    Python 157 16

  2. Music-Demixing-with-Band-Split-RNN Music-Demixing-with-Band-Split-RNN Public

    An unofficial PyTorch implementation of Music Source Separation with Band-split RNN for MDX-23 ("Label Noise" Track)

    Python 149 13

  3. torch-log-wmse torch-log-wmse Public

    logWMSE, an audio quality metric & loss function with support for digital silence target. Useful for training and evaluating audio source separation systems.

    Python 28 1

  4. Model-based-Bayesian-DoA-Analysis-for-Sound-Sources-Using-a-Spherical-Microphone-Array Model-based-Bayesian-DoA-Analysis-for-Sound-Sources-Using-a-Spherical-Microphone-Array Public

    A machine learning algorithm that estimates the directions of arrival and relative levels of an arbitrary number of sound sources using recorded data from a 16-channel spherical microphone array.

    MATLAB 11 2