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Go Bills!
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Go Bills!
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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

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  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 155 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 147 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