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    Hi all, I've spent some time working on music demixing or music source separation algorithms, which take in a mixed song and output estimates of isolated components (e.g. vocals, drums, bass, other). I took a popular PyTorch model with good performance (Open-Unmix, UMX-L weights), reimplemented the inference steps in C++, and compiled it to WebAssembly for a free client-side music demixer.

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    Hi HN, Last time I showed free-music-demixer, which people seemed to enjoy. It was a static website with a Javascript + WASM module to perform music demixing (or music source separation) using an AI model UMX-L (Open-Unmix) running client-side in the browser. Since then, I have overhauled the project and made several improvements: - The demixing/separation quality is higher now, since I implemented the missing post-processing step - Memory usage is lower now by performing a custom segmented inference with a streaming LSTM, which should allow larger tracks (or, dare I say,…

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