Demucs
Demucs is a family of neural network models for music source separation developed by Facebook AI Research (FAIR). The primary goal is to separate a mixed music track into individual stems, typically vocals, drums, bass, and other accompaniment. Unlike many frequency-domain approaches, Demucs operates in the time domain using a convolutional encoder–decoder network with skip connections, a design inspired by Wave-U-Net. The multi-resolution architecture captures both short- and long-range temporal context, enabling more accurate separation.
Since its introduction, Demucs has undergone several iterations to improve separation quality and computational efficiency. The
Demucs has been evaluated on standard benchmarks such as MUSDB18, reporting improvements in signal-to-distortion ratios for
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