We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| GPUPhobia/vocal-mask | reimplementation | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| load_checkpoint | Ran | GPUPhobia/vocal-mask/generate.py pointer only (licence: NONE) · get_code("4377c25d73df4b07") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase. To avoid omitting potentially useful information, we study the viability of using end-to-end models for music source separation --- which take into account all the information available in the raw audio signal, including the phase. Although during the last decades end-to-end music source separation has been considered almost unattainable, our results confirm that waveform-based models can perform similarly (if not better) than a spectrogram-based deep learning model. Namely: a Wavenet-based model we propose and Wave-U-Net can outperform DeepConvSep, a recent spectrogram-based deep learning model.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1810.12187")
get_code_for_paper("1810.12187")
have("1810.12187")
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