We lifted 11 functions out of this paper's own repositories and ran 10 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 |
|---|---|---|
| sp-uhh/sgmse-bbed | canonical | 10 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| energy_ratios | Ran | sp-uhh/sgmse-bbed/sgmse/util/other.py code served (permissive licence) · get_code("e4185b3ed3a097b6") |
| get_act | Ran | sp-uhh/sgmse-bbed/sgmse/backbones/ncsnpp_utils/layers.py code served (permissive licence) · get_code("216c782f9d07ef3a") |
| get_activation | Ran | sp-uhh/sgmse-bbed/sgmse/backbones/dcunet.py code served (permissive licence) · get_code("eb5ba43dadcbda84") |
| get_window | Ran | sp-uhh/sgmse-bbed/sgmse/data_module.py code served (permissive licence) · get_code("9e802d63089d5d62") |
| make_unet_encoder_decoder_args | Ran | sp-uhh/sgmse-bbed/sgmse/backbones/dcunet.py code served (permissive licence) · get_code("9ce3b9f139bb58ff") |
| mean_conf_int | Ran | sp-uhh/sgmse-bbed/sgmse/util/other.py code served (permissive licence) · get_code("f36a0fc077e7eeca") |
| si_sdr_components | Ran | sp-uhh/sgmse-bbed/sgmse/util/other.py code served (permissive licence) · get_code("5f57b9e7cfd0a20d") |
| torch_complex_from_reim | Ran | sp-uhh/sgmse-bbed/sgmse/backbones/shared.py code served (permissive licence) · get_code("63cec12b7bc5275c") |
| unet_decoder_args | Ran | sp-uhh/sgmse-bbed/sgmse/backbones/dcunet.py code served (permissive licence) · get_code("0eabe078d2ed0794") |
| variance_scaling | Ran | sp-uhh/sgmse-bbed/sgmse/backbones/ncsnpp_utils/layers.py code served (permissive licence) · get_code("3ac11bfe0ae80195") |
| ncsn_conv1x1 | Not yet run | sp-uhh/sgmse-bbed/sgmse/backbones/ncsnpp_utils/layers.py code served (permissive licence) · get_code("85b2235eb7afaa4f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recently, score-based generative models have been successfully employed for the task of speech enhancement. A stochastic differential equation is used to model the iterative forward process, where at each step environmental noise and white Gaussian noise are added to the clean speech signal. While in limit the mean of the forward process ends at the noisy mixture, in practice it stops earlier and thus only at an approximation of the noisy mixture. This results in a discrepancy between the terminating distribution of the forward process and the prior used for solving the reverse process at inference. In this paper, we address this discrepancy and propose a forward process based on a Brownian bridge. We show that such a process leads to a reduction of the mismatch compared to previous diffusion processes. More importantly, we show that our approach improves in objective metrics over the baseline process with only half of the iteration steps and having one hyperparameter less to tune.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2302.14748")
get_code_for_paper("2302.14748")
have("2302.14748")
Connect an agent — have() is free.