Ali Siahkoohi, Rudy Morel, Maarten De Hoop, Erwan Allys, Grégory Sainton, Taichi Kawamura
We lifted 11 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| alisiahkoohi/insight_src_sep | canonical | 8 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| adaptive_choice_P | Ran | alisiahkoohi/insight_src_sep/srcsep/layers/filter_bank.py code served (permissive licence) · get_code("0074d8c5464f09c0") |
| fbm | Ran | alisiahkoohi/insight_src_sep/srcsep/stochastic_classical_models/gaussian.py code served (permissive licence) · get_code("f8a29d1bb8e48b4e") |
| format_np | Ran | alisiahkoohi/insight_src_sep/srcsep/frontend.py code served (permissive licence) · get_code("eb6aa8b8c0a804df") |
| format_tensor | Ran | alisiahkoohi/insight_src_sep/srcsep/frontend.py code served (permissive licence) · get_code("6feb3bb9045c65ea") |
| gaussian_cme | Ran | alisiahkoohi/insight_src_sep/srcsep/stochastic_classical_models/gaussian.py code served (permissive licence) · get_code("abf8c29404abdb80") |
| geom_brownian | Ran | alisiahkoohi/insight_src_sep/srcsep/stochastic_classical_models/gaussian.py code served (permissive licence) · get_code("3819d3ac9b0f1a10") |
| morlet_1d | Ran | alisiahkoohi/insight_src_sep/srcsep/layers/filter_bank.py code served (permissive licence) · get_code("2765481b240e9cd9") |
| periodize_filter_fourier | Ran | alisiahkoohi/insight_src_sep/srcsep/layers/filter_bank.py code served (permissive licence) · get_code("d8db111ae4f772ea") |
| gaussian_w | Not yet run | alisiahkoohi/insight_src_sep/srcsep/stochastic_classical_models/mrw.py code served (permissive licence) · get_code("489717784ebb23d4") |
| mrw | Not yet run | alisiahkoohi/insight_src_sep/srcsep/stochastic_classical_models/mrw.py code served (permissive licence) · get_code("6e955a67739b672c") |
| skewed_mrw | Not yet run | alisiahkoohi/insight_src_sep/srcsep/stochastic_classical_models/mrw.py code served (permissive licence) · get_code("186de8e135fb3a5e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Source separation involves the ill-posed problem of retrieving a set of source signals that have been observed through a mixing operator. Solving this problem requires prior knowledge, which is commonly incorporated by imposing regularity conditions on the source signals, or implicitly learned through supervised or unsupervised methods from existing data. While data-driven methods have shown great promise in source separation, they often require large amounts of data, which rarely exists in planetary space missions. To address this challenge, we propose an unsupervised source separation scheme for domains with limited data access that involves solving an optimization problem in the wavelet scattering covariance representation space-an interpretable, low-dimensional representation of stationary processes. We present a real-data example in which we remove transient, thermally-induced microtilts-known as glitches-from data recorded by a seismometer during NASA's InSight mission on Mars. Thanks to the wavelet scattering covariances' ability to capture non-Gaussian properties of stochastic processes, we are able to separate glitches using only a few glitch-free data snippets.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2301.11981")
get_code_for_paper("2301.11981")
have("2301.11981")
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