We lifted 9 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| rainerkelz/framewise_2016 | pwc_unofficial | 0 of 8 |
| jsleep/wav2mid | pwc_unofficial | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| collect_all_filenames | Not yet run | rainerkelz/framewise_2016/create-configuration-II-splits.py code served (permissive licence) · get_code("f01607eb114582a8") |
| collect_all_filenames | Not yet run | rainerkelz/framewise_2016/create-non-overlapping-splits.py code served (permissive licence) · get_code("3615d51cf7c01654") |
| collect_all_piece_ids | Not yet run | rainerkelz/framewise_2016/create-non-overlapping-splits.py code served (permissive licence) · get_code("2848e7534e9ca13d") |
| desugar | Not yet run | rainerkelz/framewise_2016/create-non-overlapping-splits.py code served (permissive licence) · get_code("d25e53ff22299861") |
| filenames_from_splitfile | Not yet run | rainerkelz/framewise_2016/utils.py code served (permissive licence) · get_code("88c7e4a504933446") |
| find_learnrate | Not yet run | rainerkelz/framewise_2016/utils.py code served (permissive licence) · get_code("cd2adfac5c0d9625") |
| load_config | Not yet run | jsleep/wav2mid/config.py code served (permissive licence) · get_code("8b871a0f506783c9") |
| test | Not yet run | rainerkelz/framewise_2016/check_scheduler.py code served (permissive licence) · get_code("b91251c7af139f06") |
| train_one_epoch | Not yet run | rainerkelz/framewise_2016/utils.py code served (permissive licence) · get_code("d6c1b83d351192d4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In an attempt at exploring the limitations of simple approaches to the task of piano transcription (as usually defined in MIR), we conduct an in-depth analysis of neural network-based framewise transcription. We systematically compare different popular input representations for transcription systems to determine the ones most suitable for use with neural networks. Exploiting recent advances in training techniques and new regularizers, and taking into account hyper-parameter tuning, we show that it is possible, by simple bottom-up frame-wise processing, to obtain a piano transcriber that outperforms the current published state of the art on the publicly available MAPS dataset -- without any complex post-processing steps. Thus, we propose this simple approach as a new baseline for this dataset, for future transcription research to build on and improve.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1612.05153")
get_code_for_paper("1612.05153")
have("1612.05153")
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