We lifted 4 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| jinbo-hu/l3das22-task2 | pwc_unofficial | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| collate_fn | Ran | jinbo-hu/l3das22-task2/code/methods/data.py code served (permissive licence) · get_code("9bb5713d5f97937b") |
| collate_fn | Ran | jinbo-hu/l3das22-task2/code/methods/ein_seld/data.py code served (permissive licence) · get_code("49cf46e38c5118df") |
| collate_fn_test | Ran | jinbo-hu/l3das22-task2/code/methods/ein_seld/data.py code served (permissive licence) · get_code("afbbbc08bab384d5") |
| extract_normalized_eigenvector | Ran | jinbo-hu/l3das22-task2/code/learning/salsa_feature_extraction.py code served (permissive licence) · get_code("b7122e199b1d23b6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Polyphonic sound event localization and detection (SELD), which jointly performs sound event detection (SED) and direction-of-arrival (DoA) estimation, detects the type and occurrence time of sound events as well as their corresponding DoA angles simultaneously. We study the SELD task from a multi-task learning perspective. Two open problems are addressed in this paper. Firstly, to detect overlapping sound events of the same type but with different DoAs, we propose to use a trackwise output format and solve the accompanying track permutation problem with permutation-invariant training. Multi-head self-attention is further used to separate tracks. Secondly, a previous finding is that, by using hard parameter-sharing, SELD suffers from a performance loss compared with learning the subtasks separately. This is solved by a soft parameter-sharing scheme. We term the proposed method as Event Independent Network V2 (EINV2), which is an improved version of our previously-proposed method and an end-to-end network for SELD. We show that our proposed EINV2 for joint SED and DoA estimation outperforms previous methods by a large margin, and has comparable performance to state-of-the-art ensemble models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.13092")
get_code_for_paper("2010.13092")
have("2010.13092")
Connect an agent — have() is free.