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 |
|---|---|---|
| aisaka0v0/clapsep | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| load_checkpoint | Ran | aisaka0v0/clapsep/helpers/utils.py code served (permissive licence) · get_code("8e7fce2bb7365742") |
| loss_fn | Ran | aisaka0v0/clapsep/model/CLAPSep.py code served (permissive licence) · get_code("9a053c56d1d332e3") |
| model_size | Ran | aisaka0v0/clapsep/helpers/utils.py code served (permissive licence) · get_code("c481103177a670c9") |
| run_time | Ran | aisaka0v0/clapsep/helpers/utils.py code served (permissive licence) · get_code("596dd36d02e6ffdb") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Universal sound separation (USS) aims to extract arbitrary types of sounds from real-world recordings. This can be achieved by language-queried target sound extraction (TSE), which typically consists of two components: a query network that converts user queries into conditional embeddings, and a separation network that extracts the target sound accordingly. Existing methods commonly train models from scratch. As a consequence, substantial data and computational resources are required to make the randomly initialized model comprehend sound events and perform separation accordingly. In this paper, we propose to integrate pre-trained models into TSE models to address the above issue. To be specific, we tailor and adapt the powerful contrastive language-audio pre-trained model (CLAP) for USS, denoted as CLAPSep. CLAPSep also accepts flexible user inputs, taking both positive and negative user prompts of uni- and/or multi-modalities for target sound extraction. These key features of CLAPSep can not only enhance the extraction performance but also improve the versatility of its application. We provide extensive experiments on 5 diverse datasets to demonstrate the superior performance and zero- and few-shot generalizability of our proposed CLAPSep with fast training convergence, surpassing previous methods by a significant margin. Full codes and some audio examples are released for reproduction and evaluation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.17455")
get_code_for_paper("2402.17455")
have("2402.17455")
Connect an agent — have() is free.