| Autor: |
dos Santos Arthur, Pereira Jayr, Nogueira Rodrigo, Masiero Bruno, Tavallaey Shiva Sander, Zea Elias |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Acta Acustica, Vol 8, p 2 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2681-4617 |
| DOI: |
10.1051/aacus/2023067 |
| Popis: |
The increasing number of scientific publications in acoustics, in general, presents difficulties in conducting traditional literature surveys. This work explores the use of a generative pre-trained transformer (GPT) model to automate a literature survey of 117 articles on data-driven speech enhancement methods. The main objective is to evaluate the capabilities and limitations of the model in providing accurate responses to specific queries about the papers selected from a reference human-based survey. While we see great potential to automate literature surveys in acoustics, improvements are needed to address technical questions more clearly and accurately. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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