DOAJ Open Access 2024

An experiment on an automated literature survey of data-driven speech enhancement methods

dos Santos Arthur Pereira Jayr Nogueira Rodrigo Masiero Bruno Tavallaey Shiva Sander +1 lainnya

Abstrak

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.

Topik & Kata Kunci

Acoustics in engineering. Acoustical engineering Acoustics. Sound

Penulis (6)

dos Santos Arthur

Pereira Jayr

Nogueira Rodrigo

Masiero Bruno

Tavallaey Shiva Sander

Zea Elias

Format Sitasi

APA MLA BibTeX

Arthur, d.S., Jayr, P., Rodrigo, N., Bruno, M., Sander, T.S., Elias, Z. (2024). An experiment on an automated literature survey of data-driven speech enhancement methods. https://doi.org/10.1051/aacus/2023067

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Informasi Jurnal

Tahun Terbit: 2024
Sumber Database: DOAJ
DOI: 10.1051/aacus/2023067
Akses: Open Access ✓