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

被引：0

作者：

dos Santos, Arthur ^{[1
]}

Pereira, Jayr ^{[2
]}

Nogueira, Rodrigo ^{[2
]}

Masiero, Bruno ^{[1
]}

Tavallaey, Shiva Sander ^{[3
,4
]}

Zea, Elias ^{[4
]}

机构：

[1] Univ Estadual Campinas, Sch Elect & Comp Engn, Commun Acoust Lab, BR-13083970 Campinas, SP, Brazil

[2] NeuralMind, BR-13083898 Campinas, SP, Brazil

[3] ABB Corp Res, SE-72226 Vasteras, Sweden

[4] KTH Royal Inst Technol, Dept Engn Mech, Marcus Wallenberg Lab Sound & Vibrat Res, SE-10044 Stockholm, Sweden

来源：

ACTA ACUSTICA | 2024年 / 8卷

基金：

巴西圣保罗研究基金会;

关键词：

Speech enhancement methods; Data-driven acoustics; Literature survey; Natural language processing; Large language models; INDUCED HEARING-LOSS; SOURCE LOCALIZATION; ART;

D O I：

10.1051/aacus/2023067

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页数：8

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