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.
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页数：8
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