An experiment on an automated literature survey of data-driven speech enhancement methods
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Abstract (arXiv)
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 116 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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 29 references that are in the map. First posted on arXiv 10 Oct 2023.
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5 of 29 references.
References · 5
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
- 2022News Summarization and Evaluation in the Era of GPT-3cited by 324
- 2021A Survey of Sound Source Localization with Deep Learning Methodscited by 45
- 2019Machine learning in acoustics: theory and applicationscited by 36
- 2019The LOCATA Challenge: Acoustic Source Localization and Trackingcited by 16
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