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KWDOA: Adapted dataset for detection of the direction of arrival of the keyword

8 pagesPublished: January 24, 2024

Abstract

This paper describes a simulated audio dataset of spoken words which accommodate microphone array design for training and evaluating keywords spotting systems. With this dataset you could train a neural network for the detection direction of the speaker. Which is an advanced version of the original, with added noises during a speech in random locations and different rooms with different reverb. Hence it should be closer to real-world long-range applications. This task could be a new challenge for the direction of arrival activated by keyword spotting systems. Let’s call this task KWDOA. This dataset could serve as the intro level for microphone array designs.

Keyphrases: AI, direction of arrival, keyword, KWDOA, speech

In: Krishna Kambhampaty, Gongzhu Hu and Indranil Roy (editors). Proceedings of 36th International Conference on Computer Applications in Industry and Engineering, vol 97, pages 30--37

Links:
BibTeX entry
@inproceedings{CAINE2023:KWDOA_Adapted_dataset_for,
  author    = {David Bene\textbackslash{}v\{s\} and Lubo\textbackslash{}v\{s\} \textbackslash{}v\{S\}m\textbackslash{}'idl},
  title     = {KWDOA: Adapted dataset for detection of the direction of arrival of the keyword},
  booktitle = {Proceedings of 36th International Conference on Computer Applications in Industry and Engineering},
  editor    = {Krishna Kambhampaty and Gongzhu Hu and Indranil Roy},
  series    = {EPiC Series in Computing},
  volume    = {97},
  pages     = {30--37},
  year      = {2024},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/vsB3p},
  doi       = {10.29007/5k86}}
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