SoccerNet-Echoes: A Soccer Game Audio Commentary Dataset
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914001220272128 |
|---|---|
| author | Gautam, Sushant Sarkhoosh, Mehdi Houshmand Held, Jan Midoglu, Cise Cioppa, Anthony Giancola, Silvio Thambawita, Vajira Riegler, Michael A. Halvorsen, Pål Shah, Mubarak |
| author_facet | Gautam, Sushant Sarkhoosh, Mehdi Houshmand Held, Jan Midoglu, Cise Cioppa, Anthony Giancola, Silvio Thambawita, Vajira Riegler, Michael A. Halvorsen, Pål Shah, Mubarak |
| contents | The application of Automatic Speech Recognition (ASR) technology in soccer offers numerous opportunities for sports analytics. Specifically, extracting audio commentaries with ASR provides valuable insights into the events of the game, and opens the door to several downstream applications such as automatic highlight generation. This paper presents SoccerNet-Echoes, an augmentation of the SoccerNet dataset with automatically generated transcriptions of audio commentaries from soccer game broadcasts, enhancing video content with rich layers of textual information derived from the game audio using ASR. These textual commentaries, generated using the Whisper model and translated with Google Translate, extend the usefulness of the SoccerNet dataset in diverse applications such as enhanced action spotting, automatic caption generation, and game summarization. By incorporating textual data alongside visual and auditory content, SoccerNet-Echoes aims to serve as a comprehensive resource for the development of algorithms specialized in capturing the dynamics of soccer games. We detail the methods involved in the curation of this dataset and the integration of ASR. We also highlight the implications of a multimodal approach in sports analytics, and how the enriched dataset can support diverse applications, thus broadening the scope of research and development in the field of sports analytics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SoccerNet-Echoes: A Soccer Game Audio Commentary Dataset Gautam, Sushant Sarkhoosh, Mehdi Houshmand Held, Jan Midoglu, Cise Cioppa, Anthony Giancola, Silvio Thambawita, Vajira Riegler, Michael A. Halvorsen, Pål Shah, Mubarak Sound Information Retrieval Machine Learning Multimedia Audio and Speech Processing I.2.7; I.7 The application of Automatic Speech Recognition (ASR) technology in soccer offers numerous opportunities for sports analytics. Specifically, extracting audio commentaries with ASR provides valuable insights into the events of the game, and opens the door to several downstream applications such as automatic highlight generation. This paper presents SoccerNet-Echoes, an augmentation of the SoccerNet dataset with automatically generated transcriptions of audio commentaries from soccer game broadcasts, enhancing video content with rich layers of textual information derived from the game audio using ASR. These textual commentaries, generated using the Whisper model and translated with Google Translate, extend the usefulness of the SoccerNet dataset in diverse applications such as enhanced action spotting, automatic caption generation, and game summarization. By incorporating textual data alongside visual and auditory content, SoccerNet-Echoes aims to serve as a comprehensive resource for the development of algorithms specialized in capturing the dynamics of soccer games. We detail the methods involved in the curation of this dataset and the integration of ASR. We also highlight the implications of a multimodal approach in sports analytics, and how the enriched dataset can support diverse applications, thus broadening the scope of research and development in the field of sports analytics. |
| title | SoccerNet-Echoes: A Soccer Game Audio Commentary Dataset |
| topic | Sound Information Retrieval Machine Learning Multimedia Audio and Speech Processing I.2.7; I.7 |
| url | https://arxiv.org/abs/2405.07354 |