Application of Audio Fingerprinting Techniques for Real-Time Scalable Speech Retrieval and Speech Clusterization
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| Název: | Application of Audio Fingerprinting Techniques for Real-Time Scalable Speech Retrieval and Speech Clusterization |
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| Autoři: | Altwlkany, Kemal, Delalić, Sead, Alihodžić, Adis, Selmanović, Elmedin, Hasić, Damir |
| Zdroj: | 2024 47th MIPRO ICT and Electronics Convention (MIPRO). :91-96 |
| Publication Status: | Preprint |
| Informace o vydavateli: | IEEE, 2024. |
| Rok vydání: | 2024 |
| Témata: | FOS: Computer and information sciences, H.3, Sound (cs.SD), Sound, Audio and Speech Processing (eess.AS), Information Retrieval, FOS: Electrical engineering, electronic engineering, information engineering, Audio and Speech Processing, Information Retrieval (cs.IR) |
| Popis: | Audio fingerprinting techniques have seen great advances in recent years, enabling accurate and fast audio retrieval even in conditions when the queried audio sample has been highly deteriorated or recorded in noisy conditions. Expectedly, most of the existing work is centered around music, with popular music identification services such as Apple's Shazam or Google's Now Playing designed for individual audio recognition on mobile devices. However, the spectral content of speech differs from that of music, necessitating modifications to current audio fingerprinting approaches. This paper offers fresh insights into adapting existing techniques to address the specialized challenge of speech retrieval in telecommunications and cloud communications platforms. The focus is on achieving rapid and accurate audio retrieval in batch processing instead of facilitating single requests, typically on a centralized server. Moreover, the paper demonstrates how this approach can be utilized to support audio clustering based on speech transcripts without undergoing actual speech-to-text conversion. This optimization enables significantly faster processing without the need for GPU computing, a requirement for real-time operation that is typically associated with state-of-the-art speech-to-text tools. Proceedings of the International Convention MIPRO |
| Druh dokumentu: | Article |
| DOI: | 10.1109/mipro60963.2024.10569239 |
| DOI: | 10.48550/arxiv.2410.21876 |
| Přístupová URL adresa: | http://arxiv.org/abs/2410.21876 |
| Rights: | STM Policy #29 CC BY |
| Přístupové číslo: | edsair.doi.dedup.....b73c6252c9839e761655384d87e334aa |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | Audio fingerprinting techniques have seen great advances in recent years, enabling accurate and fast audio retrieval even in conditions when the queried audio sample has been highly deteriorated or recorded in noisy conditions. Expectedly, most of the existing work is centered around music, with popular music identification services such as Apple's Shazam or Google's Now Playing designed for individual audio recognition on mobile devices. However, the spectral content of speech differs from that of music, necessitating modifications to current audio fingerprinting approaches. This paper offers fresh insights into adapting existing techniques to address the specialized challenge of speech retrieval in telecommunications and cloud communications platforms. The focus is on achieving rapid and accurate audio retrieval in batch processing instead of facilitating single requests, typically on a centralized server. Moreover, the paper demonstrates how this approach can be utilized to support audio clustering based on speech transcripts without undergoing actual speech-to-text conversion. This optimization enables significantly faster processing without the need for GPU computing, a requirement for real-time operation that is typically associated with state-of-the-art speech-to-text tools.<br />Proceedings of the International Convention MIPRO |
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| DOI: | 10.1109/mipro60963.2024.10569239 |