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  "document": {
    "abbrevTitle": "SMPTE Mot. Imag. J",
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      "source": "parsed",
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      "version": "smpte-idams-publication@v1"
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    "abstract": "In this article, we focus on the machine-learning approach developed for automatic audio source recognition and mixing for the U.K. Government Department of Culture Media and Sport (DCMS) funded collaborative project called 5G Edge-XR. Leveraging graphics processing unit (GPU) acceleration, we deployed innovative algorithms in the cloud so that content can be automatically mixed on-the-fly for a personalized, immersive, and interactive experience for audiences. We describe the algorithms involved, the system architecture, how it has been implemented for immersive live boxing, and also how we are using it to enhance a live in-stadium experience.",
    "abstract$meta": {
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    "authors": [
      {
        "bio": "Rob G. Oldfield is an engineer, a researcher, and an entrepreneur, in Manchester, U.K. He is the co-founder of Salsa Sound Ltd., Manchester, which developed a patented AI engine that automates and enhances audio mixing for live sports broadcasts. Salsa’s technology improves mix quality, consistency, and production efficiency and enables the production of content that helps viewers fully connect with the action on the field and the crowd in the stands. He has been working in the audio/broadcast industry for more than 15 years. He received a PhD degree in object-based and immersive audio from the University of Salford, Salford, U.K., in 2013.",
        "name": "Rob G. Oldfield"
      },
      {
        "bio": "Max S. S. Walley is the lead software developer and researcher with, Salsa Sound Ltd., Manchester, U.K. He received first-class honors in broadcast audio and music technology from the University of the West of England, Bristol, U.K. His research interests include audio analysis, AI in large-scale audio applications, cloud computing, hardware emulation, and XR game development.",
        "name": "Max S. S. Walley"
      },
      {
        "bio": "Ben G. Shirley is the co-founder and director of Salsa Sound Ltd., Manchester, U.K., which develops live sports audio broadcast solutions. He is a senior lecturer in audio technology with the University of Salford, Salford, U.K., where he carries out research in object-based audio personalization and broadcast accessibility. He received an MSc degree from Keele University, Keele, U.K., in 2000, and a PhD degree from the University of Salford in 2013.",
        "name": "Ben G. Shirley"
      },
      {
        "bio": "Doug L. Williams has worked with BT Applied Research, Ipswich, U.K., for more than 30 years. For the past 20 years, he has focused on developing and running collaborative projects that focus on enabling media experiences that use the affordances of IP delivery. He was privileged to coordinate the interdisciplinary teams working on the U.K. Government Department of Culture Media and Sport (DCMS) funded 5G Edge-XR project, the results of which are reported in this article. 5G Edge-XR explored how GPU-based edge compute and 5G technologies can enable immersive experiences that address sports (of immediate relevance to BT) and applications across a range of other industries. Prior to this, he led three major H2020 projects, all looking at new experiences enabled by emerging technology capabilities. These projects explored reconfigurable narrative, multicamera viewpoint video conferencing, the mixing of games with communication, and personalized TV using object-based media.",
        "name": "Doug L. Williams"
      }
    ],
    "authors$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
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      "version": "smpte-idams-publication@v1"
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    "contentType": "orig-research",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "copyright": {
      "holder": "SMPTE",
      "holder$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
        "version": "smpte-idams-publication@v1"
      },
      "year": "2022",
      "year$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
        "source": "parsed",
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        "version": "smpte-idams-publication@v1"
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    "copyright$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
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      "version": "smpte-idams-publication@v1"
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    "docId": "10.5594-JMI.2022.3184849",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
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      "version": "smpte-idams-publication@v1"
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    "docLabel": "SMPTE Motion Imaging Journal ( Volume: 131, Issue: 7, August 2022)",
    "docLabel$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "docTitle": "Cloud-Based AI for Automatic Audio Production for Personalized Immersive XR Experiences",
    "docTitle$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
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    "docType": "Journal Article",
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    "doi": "10.5594/JMI.2022.3184849",
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    "href": "https://doi.org/10.5594/JMI.2022.3184849",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
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    "issn": {
      "electronic": "2160-2492",
      "electronic$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
        "source": "parsed",
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        "version": "smpte-idams-publication@v1"
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      "print": "1545-0279",
      "print$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
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    "issn$meta": {
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    "journalAcronym": "MIJR",
    "journalAcronym$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
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      "version": "smpte-idams-publication@v1"
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    "journalTitle": "SMPTE Motion Imaging Journal",
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    "keywords": [
      "5G",
      "Artificial Intelligence",
      "Audio",
      "Augmented Reality",
      "Automated Production",
      "Broadcast",
      "Extended Reality",
      "Immersive Audio",
      "Machine Learning",
      "Object-based Audio",
      "Personalization"
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    "keywords$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9851537/09851546.xml) via idamsIngest.js",
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    "number": "7",
    "number$meta": {
      "confidence": "high",
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    "pages": "6–16",
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    "publicationDate": "2022-08-01",
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    "publisher": "SMPTE",
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      "city": "White Plains, NY",
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        "confidence": "high",
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      "country": "USA",
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        "note": "References parsed from the published PDF (Journal Article Repository/2022/7261654/9851537/09851546.pdf). SMPTE's library delivery has no reference file or full text for this paper (src/main/reports/smpte-upstream/referenceFilesTruthTable.md). Cross-check: Crossref lists 12 reference(s) for this DOI; 12 parsed from the PDF.",
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