{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:48:48Z","timestamp":1760237328167,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,11]],"date-time":"2020-04-11T00:00:00Z","timestamp":1586563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Radio is evolving in a changing digital media ecosystem. Audio-on-demand has shaped the landscape of big unstructured audio data available online. In this paper, a framework for knowledge extraction is introduced, to improve discoverability and enrichment of the provided content. A web application for live radio production and streaming is developed. The application offers typical live mixing and broadcasting functionality, while performing real-time annotation as a background process by logging user operation events. For the needs of a typical radio station, a supervised speaker classification model is trained for the recognition of 24 known speakers. The model is based on a convolutional neural network (CNN) architecture. Since not all speakers are known in radio shows, a CNN-based speaker diarization method is also proposed. The trained model is used for the extraction of fixed-size identity d-vectors. Several clustering algorithms are evaluated, having the d-vectors as input. The supervised speaker recognition model for 24 speakers scores an accuracy of 88.34%, while unsupervised speaker diarization scores a maximum accuracy of 87.22%, as tested on an audio file with speech segments from three unknown speakers. The results are considered encouraging regarding the applicability of the proposed methodology.<\/jats:p>","DOI":"10.3390\/info11040205","type":"journal-article","created":{"date-parts":[[2020,4,13]],"date-time":"2020-04-13T04:45:31Z","timestamp":1586753131000},"page":"205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Web Radio Automation for Audio Stream Management in the Era of Big Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Nikolaos","family":"Vryzas","sequence":"first","affiliation":[{"name":"Multidisciplinary Media &amp; Mediated Communication Research Group (M3C), Aristotle University of Thessaloniki,  541 24 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7232-8839","authenticated-orcid":false,"given":"Nikolaos","family":"Tsipas","sequence":"additional","affiliation":[{"name":"Multidisciplinary Media &amp; Mediated Communication Research Group (M3C), Aristotle University of Thessaloniki,  541 24 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7923-9361","authenticated-orcid":false,"given":"Charalampos","family":"Dimoulas","sequence":"additional","affiliation":[{"name":"Multidisciplinary Media &amp; Mediated Communication Research Group (M3C), Aristotle University of Thessaloniki,  541 24 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.specom.2012.01.004","article-title":"Investigation of broadcast-audio semantic analysis scenarios employing radio-programme-adaptive pattern classification","volume":"54","author":"Kotsakis","year":"2012","journal-title":"Speech Commun."},{"key":"ref_2","unstructured":"Kotsakis, R., Kalliris, G., and Dimoulas, C. 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