Speaker diarization is defined as the problem of deciding “who spoke when?”, which serves many applications in broadcasting, conferencing, and intelligent information retrieval. The results are considered encouraging regarding the applicability of the proposed methodology. 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. Several clustering algorithms are evaluated, having the d-vectors as input. The trained model is used for the extraction of fixed-size identity d-vectors. Since not all speakers are known in radio shows, a CNN-based speaker diarization method is also proposed. The model is based on a convolutional neural network (CNN) architecture. For the needs of a typical radio station, a supervised speaker classification model is trained for the recognition of 24 known speakers. The application offers typical live mixing and broadcasting functionality, while performing real-time annotation as a background process by logging user operation events. A web application for live radio production and streaming is developed. In this paper, a framework for knowledge extraction is introduced, to improve discoverability and enrichment of the provided content. Audio-on-demand has shaped the landscape of big unstructured audio data available online. The data collected including the number visitors, the source where they have come from, and the pages visited in an anonymous form.Radio is evolving in a changing digital media ecosystem. The cookie is used to store information of how visitors use a website and helps in creating an analytics report of how the website is doing. This cookie is installed by Google Analytics. Google uses this cookie to distinguish users. The cookies store information anonymously and assign a randomly generated number to identify unique visitors. The cookie is used to calculate visitor, session, campaign data and keep track of site usage for the site's analytics report. It is mandatory to procure user consent prior to running these cookies on your website. Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, other embedded contents are termed as non-necessary cookies.
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