google radio automation software free download

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.

Radio is evolving in a changing digital media ecosystem.

Google radio automation software free download