Knowledge discovery and visualisation framework using machine learning for music information retrieval from broadcast radio data

Highlights：

• We identify a lack of software frameworks for broadcast radio knowledge discovery.

• A novel framework for this is proposed, using MIR and data mining technologies.

• We compare radio stations via a novel SOM visualisation and similarity metric.

• A method for building high-quality music datasets is proposed and demonstrated.

• The use of the framework is analysed in multiple research and industry contexts.