  Segmenting audio into homogeneous sections such as music and speech helps us
understand the content of audio. It is useful as a pre-processing step to
index, store, and modify audio recordings, radio broadcasts and TV programmes.
Deep learning models for segmentation are generally trained on copyrighted
material, which cannot be shared. Annotating these datasets is time-consuming
and expensive and therefore, it significantly slows down research progress. In
this study, we present a novel procedure that artificially synthesises data
that resembles radio signals. We replicate the workflow of a radio DJ in mixing
audio and investigate parameters like fade curves and audio ducking. We trained
a Convolutional Recurrent Neural Network (CRNN) on this synthesised data and
outperformed state-of-the-art algorithms for music-speech detection. This paper
demonstrates the data synthesis procedure as a highly effective technique to
generate large datasets to train deep neural networks for audio segmentation.
