%0 Generic %1 venkatesh2021artificially %A Venkatesh, Satvik %A Moffat, David %A Kirke, Alexis %A Shakeri, Gözel %A Brewster, Stephen %A Fachner, Jörg %A Odell-Miller, Helen %A Street, Alex %A Farina, Nicolas %A Banerjee, Sube %A Miranda, Eduardo Reck %D 2021 %K audio mir plk segmentation %T Artificially Synthesising Data for Audio Classification and Segmentation to Improve Speech and Music Detection in Radio Broadcast %U http://arxiv.org/abs/2102.09959 %X 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.

@misc{venkatesh2021artificially, abstract = {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.}, added-at = {2022-02-15T12:20:45.000+0100}, author = {Venkatesh, Satvik and Moffat, David and Kirke, Alexis and Shakeri, Gözel and Brewster, Stephen and Fachner, Jörg and Odell-Miller, Helen and Street, Alex and Farina, Nicolas and Banerjee, Sube and Miranda, Eduardo Reck}, biburl = {https://www.bibsonomy.org/bibtex/26ce8fd0970ce7e183fe0eae3d54d9664/simonha94}, description = {[2102.09959] Artificially Synthesising Data for Audio Classification and Segmentation to Improve Speech and Music Detection in Radio Broadcast}, interhash = {5ecbc2c8aa27bdeb15a41a9a93fbbf57}, intrahash = {6ce8fd0970ce7e183fe0eae3d54d9664}, keywords = {audio mir plk segmentation}, note = {cite arxiv:2102.09959Comment: 5 pages, 3 figures, Accepted to ICASSP 2021}, timestamp = {2022-02-15T12:20:45.000+0100}, title = {Artificially Synthesising Data for Audio Classification and Segmentation to Improve Speech and Music Detection in Radio Broadcast}, url = {http://arxiv.org/abs/2102.09959}, year = 2021 }