| Paper ID | AUD-23.3 |
| Paper Title |
ARTIFICIALLY SYNTHESISING DATA FOR AUDIO CLASSIFICATION AND SEGMENTATION TO IMPROVE SPEECH AND MUSIC DETECTION IN RADIO BROADCAST |
| Authors |
Satvik Venkatesh, David Moffat, Alexis Kirke, University of Plymouth, United Kingdom; Gözel Shakeri, Stephen Brewster, University of Glasgow, United Kingdom; Jörg Fachner, Helen Odell-Miller, Alex Street, Anglia Ruskin University, United Kingdom; Nicolas Farina, Brighton and Sussex Medical School, United Kingdom; Sube Banerjee, Eduardo Reck Miranda, University of Plymouth, United Kingdom |
| Session | AUD-23: Detection and Classification of Acoustic Scenes and Events 4: Datasets and metrics |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-CLAS] Detection and Classification of Acoustic Scenes and Events |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
