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ARTIFICIALLY SYNTHESISING DATA FOR AUDIO CLASSIFICATION AND SEGMENTATION TO IMPROVE SPEECH AND MUSIC DETECTION IN RADIO BROADCAST

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Citation Author(s):
Satvik Venkatesh, David Moffat, Alexis Kirke, Gözel Shakeri, Stephen Brewster, Jörg Fachner, Helen Odell-Miller, Alex Street, Nicolas Farina, Sube Banerjee, Eduardo Reck Miranda
Submitted by:
Satvik Venkatesh
Last updated:
7 July 2021 - 12:22pm
Document Type:
Presentation Slides
Document Year:
2021
Event:
Presenters Name:
Satvik Venkatesh
Paper Code:
3223

Abstract 

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.

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Dataset Files

Venkatesh Slides and poster.pdf

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