preprints.org > engineering > electrical and electronic engineering > doi: 10.20944/preprints201807.0185.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 10 July 2018 / Approved: 10 July 2018 / Online: 10 July 2018 (16:05:15 CEST)

In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose factorising the final task of audio transcription into multiple intermediate tasks in order to improve the training performance when dealing with this kind of low-resource datasets. We evaluate three data-efficient approaches of training a stacked convolutional and recurrent neural network for the intermediate tasks. Our results show that different methods of training have different advantages and disadvantages.

deep learning; multi-task learning; audio event detection; audio tagging; weak learning; low-resource data

Engineering, Electrical and Electronic Engineering

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