Bringing Order to Chaos: A Non-Sequential Approach for Browsing Large Sets of Found Audio Data

Abstract

We present a novel and general approach for fast and efficient non-sequential browsing of sound in large archives that we know little or nothing about, e.g. so called found data – data not recorded with the specific purpose to be analysed or used as training data. Our main motivation is to address some of the problems speech and speech technology researchers see when they try to capitalise on the huge quantities of speech data that reside in public archives. Our method is a combination of audio browsing through massively multi-object sound environments and a well-known unsupervised dimensionality reduction algorithm (SOM). We test the process chain on four data sets of different nature (speech, speech and music, farm animals, and farm animals mixed with farm sounds). The methods are shown to combine well, resulting in rapid and readily interpretable observations. Finally, our initial results are demonstrated in prototype software which is freely available.

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