We have machines that can listen to and process human speech in a variety of languages, but dealing with unstructured sounds in our environment is a much greater challenge. The team at Audio Analytic are working to impart a sense of hearing to our myriad devices with their sound recognition technology. In this episode Dr. Chris Mitchell and Dr. Thomas le Cornu describe the challenges that they are faced with in the collection and labelling of high quality data to make this possible, including the lack of a publicly available collection of audio samples to work from, the need for custom metadata throughout the processing pipeline, and the need for customized data processing tools for working with sound data. This was a great conversation about the complexities of working in a niche domain of data analysis and how to build a pipeline of high quality data from collection to analysis.
Tobias Macey
Posted on • Originally published at dataengineeringpodcast.com
Data Collection And Management For Teaching Machines To Hear At Audio Analytic - Episode 139
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