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Audio Classification with Machine Learning

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Audio Classification with Machine Learning
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Learn how to classify sound using Convolutional Neural Networks
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118
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CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
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Abstract
Sound is a rich source of information about the world around us. Modern deep learning approaches can give human-like performance on a range of sound classifiction tasks. This makes it possible to build systems that use sound to for example: understand speech, to analyze music, to assist in medical diagnostics, detect quality problems in manufacturing, and to study the behavior of animals. This talk will show you how to build practical machine learning models that can classify sound. We will convert sound into spectrograms, a visual representation of sound over time, and apply machine learning models similar to what is used to for image classification. The focus will be on Convolutional Neural Networks, which have been shown to work very well for this task. The Keras and Tensorflow deep learning frameworks will be used. Some tricks for getting usable results with small amounts of data will be covered, including transfer learning, audio embeddings and data augmentation. A basic understanding of machine learning is recommended. Familiarity with digital sound is a bonus. Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/
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