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Audio Classification with Machine Learning

Formale Metadaten

Titel
Audio Classification with Machine Learning
Untertitel
Learn how to classify sound using Convolutional Neural Networks
Serientitel
Anzahl der Teile
118
Autor
Lizenz
CC-Namensnennung - keine kommerzielle Nutzung - Weitergabe unter gleichen Bedingungen 3.0 Unported:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben
Identifikatoren
Herausgeber
Erscheinungsjahr2019
SpracheEnglisch

Inhaltliche Metadaten

Fachgebiet
Genre
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/
Schlagwörter