Abstract / truncated to 115 words (read the full abstract)

Detecting anomalous sounds is a difficult task: First, audio data is very high-dimensional and anomalous signal components are relatively subtle in relation to the entire acoustic scene. Furthermore, normal and anomalous audio signals are not inherently different because defining these terms strongly depends on the application. Third, usually only normal data is available for training a system because anomalies are rare, diverse, costly to produce and in many cases unknown in advance. Such a setting is called semi-supervised anomaly detection. In domain-shifted conditions or when only very limited training data is available, all of these problems are even more severe. The goal of this thesis is to overcome these difficulties by teaching an embedding model ... toggle 7 keywords

anomaly detection – representation learning – sound event detection – keyword spotting – domain generalization – machine listening – machine condition monitoring

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