  Identifying acoustic events from a continuously streaming audio source is of
interest for many applications including environmental monitoring for basic
research. In this scenario neither different event classes are known nor what
distinguishes one class from another. Therefore, an unsupervised feature
learning method for exploration of audio data is presented in this paper. It
incorporates the two following novel contributions: First, an audio frame
predictor based on a Convolutional LSTM autoencoder is demonstrated, which is
used for unsupervised feature extraction. Second, a training method for
autoencoders is presented, which leads to distinct features by amplifying event
similarities. In comparison to standard approaches, the features extracted from
the audio frame predictor trained with the novel approach show 13 % better
results when used with a classifier and 36 % better results when used for
clustering.
