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Computational Intelligence and Neuroscience
Volume 2017, Article ID 4694860, 14 pages
Research Article

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

1Machine Intelligence & Signal Processing Group, Technische Universität München, Munich, Germany
2audEERING GmbH, Gilching, Germany
3Chair of Complex & Intelligent Systems, University of Passau, Passau, Germany
4A3LAB, Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
5Department of Computing, Imperial College London, London, UK

Correspondence should be addressed to Erik Marchi; ed.mut@ihcram.kire

Received 12 July 2016; Accepted 25 September 2016; Published 15 January 2017

Academic Editor: Stefan Haufe

Copyright © 2017 Erik Marchi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average -measure over the three databases.