A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015 Erik Marchi ; Fabio Vesperini ; Florian Eyben ; Stefano Squartini ; Björn Schuller

Acoustic novelty detection aims at identifying abnormal/novel acoustic signals which differ from the reference/normal data that the system was trained with. In this paper we present a novel unsupervised approach based on a denoising autoencoder... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Acoustic Novelty Detection A3Lab PASCAL CHiME BLSTM-DAE F1 93.4 # 3

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