Learning representations of sound using trainable COPE feature extractors

@article{Strisciuglio2019LearningRO,
  title={Learning representations of sound using trainable COPE feature extractors},
  author={Nicola Strisciuglio and Mario Vento and Nicolai Petkov},
  journal={Pattern Recognition},
  year={2019},
  volume={92},
  pages={25-36}
}
Abstract Sound analysis research has mainly been focused on speech and music processing. The deployed methodologies are not suitable for analysis of sounds with varying background noise, in many cases with very low signal-to-noise ratio (SNR). In this paper, we present a method for the detection of patterns of interest in audio signals. We propose novel trainable feature extractors, which we call COPE (Combination of Peaks of Energy). The structure of a COPE feature extractor is determined… CONTINUE READING
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