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Table 11 Acoustic event detection

From: An analytical study of information extraction from unstructured and multidimensional big data

  Purpose Technique Dataset Results Limitations/benefits
[62] Modeling data with exemplars
To explicitly model the background events
Exemplar-based method with NMF Office Live recordings from 1 to 3 min and office synthetic with bg noise With time wrapping, Fscore improved from 50.2 to 65.2% in office live dataset whereas in office synthetic dataset, results were not promising Proposed solution suffers from data scarcity, and overfitting
[65] To overcome the overfitting limitation
To improve the performance for large scale input
CNN to train AED end to end + data augmentation method to prevent overfitting Acoustic event classification database Achieved 16% improvement as compared to Bag of Audio Words (BoAW) and classical CNN Results presented with and without data augmentation proved that augmentation improves the performance
[63] To explore the impact of feature extraction in AED
To explore the effectiveness of deep learning approaches
Multiple single resolution recognizers + selection of optimal set of events + merging or removing repeated labels CHIL2007 CNN performed better with combination scheme of multi-resolution approach DNN has the ability to model high dimensional data
[64] To improve the detection accuracy by extracting context information Context recognition phase: UBM to capture unknown events and sound event detection stage Audio database consisting 103 recordings of 1133 min duration Knowledge of context as context dependent event prior can be used to improve the accuracy Context dependent event selection and accurate sound event modeling are two important factors for the improvement in AED
[66] To improve the efficiency of acoustic scene classification and acoustic event detection Gated recurrent neural networks (GRNN) + linear discriminant analysis (LDA) DCASE2016 task 1 Achieved overall accuracy of 79.1% on DCASE2016 challenge. Relative improvement of 19.8% as compared to GMM LDA minimizes inner class variance but not efficient for high dimensional data