Journal ArticleDOI
A novel acoustic scene classification model using the late fusion of convolutional neural networks and different ensemble classifiers
TLDR
An enhanced CNN classification model using the late fusion between CNNs and ensemble classifiers to predict different classes of acoustic scenes has robust applicability for future ASC problems.About:
This article is published in Applied Acoustics.The article was published on 2021-04-01. It has received 13 citations till now. The article focuses on the topics: Convolutional neural network & Classifier (UML).read more
Citations
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Journal ArticleDOI
Deep mutual attention network for acoustic scene classification
TL;DR: Wang et al. as discussed by the authors designed a deep mutual attention network based on the principle of receptive field regularization and the mutual attention mechanism, which can realize the joint learning and complementary enhancement of multiple time-frequency features end-to-end, which improves features' learning efficiency and discriminative ability.
Journal ArticleDOI
An enhanced artificial neural network model using the Harris Hawks optimiser for predicting food liking in the presence of background noise
TL;DR: In this article, the Harris Hawks optimiser (HHO) was used to predict the relative liking of food ratings with higher performance (R2 = 0.70, RMSE= 0.8), as compared to traditional ANNs using feedforward neural networks (FFNNs) and statistical mixed models, which were used to find the threshold level that gives maximum relative food liking ratings for different types of noise.
Journal ArticleDOI
Penalties applied to wind farm noise: Current allowable limits, influencing factors, and their development
TL;DR: In this paper, the authors present a review of current allowable limits and penalties for wind farm noise and its characteristics, and discuss differences between the limits and the penalties and potential areas for improvements.
Journal ArticleDOI
Artificial neural network modeling of MHD slip-flow over a permeable stretching surface
Journal ArticleDOI
Binaural Acoustic Scene Classification Using Wavelet Scattering, Parallel Ensemble Classifiers and Nonlinear Fusion
Vahid Hajihashemi,Abdorreza Alavi Gharahbagh,Pedro Miguel Cruz,Marta C. Ferreira,José Machado,João Manuel R. S. Tavares +5 more
TL;DR: This research presents a hybrid method that includes a novel mathematical fusion step which aims to tackle the challenges of ASC accuracy and adaptability of current state-of-the-art models.
References
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Journal ArticleDOI
Taking the Human Out of the Loop: A Review of Bayesian Optimization
TL;DR: This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.
Posted Content
mixup: Beyond Empirical Risk Minimization
TL;DR: Mixup as discussed by the authors trains a neural network on convex combinations of pairs of examples and their labels, and regularizes the neural network to favor simple linear behavior in between training examples, which improves the generalization of state-of-the-art neural network architectures.
Proceedings ArticleDOI
Early versus late fusion in semantic video analysis
TL;DR: It is shown by experiment on 184 hours of broadcast video data and for 20 semantic concepts, that late fusion tends to give slightly better performance for most concepts, however, for those concepts where early fusion performs better the difference is more significant.
Journal ArticleDOI
Machine learning in acoustics: Theory and applications
Michael J. Bianco,Peter Gerstoft,James Traer,Emma Ozanich,Marie A. Roch,Sharon Gannot,Charles-Alban Deledalle +6 more
TL;DR: This work surveys the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics, and highlights ML developments in four acoustICS research areas: source localization in speech processing, source localized in ocean acoustic, bioacoustics and environmental sounds in everyday scenes.
Journal ArticleDOI
Machine learning in acoustics: theory and applications
Michael J. Bianco,Peter Gerstoft,James Traer,Emma Ozanich,Marie A. Roch,Sharon Gannot,Charles-Alban Deledalle +6 more
TL;DR: In this paper, the authors survey the recent advances and transformative potential of machine learning (ML) including deep learning, in the field of acoustics and highlight ML developments in four acoustICS research areas: source localization in speech processing, source localization from ocean acoustic, bioacoustics, and environmental sounds in everyday scenes.
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