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Found 6 papers, 2 papers with code
AudRandAug: Random Image Augmentations for Audio Classification
To address this gap, we introduce AudRandAug, an adaptation of RandAug for audio data.
Image Data Augmentation Approaches: A Comprehensive Survey and Future directions
To cope with this problem, various techniques have been proposed such as dropout, normalization and advanced data augmentation.
Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets
To evaluate the generalization of the proposed method, we use four medical datasets and compare its performance with state-of-the-art methods for both classification and segmentation tasks.
Investigating Multi-Feature Selection and Ensembling for Audio Classification
To investigate this role, we conduct an extensive evaluation of the performance of several cutting-edge DL models (i. e., Convolutional Neural Network, EfficientNet, MobileNet, Supper Vector Machine and Multi-Perceptron) with various state-of-the-art audio features (i. e., Mel Spectrogram, Mel Frequency Cepstral Coefficients, and Zero Crossing Rate) either independently or as a combination (i. e., through ensembling) on three different datasets (i. e., Free Spoken Digits Dataset, Audio Urdu Digits Dataset, and Audio Gujarati Digits Dataset).
Hierarchical Aggregation Approach for Distributed clustering of spatial datasets
In this paper, we present a new approach of distributed clustering for spatial datasets, based on an innovative and efficient aggregation technique.
Efficient Large Scale Clustering based on Data Partitioning
However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality data, heterogeneity, and high complexity of some algorithms.
