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Found 3 papers, 1 papers with code
AudRandAug: Random Image Augmentations for Audio Classification
To address this gap, we introduce AudRandAug, an adaptation of RandAug for audio data.
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).
MILP for the Multi-objective VM Reassignment Problem
Machine Reassignment is a challenging problem for constraint programming (CP) and mixed-integer linear programming (MILP) approaches, especially given the size of data centres.
