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Found 4 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.
Data Dimension Reduction makes ML Algorithms efficient
Similarly, in AE based DDR, we compare unsupervised learning algorithm accuracy and time before and after AE representation learning.
Advanced Audio Aid for Blind People
One of the most important senses in human life is vision, without it life is totally filled with darkness.
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).
