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Found 7 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.
Understanding EEG signals for subject-wise Definition of Armoni Activities
The learning is performed in a closed-loop by using feedback in the form of change in affective state.
Precise Single-stage Detector
There are still two problems in SDD causing some inaccurate results: (1) In the process of feature extraction, with the layer-by-layer acquisition of semantic information, local information is gradually lost, resulting into less representative feature maps; (2) During the Non-Maximum Suppression (NMS) algorithm due to inconsistency in classification and regression tasks, the classification confidence and predicted detection position cannot accurately indicate the position of the prediction boxes.
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.
SQL and NoSQL Databases Software architectures performance analysis and assessments -- A Systematic Literature review
Also, the mixed model is followed by each category of NoSQL Databases.
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).
