Improving Audio Recognition With Randomized Area Ratio Patch Masking: A Data Augmentation Perspective

Improving Audio Recognition With Randomized Area Ratio Patch Masking: A Data Augmentation Perspective

In audio recognition, improving the accuracy and generalizability of Pretrained Audio Neural Networks (PANNs) remains challenging. This study introduces Randomized Area Ratio Patch Masking (RARPM), a novel data augmentation technique that applies random patches with varying transparency to log mel s...

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Bibliographic Details
Main Authors: Weichun Wong, Yachun Li, Shihan Li
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Randomized area ratio patch masking
data augmentation
neural networks
audio classification
spectrogram analysis
Online Access:https://ieeexplore.ieee.org/document/10706845/
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https://ieeexplore.ieee.org/document/10706845/

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