# AudRandAug: Random Image Augmentations for Audio Classification

arXiv: https://arxiv.org/abs/2309.04762 · HTML: https://mlsanity.com/paper/2309.04762/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 62 references that are in the map.
- First posted on arXiv 9 Sep 2023.

## Builds on (most cited first, 25 of 25)

- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music](https://mlsanity.com/paper/1803.05428/) (2018)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [Natural Language Processing Advancements By Deep Learning: A Survey](https://mlsanity.com/paper/2003.01200/) (2020)
- [Convolutional Recurrent Neural Networks for Music Classification](https://mlsanity.com/paper/1609.04243/) (2016)
- [Rethinking CNN Models for Audio Classification](https://mlsanity.com/paper/2007.11154/) (2020)
- [Data augmentation approaches for improving animal audio classification](https://mlsanity.com/paper/1912.07756/) (2019)
- [ESResNet: Environmental Sound Classification Based on Visual Domain Models](https://mlsanity.com/paper/2004.07301/) (2020)
- [SpecMix : A Mixed Sample Data Augmentation method for Training withTime-Frequency Domain Features](https://mlsanity.com/paper/2108.03020/) (2021)
- [Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms](https://mlsanity.com/paper/1703.01789/) (2017)
- [Multimodal Composite Association Score: Measuring Gender Bias in Generative Multimodal Models](https://mlsanity.com/paper/2304.13855/) (2023)
- [Raw Waveform-based Audio Classification Using Sample-level CNN Architectures](https://mlsanity.com/paper/1712.00866/) (2017)
- [Investigating Multi-Feature Selection and Ensembling for Audio Classification](https://mlsanity.com/paper/2206.07511/) (2022)
- [Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification](https://mlsanity.com/paper/1802.09697/) (2018)
- [Multi-stream Network With Temporal Attention For Environmental Sound Classification](https://mlsanity.com/paper/1901.08608/) (2019)
- [SpliceOut: A Simple and Efficient Audio Augmentation Method](https://mlsanity.com/paper/2110.00046/) (2021)
- [Precise Single-stage Detector](https://mlsanity.com/paper/2210.04252/) (2022)
- [Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets](https://mlsanity.com/paper/2210.00824/) (2022)
- [Understanding EEG signals for subject-wise Definition of Armoni Activities](https://mlsanity.com/paper/2301.00948/) (2023)
- [Data Dimension Reduction makes ML Algorithms efficient](https://mlsanity.com/paper/2211.09392/) (2022)
- [Advanced Audio Aid for Blind People](https://mlsanity.com/paper/2212.00004/) (2022)
- [Go Together: Bridging the Gap between Learners and Teachers](https://mlsanity.com/paper/2308.06268/) (2023)

## Built on by (most cited first, 3 of 3)

- [OxML Challenge 2023: Carcinoma classification using data augmentation](https://mlsanity.com/paper/2409.10544/) (2024)
- [FaceSaliencyAug: Mitigating Geographic, Gender and Stereotypical Biases via Saliency-Based Data Augmentation](https://mlsanity.com/paper/2410.14070/) (2024)
- [Saliency-Based diversity and fairness Metric and FaceKeepOriginalAugment: A Novel Approach for Enhancing Fairness and Diversity](https://mlsanity.com/paper/2411.00831/) (2024)

## Abstract (arXiv)

Data augmentation has proven to be effective in training neural networks. Recently, a method called RandAug was proposed, randomly selecting data augmentation techniques from a predefined search space. RandAug has demonstrated significant performance improvements for image-related tasks while imposing minimal computational overhead. However, no prior research has explored the application of RandAug specifically for audio data augmentation, which converts audio into an image-like pattern. To address this gap, we introduce AudRandAug, an adaptation of RandAug for audio data. AudRandAug selects data augmentation policies from a dedicated audio search space. To evaluate the effectiveness of AudRandAug, we conducted experiments using various models and datasets. Our findings indicate that AudRandAug outperforms other existing data augmentation methods regarding accuracy performance.