Sample Mixed-based Data Augmentation For Domestic Audio Tagging

Abstract

Audio tagging has attracted increasing attention since last decade and has various potential applications in many fields. The objective of audio tagging is to predict the labels of an audio clip. Recently deep learning methods have been applied to audio tagging and have achieved state-of-the-art performance, which provides a poor generalization ability on new data. However due to the limited size of audio tagging data such as DCASE data, the trained models tend to result in overfitting of the network. Previous data augmentation methods such as pitch shifting, time stretching and adding background noise do not show much improvement in audio tagging. In this paper, we explore the sample mixed data augmentation for the domestic audio tagging task, including mixup, SamplePairing and extrapolation. We apply a convolutional recurrent neural network (CRNN) with attention module with log-scaled mel spectrum as a baseline system. In our experiments, we achieve an state-of-the-art of equal error

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keywei2018sample

Related papers

• Audio-based Music Classification With Densenet And Data Augmentation (2019)10.48
• Combining High-level Features Of Raw Audio Waves And Mel-spectrograms For Audio Tagging (2018)0.00
• Convolutional Gated Recurrent Neural Network Incorporating Spatial Features For Audio Tagging (2017)13.23
• Exploring Train And Test-time Augmentations For Audio-language Learning (2022)0.00
• Attention And Localization Based On A Deep Convolutional Recurrent Model For Weakly Supervised Audio Tagging (2017)11.39
• Mixspeech: Data Augmentation For Low-resource Automatic Speech Recognition (2021)13.60
• Improving Natural-language-based Audio Retrieval With Transfer Learning And Audio & Text Augmentations (2022)0.00
• Audiosetmix: Enhancing Audio-language Datasets With Llm-assisted Augmentations (2024)0.00