arXiv:2110.04585
An evaluation of data augmentation methods for sound scene geotagging
원문 초록
Sound scene geotagging is a new topic of research which has evolved from acoustic scene classification. It is motivated by the idea of audio surveillance. Not content with only describing a scene in a recording, a machine which can locate where the recording was captured would be of use to many. In this paper we explore a series of common audio data augmentation methods to evaluate which best improves the accuracy of audio geotagging classifiers. Our work improves on the state-of-the-art city geotagging method by 23% in terms of classification accuracy.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)
Lightweight Convolutional Neural Networks for Retinal Disease Classification
Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset
Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors
토론
이 논문과 연결된 포럼 글이 아직 없습니다.