Leveraging Hierarchical Contrastive Learning for Acoustic Identification of Individual Animals Across Multiple Species
Acoustic identification of individual animals can be improved by leveraging hierarchical contrastive learning to create robust representations that preserve the hierarchical relationships between species and taxa.
새로운 해석 가능한 딥러닝 모델 AudioProtoPNet을 통한 새소리 분류
AudioProtoPNet은 새소리 분류를 위한 해석 가능한 딥러닝 모델로, 새소리 데이터의 특징적인 부분을 프로토타입으로 학습하여 분류 결과에 대한 설명을 제공한다.
AudioProtoPNet: An Interpretable Deep Learning Model for Classifying Bird Sounds
AudioProtoPNet is an interpretable deep learning model that can accurately classify bird species from audio recordings by learning and identifying prototypical sound patterns.
BirdSet: A Multi-Task Benchmark For Classification In Avian Bioacoustics
Deep learning models in avian bioacoustics face challenges due to inconsistent research practices. BirdSet benchmark aims to address these issues by providing a unified framework for classifying bird vocalizations.
BirdSet: A Unified Benchmark for Avian Bioacoustics Classification
Creating a unified benchmark for classifying bird vocalizations in avian bioacoustics.
Challenges in Multi-label Classification of Rare Anuran Sounds Addressed with Mixup Methods
Mix2 framework effectively addresses multi-label imbalanced classification challenges in bioacoustics.
Wavelet Scattering Transform Application to Watkins Marine Mammal Sound Database
The author explores the application of Wavelet Scattering Transform (WST) in bioacoustics, outperforming existing methods by 6% using WST and 8% using Mel spectrogram preprocessing, achieving a top accuracy of 96%.
