ARIONet: An Advanced Self-supervised Contrastive Representation Network for Birdsong Classification and Future Frame Prediction

2025-10-01

AI Summary

To address the challenges of heavy reliance on labeled data, limited feature representation, and neglect of temporal dynamics in avian vocalization classification, this paper proposes ARIONet—the first framework integrating self-supervised contrastive learning with future audio frame prediction. ARIONet employs a multi-feature-fused Transformer encoder to learn high-quality acoustic representations without large-scale annotated data. By jointly optimizing contrastive loss and frame prediction loss, and incorporating targeted data augmentation, the model significantly enhances both species identification accuracy and temporal modeling capability. Evaluated on four public avian vocalization datasets, ARIONet achieves up to 98.41% classification accuracy (F1-score: 97.84%) and 95% similarity in future frame reconstruction, with substantially reduced prediction error. This work establishes a scalable, low-resource paradigm for bioacoustic analysis and ecological monitoring.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Representation Learning for Vision
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Automated birdsong classification is essential for advancing ecological monitoring and biodiversity studies. Despite recent progress, existing methods often depend heavily on labeled data, use limited feature representations, and overlook temporal dynamics essential for accurate species identification. In this work, we propose a self-supervised contrastive network, ARIONet (Acoustic Representation for Interframe Objective Network), that jointly optimizes contrastive classification and future frame prediction using augmented audio representations. The model simultaneously integrates multiple complementary audio features within a transformer-based encoder model. Our framework is designed with two key objectives: (1) to learn discriminative species-specific representations for contrastive learning through maximizing similarity between augmented views of the same audio segment while pushing apart different samples, and (2) to model temporal dynamics by predicting future audio frames, both without requiring large-scale annotations. We validate our framework on four diverse birdsong datasets, including the British Birdsong Dataset, Bird Song Dataset, and two extended Xeno-Canto subsets (A-M and N-Z). Our method consistently outperforms existing baselines and achieves classification accuracies of 98.41%, 93.07%, 91.89%, and 91.58%, and F1-scores of 97.84%, 94.10%, 91.29%, and 90.94%, respectively. Furthermore, it demonstrates low mean absolute errors and high cosine similarity, up to 95%, in future frame prediction tasks. Extensive experiments further confirm the effectiveness of our self-supervised learning strategy in capturing complex acoustic patterns and temporal dependencies, as well as its potential for real-world applicability in ecological conservation and monitoring.

Problem

• Classifying birdsong species without requiring large labeled datasets
• Modeling temporal dynamics in audio for accurate species identification
• Learning discriminative acoustic representations through self-supervised contrastive learning

Innovation

• Self-supervised contrastive learning with augmented audio representations
• Transformer-based encoder integrating multiple complementary audio features
• Joint optimization of classification and future frame prediction