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<p>Various studies have been conducted in the field of sound event classification, with a specific emphasis on urban environments. However, there has been limited investigation in the realm of forest-oriented sound event classification. We introduce FSM5, a task-specific forest acoustic dataset curated from multiple publicly available sources to support forest sound event classification research, and propose a two-stage framework, ForNet, for effectively classifying critical sound events in forest. Initially, a Convolutional Neural Network (CNN) is used to extract discriminative audio embeddings. In the subsequent phase, ensemble classifiers such as XGBoost and Random Forest are employed for classification. The performance of MFCC, Log-Mel, and Mel spectrogram features is systematically evaluated, and the results indicate that MFCC and Log-Mel features significantly enhance classification performance. The findings indicate that combining handcrafted acoustic representations with CNN-derived embeddings yields improved performance compared to end-to-end CNN classification. The efficacy of deep CNN’s feature representation and the discriminative capability of shallow classifiers are evaluated. To showcase the reliability of ForNet, we have also conducted tests on the benchmark: Urbansound8k dataset. ForNet achieves an accuracy of 94% under 10-fold cross-validation on UrbanSound8K, outperforming several state-of-the-art methods, and attains an accuracy of 91.4% using 10-fold cross-validation on the FSM5 dataset.</p>

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ForNet: classification of critical forest acoustic events using discriminative CNN representations and ensemble learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Deepak Krishnamoorthy,
  • Vemulapalli Shanmukha Sai,
  • R. Vishal,
  • Yannick Benezeth

摘要

Various studies have been conducted in the field of sound event classification, with a specific emphasis on urban environments. However, there has been limited investigation in the realm of forest-oriented sound event classification. We introduce FSM5, a task-specific forest acoustic dataset curated from multiple publicly available sources to support forest sound event classification research, and propose a two-stage framework, ForNet, for effectively classifying critical sound events in forest. Initially, a Convolutional Neural Network (CNN) is used to extract discriminative audio embeddings. In the subsequent phase, ensemble classifiers such as XGBoost and Random Forest are employed for classification. The performance of MFCC, Log-Mel, and Mel spectrogram features is systematically evaluated, and the results indicate that MFCC and Log-Mel features significantly enhance classification performance. The findings indicate that combining handcrafted acoustic representations with CNN-derived embeddings yields improved performance compared to end-to-end CNN classification. The efficacy of deep CNN’s feature representation and the discriminative capability of shallow classifiers are evaluated. To showcase the reliability of ForNet, we have also conducted tests on the benchmark: Urbansound8k dataset. ForNet achieves an accuracy of 94% under 10-fold cross-validation on UrbanSound8K, outperforming several state-of-the-art methods, and attains an accuracy of 91.4% using 10-fold cross-validation on the FSM5 dataset.