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Graph-Based Audio Classification Using Pre-Trained Models and Graph Neural Networks.

Andrés Eduardo Castro-Ospina1, Miguel Angel Solarte-Sanchez1, Laura Stella Vega-Escobar1

  • 1Grupo de Investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia.

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Summary
This summary is machine-generated.

Representing audio data as graphs significantly improves sound classification. Graph neural networks (GNNs) show strong performance, with graph attention networks (GATs) achieving high accuracy in environmental sound and land cover identification.

Keywords:
ecoacousticsenvironmental sound classificationgraph neural networksgraph representation learningnode classificationpre-trained models

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Area of Science:

  • Acoustics and Signal Processing
  • Machine Learning
  • Environmental Science

Background:

  • Sound classification is vital for acoustic data analysis and environmental monitoring.
  • Traditional methods may not fully capture complex audio patterns.
  • Graph representation offers a novel approach to audio data.

Purpose of the Study:

  • To explore audio data representation as graphs for sound classification.
  • To evaluate the performance of various graph neural networks (GNNs) on audio tasks.
  • To identify the most effective GNN model for environmental sound analysis.

Main Methods:

  • Utilized pre-trained audio models to extract deep audio features.
  • Constructed graphs using extracted features as node information.
  • Trained and compared graph convolutional networks (GCNs), GraphSAGE, and graph attention networks (GATs).

Main Results:

  • Graph representation of audio data proved effective for classification.
  • GNNs demonstrated competitive performance in sound classification tasks.
  • The graph attention network (GAT) model achieved the highest accuracy: 83% for environmental sounds and 91% for land cover identification.

Conclusions:

  • Graph representation learning is a promising technique for audio data analysis.
  • GNNs, particularly GATs, offer a powerful tool for multi-class audio classification.
  • This methodology enhances the interpretation and application of acoustic data in environmental contexts.