Monday 03 March 2025
The pursuit of better audio classification and tagging has led researchers to explore new frontiers in machine learning. Recently, a team of scientists has introduced Local-Higher Order Graph Neural Networks (LHGNN), a novel approach that combines graph neural networks with clustering techniques to improve the accuracy and efficiency of audio processing tasks.
Audio classification and tagging involve identifying specific sounds or events within an audio signal. This can be a challenging task, as audio data can be noisy, variable in pitch and volume, and contain multiple overlapping sounds. Traditional approaches rely on convolutional neural networks (CNNs) or recurrent neural networks (RNNs), which can struggle to capture complex patterns and relationships between different audio features.
LHGNN addresses this challenge by leveraging graph neural networks, a type of deep learning architecture that excels at modeling relational data. In the context of audio processing, LHGNN constructs a graph where nodes represent individual audio frames or segments, and edges connect frames that share similar acoustic properties. This allows the model to capture hierarchical relationships between different audio features, such as mel-frequency cepstral coefficients (MFCCs) or spectrogram representations.
The key innovation of LHGNN lies in its ability to integrate local neighborhood information with higher-order clustering patterns. The model first identifies the k-nearest neighbors (k-NN) for each node, which provides a local view of the audio signal. It then applies Fuzzy C-Means clustering to identify clusters of nodes that share similar acoustic properties. These clusters are used to update the node embeddings, allowing the model to capture both local and global patterns in the audio data.
Experiments on three publicly available datasets – Audioset, FSD50K, and ESC-50 – demonstrate the effectiveness of LHGNN. The model outperforms state-of-the-art approaches, including CNN-based models like PANNs and Audio Transformers, across all three datasets. Notably, LHGNN achieves this performance without relying on extensive pretraining or large-scale computing resources.
The authors also conduct an ablation study to evaluate the impact of different graph kernel functions and clustering methods on the model’s performance. The results suggest that combining local feature information with cluster centroids produces the best results, while density-based clustering performs slightly better than Fuzzy C-Means in some cases.
While LHGNN shows promise as a more accurate and efficient approach to audio classification and tagging, there are still opportunities for improvement.
Cite this article: “Advancing Audio Classification with Local-Higher Order Graph Neural Networks”, The Science Archive, 2025.
Audio, Classification, Tagging, Machine Learning, Graph Neural Networks, Clustering, Audio Processing, Deep Learning, Relational Data, Acoustic Properties
