Unsupervised Variational Acoustic Clustering

Luan Vinícius Fiorio; Bruno Defraene; Johan David; Frans Widdershoven; Wim van Houtum; Ronald M. Aarts

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arXiv · 2503.18579

Unsupervised Variational Acoustic Clustering

Luan Vinícius Fiorio·Bruno Defraene·Johan David·Frans Widdershoven·Wim van Houtum·Ronald M. Aarts

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Abstract

We propose an unsupervised variational acoustic clustering model for clustering audio data in the time-frequency domain. The model leverages variational inference, extended to an autoencoder framework, with a Gaussian mixture model as a prior for the latent space. Specifically designed for audio applications, we introduce a convolutional-recurrent variational autoencoder optimized for efficient time-frequency processing. Our experimental results considering a spoken digits dataset demonstrate a significant improvement in accuracy and clustering performance compared to traditional methods, showcasing the model's enhanced ability to capture complex audio patterns.

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BibTeX RIS

Luan Vinícius Fiorio, Bruno Defraene, Johan David, Frans Widdershoven, Wim van Houtum, Ronald M. Aarts. 2025-03-24. Unsupervised Variational Acoustic Clustering. https://arxiv.org/abs/2503.18579

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