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Description
In this work, we investigate whether topological features derived from persistent homology of Takens embeddings can complement conventional acoustic features for environmental sound classification on the ESC-50 dataset. A compact statistical summary of persistence diagrams is extracted from short overlapping segments, aggregated at the recording level, and combined with conventional acoustic features. Experiments show that fusing topological and acoustic descriptors consistently improves classification performance across multiple classifiers. With a Random Forest classifier, the proposed fusion achieves 48.5% accuracy, an absolute improvement of 7.6 percentage points over the baseline. With a multilayer perceptron, the improvement reaches 12.1 percentage points, yielding 55.1% accuracy. A dimensionality-matched control experiment confirms that these gains reflect informational content, not extended dimensions. This work provides empirical evidence that persistent homology captures structural properties of environmental sounds that are complementary to conventional acoustic features.