Cluster-based pruning techniques for audio data

TL;DR

This paper introduces the novel application of k-means clustering for efficient data pruning in audio datasets, demonstrating significant dataset reduction while maintaining neural network classification performance.

Contribution

It is the first to apply k-means clustering for data pruning in the audio domain, showing its effectiveness in dataset reduction without performance loss.

Findings

K-means clustering can significantly reduce dataset size.

Dataset pruning preserves classification accuracy.

Scaling analysis helps identify optimal pruning strategies.

Abstract

Deep learning models have become widely adopted in various domains, but their performance heavily relies on a vast amount of data. Datasets often contain a large number of irrelevant or redundant samples, which can lead to computational inefficiencies during the training. In this work, we introduce, for the first time in the context of the audio domain, the k-means clustering as a method for efficient data pruning. K-means clustering provides a way to group similar samples together, allowing the reduction of the size of the dataset while preserving its representative characteristics. As an example, we perform clustering analysis on the keyword spotting (KWS) dataset. We discuss how k-means clustering can significantly reduce the size of audio datasets while maintaining the classification performance across neural networks (NNs) with different architectures. We further comment on the role of scaling analysis in identifying the optimal pruning strategies for a large number of samples. Our studies serve as a proof-of-principle, demonstrating the potential of data selection with distance-based clustering algorithms for the audio domain and highlighting promising research avenues.