I Guess That's Why They Call it the Blues: Causal Analysis for Audio Classifiers

2026-01-23

AI Summary

Existing audio classifiers often rely on non-musical features or spurious correlations, rendering them susceptible to misleading cues. This work introduces, for the first time, the causal inference concepts of sufficiency and necessity into the interpretability of audio classification. By conducting frequency-domain analysis, the study identifies critical subsets of frequency components that are causally responsible for model decisions and develops FreqReX, a tool enabling targeted interventions. Experiments demonstrate that altering just a single frequency bin among 240,000 can flip the classification outcome in 58% of cases, with perturbations remaining nearly imperceptible to human listeners. These findings expose significant model fragility and offer a novel pathway toward more robust and interpretable audio classification systems.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

It is well-known that audio classifiers often rely on non-musically relevant features and spurious correlations to classify audio. Hence audio classifiers are easy to manipulate or confuse, resulting in wrong classifications. While inducing a misclassification is not hard, until now the set of features that the classifiers rely on was not well understood. In this paper we introduce a new method that uses causal reasoning to discover features of the frequency space that are sufficient and necessary for a given classification. We describe an implementation of this algorithm in the tool FreqReX and provide experimental results on a number of standard benchmark datasets. Our experiments show that causally sufficient and necessary subsets allow us to manipulate the outputs of the models in a variety of ways by changing the input very slightly. Namely, a change to one out of 240,000 frequencies results in a change in classification 58% of the time, and the change can be so small that it is practically inaudible. These results show that causal analysis is useful for understanding the reasoning process of audio classifiers and can be used to successfully manipulate their outputs.

Problem

• audio classifiers
• spurious correlations
• causal analysis
• frequency features
• model robustness

Innovation

• causal reasoning
• audio classifiers
• frequency space
• sufficient and necessary features
• model manipulation