Learning Compact Structural Representations for Audio Events Using Regressor Banks

TL;DR

This paper presents a novel, compact learned descriptor for audio event representation that uses class-specific regressors to improve classification accuracy with simple models.

Contribution

The introduction of a regressor bank-based descriptor that is both compact and more effective than existing methods for audio event classification.

Findings

Descriptor is as compact as the number of classes.

Simple linear classifiers outperform nonlinear baselines.

Achieves state-of-the-art accuracy on audio event classification.

Abstract

We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on the input signal. The regressors are class-specific and trained using the random regression forests framework. Given an input signal, each regressor estimates the onset and offset positions of the target event. The estimation confidence scores output by a regressor are then used to quantify how the target event aligns with the temporal structure of the corresponding category. Our proposed descriptor has two advantages. First, it is compact, i.e. the dimensionality of the descriptor is equal to the number of event classes. Second, we show that even simple linear classification models, trained on our descriptor, yield better accuracies on audio event classification task than not only the nonlinear baselines but also the state-of-the-art results.