Learning Compact Structural Representations For Audio Events Using Regressor Banks

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 con-

fidence 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.