Maximal Figure-of-Merit Embedding for Multi-Label Audio Classification

Abstract

This work tackles the problem of the domestic audio tagging or environmental sound classification, where one audio recording can contain one or more acoustic events and a recognizer should output all of those tags. A baseline model for this task is a convolutional recurrent neural network (CRNN) with sigmoid output nodes optimized using the binary cross-entropy objective. Traditional error metrics, such as classification error, are not suitable for this type of task. In this work, we show that the maximal figure-of-merit (MFoM) framework helps to separate the multi-label classes in terms of equal error rate (EER). We embed MFoM into the deep learning objective function and gain more than 9% relative improvement, compared to the baseline model with binary cross-entropy.