Symbolic Audio Classification via Modal Decision Tree Learning
This provides a transparent, symbolic alternative to black-box neural networks for audio classification, potentially useful in applications like autonomous conversation systems for healthcare.
The paper tackled audio classification tasks (age/gender recognition, emotion classification, respiratory disease diagnosis) using a symbolic modal decision tree learning approach, achieving high accuracy and low complexity with simple rules.
The range of potential applications of acoustic analysis is wide. Classification of sounds, in particular, is a typical machine learning task that received a lot of attention in recent years. The most common approaches to sound classification are sub-symbolic, typically based on neural networks, and result in black-box models with high performances but very low transparency. In this work, we consider several audio tasks, namely, age and gender recognition, emotion classification, and respiratory disease diagnosis, and we approach them with a symbolic technique, that is, (modal) decision tree learning. We prove that such tasks can be solved using the same symbolic pipeline, that allows to extract simple rules with very high accuracy and low complexity. In principle, all such tasks could be associated to an autonomous conversation system, which could be useful in different contexts, such as an automatic reservation agent for an hospital or a clinic.