  Although music is typically multi-label, many works have studied hierarchical
music tagging with simplified settings such as single-label data. Moreover,
there lacks a framework to describe various joint training methods under the
multi-label setting. In order to discuss the above topics, we introduce
hierarchical multi-label music instrument classification task. The task
provides a realistic setting where multi-instrument real music data is assumed.
Various hierarchical methods that jointly train a DNN are summarized and
explored in the context of the fusion of deep learning and conventional
techniques. For the effective joint training in the multi-label setting, we
propose two methods to model the connection between fine- and coarse-level
tags, where one uses rule-based grouped max-pooling, the other one uses the
attention mechanism obtained in a data-driven manner. Our evaluation reveals
that the proposed methods have advantages over the method without joint
training. In addition, the decision procedure within the proposed methods can
be interpreted by visualizing attention maps or referring to fixed rules.
