  Chord recognition is an important task since chords are highly abstract and
descriptive features of music. For effective chord recognition, it is essential
to utilize relevant context in audio sequence. While various machine learning
models such as convolutional neural networks (CNNs) and recurrent neural
networks (RNNs) have been employed for the task, most of them have limitations
in capturing long-term dependency or require training of an additional model.
In this work, we utilize a self-attention mechanism for chord recognition to
focus on certain regions of chords. Training of the proposed bi-directional
Transformer for chord recognition (BTC) consists of a single phase while
showing competitive performance. Through an attention map analysis, we have
visualized how attention was performed. It turns out that the model was able to
divide segments of chords by utilizing adaptive receptive field of the
attention mechanism. Furthermore, it was observed that the model was able to
effectively capture long-term dependencies, making use of essential information
regardless of distance.
