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      A Bi-directional Transformer for Musical Chord Recognition

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          Abstract

          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.

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          Most cited references7

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          Rethinking Automatic Chord Recognition with Convolutional Neural Networks

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            Learning a robust Tonnetz-space transform for automatic chord recognition

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              HMM-based approach for automatic chord detection using refined acoustic features

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                Author and article information

                Journal
                05 July 2019
                Article
                1907.02698
                06fec974-e108-4376-8be4-e4cac46a395a

                http://creativecommons.org/licenses/by/4.0/

                History
                Custom metadata
                20th International Society for Music Information Retrieval Conference (ISMIR), Delft, The Netherlands, 2019
                cs.SD cs.LG eess.AS

                Artificial intelligence,Electrical engineering,Graphics & Multimedia design

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