Chord Recognition | ScienceGate

Abstract Music symbol recognition is an important part of Optical Music Recognition (OMR), Chord recognition is one of the most important research contents in the field of music information retrieval. It plays an important role in information processing, music structure analysis, and recommendation systems. Aiming at the problem of low chord recognition accuracy in the OMR recognition model, the article proposes a chord recognition method based on the YOLOV4 neural network model. First, the YOLOV4 network model is used to train single-voice scores to obtain the best training model. Then, the scores containing chords are trained through neural network fine-tuning technology. The experimental results show that the method recognizes the chords with great results, the model was tested on the test set generated by MuseScore. The experimental results show that the accuracy of note recognition is high, which can reach the accuracy of duration value of 0.96 which is higher than the accuracy of note recognition of other score recognition models.

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Automatic Chord Recognition Based on Probabilistic Integration of Acoustic Features, Bass Sounds, and Chord Transition

Advanced Research in Applied Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-642-31087-4_7 ◽

2012 ◽

pp. 58-67

Author(s):

Katsutoshi Itoyama ◽

Tetsuya Ogata ◽

Hiroshi G. Okuno

Keyword(s):

Acoustic Features ◽

Chord Recognition

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A Chord Recognition Method of Guitar Sound Using Its Constituent Tone Information

IEEJ Transactions on Electrical and Electronic Engineering ◽

10.1002/tee.23492 ◽

2021 ◽

Author(s):

Yoshifumi Bando ◽

Motoshi Tanaka

Keyword(s):

Recognition Method ◽

Chord Recognition

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Guitar Chord Sensing and Recognition Using Multi-Task Learning and Physical Data Augmentation with Robotics

Sensors ◽

10.3390/s20216077 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6077

Author(s):

Gerelmaa Byambatsogt ◽

Lodoiravsal Choimaa ◽

Gou Koutaki

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Recognition Task ◽

Learning Model ◽

Training Data ◽

Single Task ◽

Task Learning ◽

Physical Data ◽

Music Information ◽

Chord Recognition

In recent years, many researchers have shown increasing interest in music information retrieval (MIR) applications, with automatic chord recognition being one of the popular tasks. Many studies have achieved/demonstrated considerable improvement using deep learning based models in automatic chord recognition problems. However, most of the existing models have focused on simple chord recognition, which classifies the root note with the major, minor, and seventh chords. Furthermore, in learning-based recognition, it is critical to collect high-quality and large amounts of training data to achieve the desired performance. In this paper, we present a multi-task learning (MTL) model for a guitar chord recognition task, where the model is trained using a relatively large-vocabulary guitar chord dataset. To solve data scarcity issues, a physical data augmentation method that directly records the chord dataset from a robotic performer is employed. Deep learning based MTL is proposed to improve the performance of automatic chord recognition with the proposed physical data augmentation dataset. The proposed MTL model is compared with four baseline models and its corresponding single-task learning model using two types of datasets, including a human dataset and a human combined with the augmented dataset. The proposed methods outperform the baseline models, and the results show that most scores of the proposed multi-task learning model are better than those of the corresponding single-task learning model. The experimental results demonstrate that physical data augmentation is an effective method for increasing the dataset size for guitar chord recognition tasks.

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