Music Recognition and Classification Algorithm considering Audio Emotion

Wang, Na; Fang, Yong

doi:10.1155/2022/3138851

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…According to the experimental findings, the proposed approach has a recognition accuracy of 71% for joyful emotions and 68.8% for sad emotions [38]. For three experiments, MECS 1 obtains accuracy of 91 %, 88 %, and 86 %, MECS 2 obtains accuracy of 87 %, 82 %, and 79 %, MECS 3 obtains 82.3 %, 82 percent, and 81.6 % accuracy [39] [40].…”

Section: B Related Workmentioning

confidence: 86%

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

Ghosh

Riad

2022

ijcs

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For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. To extract meaningful knowledge, however, past studies' shortcomings of low accuracy and overfitting have to be addressed. We have proposed a model combining Conv1D, Bi-GRU and the Bahdanau attention mechanism for music emotion classification of our Bengali music dataset. The model integrates distinct MFCCs wav preprocessing methods with deep learning methods and attention-based methods. The attention mechanism has increased the accuracy of the proposed classification model. The music is finally classified into one of the four emotion classes: Angry, Happy, Relax, Sad. The proposed Conv1D+BiGRU+Attention model is validated as more effective and efficient at classifying emotions in the Bengali music dataset than baseline methods, according to comparisons with baseline models. For our Bengali music dataset, the performance of our proposed model is 95%.

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Section: B Related Workmentioning

confidence: 86%

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

Ghosh

Riad

2022

ijcs

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“…In [7], the authors used a training RNN model in piano performance; this model not only generates music, but it can also learn dynamic music performance. Na et al investigate the effectiveness of deep learning algorithms in automatic music generation, but having less literature on the interactivity for the generation systems of neuro music because interpretability of neural networks is poor [8]. Dua…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-Enabled Composition System Based on Piano Score Recognition

Shang

2022

Mobile Information Systems

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Piano is used for music and comprises a stringed keyboard instrument wherein the strings are tapped by softer-coated wooden hammers. The score providing music for the piano, often a compressed transcription of orchestral music, is referred to as piano score. Presently, the Internet is overflowing with music score resources. Having so many music score resources available, professional learners and amateur music lovers are unable to identify and obtain music score information that matches their needs and wasting valuable time. Due to the rapid development of deep learning algorithms, some individuals utilize these algorithms to detect piano scores and construct composition systems, reducing the need of traditional machine learning algorithms on manual design and music knowledge guidelines. This paper uses the deep learning algorithm to construct piano score recognition framework based on K-Nearest Neighbor (KNN) algorithm and formulates the recognition system into multinote that significantly improves the recognition rate for the system. The self-attention mechanism is then introduced in order to build a composition system based on a deep learning algorithm in which composition training and processes are described. Finally, a comparative experiment is conducted to evaluate the recognition accuracy for the KNN-based piano score recognition system. The results show that highest recognition accuracy of this system is 67.5%. The effect of composition system is evaluated based on prediction accuracy of notes. Three experiments are conducted to train the composition notes. As a result, the prediction accuracy of experiments 1, 2, and 3 is 89.2%, 91.8%, and 92.7%, respectively, indicating that the system has a high prediction accuracy and a perfect composition effect.

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Comparative Analysis of Multi-Model and Uni-Model Approaches using Time Distributed Bidirectional LSTM for Multidata Sentiment Analysis

Gupta,

Parmar

2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Music Recognition and Classification Algorithm considering Audio Emotion

Cited by 6 publications

References 29 publications

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

A Deep Learning-Enabled Composition System Based on Piano Score Recognition

Comparative Analysis of Multi-Model and Uni-Model Approaches using Time Distributed Bidirectional LSTM for Multidata Sentiment Analysis

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