What type of study is this?

This is a Quantitative Study study.

October 3, 2025International Journal of Information Technologies and Systems ApproachOpen Access

Application of Long Short-Term Memory Intelligent Algorithm in Automatic Classification System

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Authors

YZYuping ZengJiangxi Science and Technology Normal University JXJichun XieDuke University

Discussion

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Implication

This research demonstrates improved classification accuracy in music genre recognition using LSTM, suggesting a new approach in music information retrieval.

Key Points

LSTM-based music genre classification achieved 49.8% accuracy over 10 genres, outperforming traditional methods.
The system demonstrated a 3.13% improvement in accuracy compared to baseline convolutional neural networks.
Utilizing deep LSTM models can effectively learn long-term dependencies in music signal processing.
Adjustments in time-based gradient back-propagation address challenges like gradient vanishing and explosion.

Cite This Study

Zeng et al. (2025) studied this question.

synapsesocial.com/papers/68e03501f0e39f13e7fa3826 https://doi.org/10.4018/ijitsa.390038

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October 3, 2025International Journal of Information Technologies and Systems ApproachOpen Access

Application of Long Short-Term Memory Intelligent Algorithm in Automatic Classification System

View Full Paper

Ask AI

Bookmark

Authors

YZYuping ZengJiangxi Science and Technology Normal University JXJichun XieDuke University

Discussion

Member takes

Implication

This research demonstrates improved classification accuracy in music genre recognition using LSTM, suggesting a new approach in music information retrieval.

Key Points

LSTM-based music genre classification achieved 49.8% accuracy over 10 genres, outperforming traditional methods.
The system demonstrated a 3.13% improvement in accuracy compared to baseline convolutional neural networks.
Utilizing deep LSTM models can effectively learn long-term dependencies in music signal processing.
Adjustments in time-based gradient back-propagation address challenges like gradient vanishing and explosion.

Cite This Study

Zeng et al. (2025) studied this question.

synapsesocial.com/papers/68e03501f0e39f13e7fa3826 https://doi.org/10.4018/ijitsa.390038

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