Earticle

초록

영어

The integration of deep learning techniques in the field of audio signal processing has marked a significant leap forward in the capability to analyze and classify complex sounds, including the nuanced and information-rich cries of infants. Deep learning's promise in this domain lies in its potential to decipher the subtle cues contained within these cries, offering insights into an infant's health, emotional state, and developmental needs. This potential application stands at the intersection of technology and healthcare, promising to enhance our understanding and response to the needs of the youngest members of society. This study experimentally demonstrates that a convolutional neural network–based audio classification model effectively learns discriminative spectral and temporal features from audio signals. Experimental results show that the proposed convolutional neural networks architecture achieves significantly higher classification accuracy than traditional machine-learning baselines, particularly when trained on spectrogram-based representations. The findings confirm that deep learning models not only improve overall performance but also provide robust generalization across different audio classes and noisy conditions.

목차

Abstract
1. INTRODUCTION
2. METHOLOGIES AND MODELING
2.1. Development of Models
2.2. Training and Validation
3. DATA PREPARATION FOR TESTS
4. EXPERIMENTAL RESULTS
4.1. Model Performance Metrics
4.2. Confusion Matrix
5. CONCLUSION
REFERENCES

키워드

저자

• Won Gyeong Hong [ Doctoral Student, School of Business, Sogang University, Seoul, South Korea ]
• Eunjee Lee [ Doctoral Student, School of Business, Sogang University, Seoul, South Korea ]
• Jinhwa Kim [ Professor, School of Business, Sogang University, Seoul, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

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• 간행물명

  International Journal of Intelligent Technologies and Innovative Practices

• 간기

  계간

• eISSN

  3092-412X

• 수록기간

  2026~2026

• 십진분류

  KDC 323 DDC 338