School of Music, Sichuan Normal University, Chengdu 610000, China.
J Healthc Eng. 2022 Mar 9;2022:1363690. doi: 10.1155/2022/1363690. eCollection 2022.
In chorus activities, the conductor leads chorus members to recreate music works. If you want to interpret music works perfectly with sound, emotion and emotional expression are particularly important. In this paper, a cloud HBD (health big data) integration system based on ensemble learning is designed to realize the high-efficiency and high-precision integration of HBD. An emotional speech database containing three emotions such as pleasure, calmness, and boredom is established, and the corpus problems such as emotional feature analysis and extraction needed for chorus emotion recognition research are solved. It also studies the classification and decision-making in emotional changes, and a DBN (deep belief network) chorus emotion recognition algorithm based on multiple emotional features is proposed. Feature DBN (Deep Belief Network) Chorus Emotion Recognition Algorithm This paper extracts various robust low-level features according to different features' ability to describe emotions and then feeds them into the DBN network to extract high-level feature descriptors. Then, the classification results of ELM (extreme learning machine) are voted and fused with the idea of ensemble learning, and the effectiveness of the algorithm is proved on three public datasets.
在合唱活动中，指挥引导合唱团员再现音乐作品。如果想用声音完美地诠释音乐作品，情感和情感表达尤为重要。本文设计了一种基于集成学习的云 HBD（健康大数据）集成系统，实现 HBD 的高效高精度集成。建立了一个包含快乐、平静和无聊三种情绪的情感语音数据库，解决了合唱情感识别研究中需要的情感特征分析和提取等语料库问题。还研究了情感变化中的分类和决策，提出了一种基于多种情感特征的 DBN（深度置信网络）合唱情感识别算法。特征 DBN（深度置信网络）合唱情感识别算法 根据不同特征对情感的描述能力，本文提取了各种稳健的低层次特征，然后将它们输入 DBN 网络，以提取高层次特征描述符。然后，使用集成学习的思想对 ELM（极限学习机）的分类结果进行投票和融合，并在三个公共数据集上验证了算法的有效性。
