Piano accompaniment is necessary for the creation and performance in music atmosphere and exhibits dance motion, passionate appeal, or qualities of the style. These are stated positively, and work rates were poor with no music, while removing the music is considered the longest in limited research to find the differences with reports that could derive from methodological problems. Performing a complete evaluation of music affects hormones, autonomous behavior, and human stress response with health deficiencies. Therefore, music classification analysis using the machine learning (MCA-ML) technique has been introduced to predict this qualitative method to explore dance, music as a rehabilitation process described in a stressful life event by persons involved and affected. A convolution neural network (CNN) approach is frequently employed to analyze various coping effects of different music pattern-based classification issues to attain high advantages. A musical perception approach based on cloud computing is introduced to efficiently allocate resources to analyze the music with cognitive systems efficiently. Empirical analysis revealed that the proposed architecture permits real-time access to the data and successfully supports several parallel applications with 97.81%performance.

钢琴伴奏是音乐氛围的创造和表现所必需的，表现出舞蹈的动作、激情的感染力或风格的特质。这些都是积极的陈述，没有音乐的工作率很低，而在有限的研究中，删除音乐被认为是最长的，以找出与可能源于方法问题的报告的差异。对音乐进行完整的评估会影响荷尔蒙、自主行为和具有健康缺陷的人类压力反应。因此，引入了使用机器学习 (MCA-ML) 技术的音乐分类分析来预测这种定性方法，以探索舞蹈、音乐作为涉及和受影响的人在压力生活事件中描述的康复过程。卷积神经网络 (CNN) 方法经常被用来分析基于不同音乐模式的分类问题的各种应对效果，以获得很高的优势。引入了一种基于云计算的音乐感知方法，以高效分配资源，利用认知系统对音乐进行高效分析。实证分析表明，所提出的架构允许实时访问数据，并以 97.81% 的性能成功支持多个并行应用程序。