Zhoukou Vocational and Technical College, Henan, Zhoukou 466002, China.
Comput Intell Neurosci. 2021 Dec 16;2021:5398922. doi: 10.1155/2021/5398922. eCollection 2021.
To implement a mature music composition model for Chinese users, this paper analyzes the music composition and emotion recognition of composition content through big data technology and Neural Network (NN) algorithm. First, through a brief analysis of the current music composition style, a new Music Composition Neural Network (MCNN) structure is proposed, which adjusts the probability distribution of the Long Short-Term Memory (LSTM) generation network by constructing a reasonable Reward function. Meanwhile, the rules of music theory are used to restrict the generation of music style and realize the intelligent generation of specific style music. Afterward, the generated music composition signal is analyzed from the time-frequency domain, frequency domain, nonlinearity, and time domain. Finally, the emotion feature recognition and extraction of music composition content are realized. Experiments show that: when the iteration times of the function increase, the number of weight parameter adjustments and learning ability will increase, and thus the accuracy of the model for music composition can be greatly improved. Meanwhile, when the iteration times increases, the loss function will decrease slowly. Moreover, the music composition generated through the proposed model includes the following four aspects: sadness, joy, loneliness, and relaxation. The research results can promote music composition intellectualization and impacts traditional music composition mode.
为了为中国用户实现成熟的音乐作曲模型，本文通过大数据技术和神经网络（NN）算法对作曲内容进行音乐作曲和情感识别分析。首先，通过对当前音乐作曲风格的简要分析，提出了一种新的音乐作曲神经网络（MCNN）结构，通过构建合理的奖励函数来调整长短期记忆（LSTM）生成网络的概率分布。同时，利用音乐理论规则来限制音乐风格的生成，实现特定风格音乐的智能生成。然后，从时频域、频域、非线性和时域分析生成的音乐作曲信号。最后，实现音乐作曲内容的情感特征识别和提取。实验表明：随着函数迭代次数的增加，权重参数调整的次数和学习能力将会增加，从而极大地提高模型对音乐作曲的准确性。同时，随着迭代次数的增加，损失函数会缓慢下降。此外，通过所提出的模型生成的音乐作曲包括悲伤、喜悦、孤独和放松四个方面。研究结果可以促进音乐作曲的智能化发展，并对传统音乐作曲模式产生影响。
