Emotion recognition from MIDI musical file using Enhanced Residual Gated Recurrent Unit architecture

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
V. Bhuvana Kumar, M. Kathiravan
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引用次数: 0

Abstract

The complex synthesis of emotions seen in music is meticulously composed using a wide range of aural components. Given the expanding soundscape and abundance of online music resources, creating a music recommendation system is significant. The area of music file emotion recognition is particularly fascinating. The RGRU (Enhanced Residual Gated Recurrent Unit), a complex architecture, is used in our study to look at MIDI (Musical Instrument and Digital Interface) compositions for detecting emotions. This involves extracting diverse features from the MIDI dataset, encompassing harmony, rhythm, dynamics, and statistical attributes. These extracted features subsequently serve as input to our emotion recognition model for emotion detection. We use an improved RGRU version to identify emotions and the Adaptive Red Fox Algorithm (ARFA) to optimize the RGRU hyperparameters. Our suggested model offers a sophisticated classification framework that effectively divides emotional content into four separate quadrants: positive-high, positive-low, negative-high, and negative-low. The Python programming environment is used to implement our suggested approach. We use the EMOPIA dataset to compare its performance to the traditional approach and assess its effectiveness experimentally. The trial results show better performance compared to traditional methods, with higher accuracy, recall, F-measure, and precision.
利用增强型残差门控递归单元架构从 MIDI 音乐文件中识别情感
音乐中复杂的情感综合体是由各种听觉成分精心构成的。随着声音范围的扩大和在线音乐资源的丰富,创建一个音乐推荐系统意义重大。音乐文件情感识别领域尤其引人入胜。在我们的研究中,使用了 RGRU(增强型残差门控循环单元)这一复杂结构来研究 MIDI(乐器和数字接口)作品,以检测情感。这涉及从 MIDI 数据集中提取各种特征,包括和声、节奏、动态和统计属性。这些提取的特征随后将作为情感识别模型的输入,用于情感检测。我们使用改进的 RGRU 版本来识别情感,并使用自适应红狐算法 (ARFA) 来优化 RGRU 的超参数。我们建议的模型提供了一个复杂的分类框架,可有效地将情感内容分为四个独立的象限:正-高、正-低、负-高和负-低。我们使用 Python 编程环境来实现我们建议的方法。我们使用 EMOPIA 数据集将其性能与传统方法进行比较,并通过实验评估其有效性。试验结果表明,与传统方法相比,该方法具有更高的准确率、召回率、F-measure 和精确度。
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来源期刊
Frontiers in Computer Science
Frontiers in Computer Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.30
自引率
0.00%
发文量
152
审稿时长
13 weeks
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