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基于内容的音频分类和检索使用修改的细菌食优化算法
Amani K Samha1, Ghalib H Alshammri2, Stephen Jeswinde Nuagah3
1Management Information System Department, College of Business Administration, King Saud University, Riyadh 28095, Saudi Arabia.
Computational intelligence and neuroscience
|July 17, 2023
概括
一个新的修改后的细菌食优化算法 (MBFOA) 改善了音频分类和检索. 这种方法提高了各种应用的准确性,灵敏性和特异性,减少了计算复杂性.
科学领域:
- 计算机科学 计算机科学
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 音频分类和检索对于多媒体和医学和监控等不同领域至关重要.
- 现有的方法在计算复杂性和特征选择方面面临挑战.
- 确定最佳的音频属性是有效分类的关键.
研究的目的:
- 引入一种新的算法,即修改后的细菌食优化算法 (MBFOA),用于音频数据检索和分类.
- 为了减少与当前音频处理技术相关的计算复杂性.
- 为了提高音频信号分析的准确性和效率.
主要方法:
- 该研究使用增强的Mel频率塞普斯特拉系数 (EMFCC) 和增强的功率正常化塞普斯特拉系数 (EPNCC) 与峰值估计信号相结合.
- 修改后的细菌食优化算法 (MBFOA) 用于通过健身功能优化特征选择.
- 一个概率神经网络 (PNN) 用于区分音乐和语音信号.
主要成果:
- 与类似的现有方法相比,MBFOA算法显示出更高的性能.
- 拟议的方法在音频分类中实现了更高的灵敏度,特异性和整体准确度.
- 功能提取和特性列表在分类后有效地执行.
结论:
- 该MBFOA在音频分类和检索系统方面取得了重大进展.
- 该算法提供了改进的计算效率和强大的性能.
- 这种方法有可能在多媒体和需要精确音频分析的专业领域得到广泛应用.
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