优化了用于多扬声器录音中的扬声器变化检测的技术,使用pyknogram和高效的距离度量.
Sukhvinder Kaur1, Chander Prabha2, Ravinder Pal Singh3
1Swami Devi Dyal Institute of Engineering and Technology, Panchkula, Haryana, India.
本研究介绍了一种用于多扬声器音频的新型扬声器细分系统. 一个新的距离计与皮克诺格拉姆特征提取相结合,在检测扬声器更换点方面实现了99.34%的准确性.
科学领域:
- 语音处理 语音处理
- 信号分析 信号分析
- 机器学习 机器学习
背景情况:
- 扬声器细分对于各种音频分析任务至关重要,例如语音识别和扬声器日记化.
- 在多扬声器录音中准确检测扬声器更换点仍然是一个挑战.
研究的目的:
- 开发和评估一个新的扬声器细分系统,用于多扬声器音频.
- 为了提高检测扬声器变换点的准确性,使用新的特征提取和距离度量算法.
主要方法:
- 音频预处理涉及降低噪音,使用离散波段转换 (道贝契波段"db40") 进行语音压缩,以及制.
- 功能提取是使用pyknogram和非线性能量运算符 (NEO) 进行的.
- 通过应用不相似度措施 (贝叶斯信息标准,库尔巴克莱布勒分歧,T测试和拟议的算法) 来检测扬声器变化点,以在滑动窗口中框架特征.
主要成果:
- 拟议的距离度量与图形图表功能相结合,实现了最高准确率99.34%.
- 使用回忆,精度和F测量来评估性能,证明了拟议方法在标准算法上的优越性.
- 该系统在多扬声器录音中有效地识别了扬声器边界.
结论:
- 拟议的扬声器细分系统,利用pyknogram功能和一种新的距离度量,显著提高扬声器变化点检测的准确性.
- 这种方法为多扬声器音频细分提供了强大的解决方案,超过了像BIC,KLD和T-test这样的既定方法.
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