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Frequency component grouping based sound source extraction from mixed audio signals using spectral analysis

机译:使用频谱分析从混合音频信号中提取基于频率成分分组的声源

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The extraction of the polyphonic sounds from mix of different sources is natural, even to normal human listeners. For this reason, human auditory recognition system is very robust compared to the algorithms used to analyze audio in different computing systems. This work is a frequency domain based approach for sound source extraction from any mixed audio signal to improve the efficiency of audio recognition algorithms. In this work, we propose a new approach to estimate, analyze and extract different melodies from different physical sources without modeling the signal or sources. This process uses one or two microphone(s) based systems to analyze sound signal to be compatible to human hearing. Our work is specifically aiming the extraction of sound sources by matching frequency patterns and calculation of delay times, providing the spatial and temporal data of the signal sources and their outputs.
机译:从不同来源的混合中提取和弦声音是很自然的,即使对于普通的人类听众也是如此。因此,与用于分析不同计算系统中的音频的算法相比,人类听觉识别系统非常强大。这项工作是一种基于频域的方法,可从任何混合音频信号中提取声源,以提高音频识别算法的效率。在这项工作中,我们提出了一种新的方法来估计,分析和提取来自不同物理源的不同旋律,而无需对信号或源进行建模。此过程使用一个或两个基于麦克风的系统来分析声音信号以与人类听力兼容。我们的工作专门针对通过匹配频率模式和计算延迟时间来提取声源,提供信号源及其输出的时空数据。

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