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基于方位特征的听觉选择性注意计算模型研究

吕菲 夏秀渝

吕菲, 夏秀渝. 基于方位特征的听觉选择性注意计算模型研究. 自动化学报, 2017, 43(4): 634-644. doi: 10.16383/j.aas.2017.c160277
引用本文: 吕菲, 夏秀渝. 基于方位特征的听觉选择性注意计算模型研究. 自动化学报, 2017, 43(4): 634-644. doi: 10.16383/j.aas.2017.c160277
LV Fei, XIA Xiu-Yu. Study on Computational Model of Auditory Selective Attention with Orientation Feature. ACTA AUTOMATICA SINICA, 2017, 43(4): 634-644. doi: 10.16383/j.aas.2017.c160277
Citation: LV Fei, XIA Xiu-Yu. Study on Computational Model of Auditory Selective Attention with Orientation Feature. ACTA AUTOMATICA SINICA, 2017, 43(4): 634-644. doi: 10.16383/j.aas.2017.c160277

基于方位特征的听觉选择性注意计算模型研究

doi: 10.16383/j.aas.2017.c160277
详细信息
    作者简介:

    吕菲  四川大学电子信息学院硕士研究生.2013年获得温州大学通信工程学士学位.主要研究方向为听觉选择性注意计算模型.E-mail:lvfei47@163.com

    通讯作者:

    夏秀渝  四川大学电子信息学院副教授.主要研究方向为自适应声回波对消, 语音增强, 语音分离, 计算听觉场景分析, 听觉计算模型.E-mail:xiaxxy@163.com

Study on Computational Model of Auditory Selective Attention with Orientation Feature

More Information
    Author Bio:

      Master student at the College of Electronics and Information Engineering, Sichuan University. She received her bachelor degree from Wenzhou University in 2013. Her research interest covers modeling and simulating auditory selective attention computational model

    Corresponding author: XIA Xiu-Yu   Associate professor at the College of Electronics and Information Engineering, Sichuan University. Her research interest covers acoustic echo cancellation, speech enhancement, speech separation, computational auditory scene analysis, and auditory computational model. Corresponding author of this paper
  • 摘要: 经典的听觉注意计算模型主要针对声音强度、频率、时间等初级听觉特征进行研究,这些特征不能较好地模拟听觉注意指向性,必须寻求更高级的听觉特征来区分不同声音.根据听觉感知机制,本文基于声源方位特征和神经网络提出了一种双通路信息处理的自下而上听觉选择性注意计算模型.模型首先对双耳信号进行预处理和频谱分析;然后,将其分别送入where通路和what通路,其中where通路用于提取方位特征参数,并利用神经网络提取声源的局部方位特征,接着通过局部特征聚合和全局优化法得到方位特征显著图;最后,根据方位特征显著图提取主导方位并作用于what通路,采用时频掩蔽法分离出相应的主导音.仿真结果表明:该模型引入方位特征作为聚类线索,利用多级神经网络自动筛选出值得注意的声音对象,实时提取复杂声学环境中的主导音,较好地模拟了人类听觉的方位分类机制、注意选择机制和注意转移机制.
  • 图  1  双耳听觉神经信息处理系统

    Fig.  1  Neural information processing system of binaural auditory

    图  2  左右耳听觉示意图

    Fig.  2  Illustration of binaural auditory system

    图  3  听觉选择性注意计算模型结构框图

    Fig.  3  Schematic diagram of auditory selective attention computational model

    图  4  方位特征显著性计算框图

    Fig.  4  Diagram of saliency computation about orientation feature

    图  5  人工信号仿真结果

    Fig.  5  Simulation results of the artiflcial signal

    图  6  遗忘因子$\beta $对主导音提取的影响

    Fig.  6  Efiects of forgetting factors on separating spectrogram of leading signals

    图  7  自然声学信号仿真结果

    Fig.  7  Simulation results of natural acoustic signals

    图  8  带噪混合声的仿真结果 (SNR = 10 dB)

    Fig.  8  Simulation results of the mixed signal in noise (SNR = 10 dB)

    图  9  混响环境中混合声的仿真结果

    Fig.  9  Simulation results of the mixed signal in reverberation

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出版历程
  • 收稿日期:  2016-03-18
  • 录用日期:  2016-08-15
  • 刊出日期:  2017-04-01

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