2.624

2020影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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

  • [1] 罗跃嘉, 魏景汉.注意的认知神经科学研究.北京:高等教育出版社, 2004. 27-47 http://www.cnki.com.cn/Article/CJFDTOTAL-SYQY201603027.htm

    Luo Yue-Jia, Wei Jin-Han. Attentive Research and Cognitive Neuroscience. Beijing:Higher Education Press, 2004. 27-47 http://www.cnki.com.cn/Article/CJFDTOTAL-SYQY201603027.htm
    [2] Pylkkonen J. Towards Efficient and Robust Automatic Speech Recognition:Decoding Techniques and Discriminative Training[Ph.D. dissertation], Aalto University, Finland, 2013
    [3] 刘文举, 聂帅, 梁山, 张学良.基于深度学习语音分离技术的研究现状与进展.自动化学报, 2016, 42(6):819-833 http://www.aas.net.cn/CN/abstract/abstract18873.shtml

    Liu Wen-Ju, Nie Shuai, Liang Shan, Zhang Xue-Liang. Deep learning based speech separation technology and its developments. Acta Automatica Sinica, 2016, 42(6):819-833 http://www.aas.net.cn/CN/abstract/abstract18873.shtml
    [4] 段艳杰, 吕宜生, 张杰, 赵学亮, 王飞跃.深度学习在控制领域的研究现状与展望.自动化学报, 2016, 42(5):643-654 http://www.aas.net.cn/CN/abstract/abstract18852.shtml

    Duan Yan-Jie, Lv Yi-Sheng, Zhang Jie, Zhao Xue-Liang, Wang Fei-Yue. Deep learning for control:the state of the art and prospects. Acta Automatica Sinica, 2016, 42(5):643-654 http://www.aas.net.cn/CN/abstract/abstract18852.shtml
    [5] Kayser C, Petkov C I, Lippert M, Logothetis N K. Mechanisms for allocating auditory attention:an auditory saliency map. Current Biology, 2005, 15(21):1943-1947 doi: 10.1016/j.cub.2005.09.040
    [6] Kalinli O, Narayanan S S. A saliency-based auditory attention model with applications to unsupervised prominent syllable detection in speech. In:Proceedings of the 8th Annual Conference of the International Speech Communication Association. Antwerp, Belgium:Interspeech, 2007. 1941-1944
    [7] De Coensel B, Botteldooren D. A model of saliency-based auditory attention to environmental sound. In:Proceedings of the 20th International Congress on Acoustics. Sydney, Australia:International Congress on Acoustics, 2010. 1-8
    [8] Kaya E M, Elhilali M. A temporal saliency map for modeling auditory attention. In:Proceedings of the 46th Annual Conference on Information Sciences and Systems. Princeton, USA:IEEE, 2012. 1-6
    [9] 刘扬, 张苗辉, 郑逢斌.听觉选择性注意的认知神经机制与显著性计算模型.计算机科学, 2013, 40(6):283-287 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJA201306065.htm

    Liu Yang, Zhang Miao-Hui, Zheng Feng-Bin. Cognitive neural mechanisms and saliency computational model of auditory selective attention. Computer Science, 2013, 40(6):283-287 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJA201306065.htm
    [10] Bizley J K, Cohen Y E. The what, where and how of auditory-object perception. Nature Reviews Neuroscience, 2013, 14(10):693-707 doi: 10.1038/nrn3565
    [11] Roman N, Wang D L, Brown G J. Speech segregation based on sound localization. The Journal of the Acoustical Society of America, 2003, 114(4):2236-2252 doi: 10.1121/1.1610463
    [12] Friederici A D, Singer W. Grounding language processing on basic neurophysiological principles. Trends in Cognitive Sciences, 2015, 19(6):329-338 doi: 10.1016/j.tics.2015.03.012
    [13] Kayser C, Wilson C, Safaai H, Sakata S, Panzeri S. Rhythmic auditory cortex activity at multiple timescales shapes stimulus-Response gain and background firing. Journal of Neuroscience, 2015, 35(20):7750-7762 doi: 10.1523/JNEUROSCI.0268-15.2015
    [14] 黎万义, 王鹏, 乔红.引入视觉注意机制的目标跟踪方法综述.自动化学报, 2014, 40(4):561-576 http://www.aas.net.cn/CN/abstract/abstract18323.shtml

    Li Wan-Yi, Wang Peng, Qiao Hong. A survey of visual attention based methods for object tracking. Acta Automatica Sinica, 2014, 40(4):561-576 http://www.aas.net.cn/CN/abstract/abstract18323.shtml
    [15] Henry M J, Herrmann B, Obleser J. Selective attention to temporal features on nested time scales. Cerebral Cortex, 2015, 25(2):450-459 doi: 10.1093/cercor/bht240
    [16] Wang W J, Wu X H, Li L. The dual-pathway model of auditory signal processing. Neuroscience Bulletin, 2008, 24(3):173-182. doi: 10.1007/s12264-008-1226-8
    [17] Qu T, Xiao Z, Gong M. Distance-dependent head-related transfer functions measured with high spatial resolution using a spark gap. IEEE Transactions on Audio, Speech, and Language Processing, 2009, 17(6):1124-1132 doi: 10.1109/TASL.2009.2020532
    [18] Cheng C I, Wakefield G H. Introduction to head-related transfer functions (HRTFs):representations of HRTFs in time, frequency, and space. In:Proceedings of the 107th Convention of the Audio-Engineering-Society. Ann Arbor, USA:University of Michigan, 2001. 231-248
    [19] Zhang J P, Nakamoto K T, Kitzes L M. Modulation of level response areas and stimulus selectivity of neurons in cat primary auditory cortex. Journal of Neurophysiology, 2005, 94(4):2263-2274 doi: 10.1152/jn.01207.2004
    [20] Jin C, Schenkel M, Carlile S. Neural system identification model of human sound localization. The Journal of the Acoustical Society of America, 2000, 108(3):1215-1235 doi: 10.1121/1.1288411
    [21] Algazi V R, Duda R O, Thompson D M, Avendano C. The CIPIC HRTF database. In:Proceedings of the 2009 IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics. New Platz, USA:IEEE, 2001. 99-102
    [22] Catic J, Santurette S, Dau T. The role of reverberation-related binaural cues in the externalization of speech. The Journal of the Acoustical Society of America, 2015, 138(2):1154-1167 doi: 10.1121/1.4928132
    [23] Hassager H G, Gran F, Dau T. The role of spectral detail in the binaural transfer function on perceived externalization in a reverberant environment. The Journal of the Acoustical Society of America, 2016, 139(5):2992-3000 doi: 10.1121/1.4950847
  • 加载中
图(9)
计量
  • 文章访问数:  1509
  • HTML全文浏览量:  317
  • PDF下载量:  696
  • 被引次数: 0
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

目录

    /

    返回文章
    返回