Attention-driven auditory stream segregation using a SOM coupled with an excitatory-inhibitory ANN

Auditory attention is an essential property of human hearing. It is responsible for the selection of information to be sent to working memory and as such to be perceived consciously, from the abundance of auditory information that is continuously entering the ears. Thus, auditory attention heavily influences human auditory perception and systems simulating human auditory scene analysis would benefit from an attention model. In this paper, a human-mimicking model of auditory attention is presented, aimed to be used in environmental sound monitoring. It relies on a Self-Organizing Map (SOM) for learning and classifying sounds. Coupled to this SOM, an excitatory-inhibitory artificial neural network (ANN), simulating the auditory cortex, is defined. The activation of these neurons is calculated based on an interplay of various excitatory and inhibitory inputs. The latter simulate auditory attention mechanisms in a human-inspired but simplified way, in order to keep the computational cost within bounds. The behavior of the model incorporating all of these mechanisms is investigated, and plausible results are obtained.

[1]  S. David,et al.  Auditory attention : focusing the searchlight on sound , 2007 .

[2]  Guy J. Brown,et al.  A computational model of auditory selective attention , 2004, IEEE Transactions on Neural Networks.

[3]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[4]  DeLiang Wang,et al.  Locally excitatory globally inhibitory oscillator networks , 1995, IEEE Transactions on Neural Networks.

[5]  Hugo Fastl,et al.  Psychoacoustics: Facts and Models , 1990 .

[6]  Bernard De Baets,et al.  Soundscape analysis by means of a neural network-based acoustic summary , 2011 .

[7]  Bert De Coensel,et al.  A model of saliency-based auditory attention to environmental sound , 2010 .

[8]  Michael T. Lippert,et al.  Mechanisms for Allocating Auditory Attention: An Auditory Saliency Map , 2005, Current Biology.

[9]  Dick Botteldooren,et al.  A model for the perception of environmental sound based on notice-events. , 2009, The Journal of the Acoustical Society of America.

[10]  Shrikanth S. Narayanan,et al.  Prominence Detection Using Auditory Attention Cues and Task-Dependent High Level Information , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[11]  Bernard De Baets,et al.  Context-dependent environmental sound monitoring using SOM coupled with LEGION , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[12]  E. Knudsen.  Fundamental components of attention. , 2007, Annual review of neuroscience.

[13]  William M. Hartmann,et al.  Psychoacoustics: Facts and Models , 2001 .

[14]  Bert De Coensel,et al.  A model for long-term environmental sound detection , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[15]  Jessica M. Foxton,et al.  Effects of attention and unilateral neglect on auditory stream segregation. , 2001, Journal of experimental psychology. Human perception and performance.

[16]  S. Shamma,et al.  Interaction between Attention and Bottom-Up Saliency Mediates the Representation of Foreground and Background in an Auditory Scene , 2009, PLoS biology.

[17]  Albert S. Bregman,et al.  The Auditory Scene. (Book Reviews: Auditory Scene Analysis. The Perceptual Organization of Sound.) , 1990 .

[18]  G. Kramer.  Auditory Scene Analysis: The Perceptual Organization of Sound by Albert Bregman (review) , 2016 .