Binaural lateral localization of multiple sources in real environments using a kurtosis-driven split-EM algorithm. (March 2018)

• Record Type: Journal Article
• Authors: Reche-Lopez, P.; Perez-Lorenzo, J.M.; Rivas, F.; Viciana-Abad, R.
• Is Part Of: Engineering applications of artificial intelligence. Volume 69(2017:Sep.)
• Page Start: 137
• Page End: 146
• Publication Date: 2018-03
• Subjects: Robot audition -- Multiple sound localization -- Laplacian model mixture -- KDS-EM -- Mutational split; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.12.013
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this work a method for an unsupervised lateral localization of simultaneous sound sources is presented. Following a binaural approach, the kurtosis-driven split-EM algorithm (KDS-EM) implemented is able to estimate the direction of arrival of relevant sound sources without knowing a priori their number. Information about the localization is integrated within a period of observation time to serve as an auditory memory in the context of social robotics. Experiments have been conducted using two types of observation times, one shorter with the purpose of analyzing its performance in a reactive level, and other longer that allows the analysis of its contribution as an input of the building process of the sorroundings auditory models that servesto drive a more deliberative behavior. The system has been tested in real and reverberant environments, achieving a good performance based on an over-modeling process that is able to isolate the location of the relevant sources from adverse acoustic effects, such as reverberations.