Title :
A brain-like neural network for periodicity analysis
Author :
Voutsas, Kyriakos ; Langner, Gerald ; Adamy, Jürgen ; Ochse, Michael
Author_Institution :
Control Theor. & Robotics Lab., Tech. Univ. Darmstadt, Germany
Abstract :
This paper introduces a brain-like neural model for sound processing. The periodicity analyzing network (PAN) is a bio-inspired neural network of spiking neurons. The PAN consists of complex models of neurons, which can be used for understanding the dynamics of individual neurons and neuronal networks. On a technical level, the PAN is able to compute the ratio of modulation and carrier frequency of harmonic sound signals. The PAN model may, therefore, be used in audio signal processing applications, such as sound source separation, periodicity analysis, and the cocktail party problem.
Keywords :
audio signal processing; brain models; hearing; neural nets; audio signal processing; bio-inspired neural network; brain-like neural network; carrier frequency; cocktail party problem; computational neuroscience; harmonic sound signal; modulation ratio; neuronal network; periodicity analysis; periodicity analyzing network; pitch processing; sound processing; sound source separation; temporal processing; Auditory system; Biological neural networks; Biological system modeling; Biology computing; Biomembranes; Computational modeling; Ear; Frequency; Neurons; Signal analysis; Brain-like systems; computational neuroscience; neuronal modeling; periodicity analysis; pitch processing; spiking neurons; temporal processing; Algorithms; Animals; Auditory Cortex; Auditory Perception; Biological Clocks; Biomimetics; Computer Simulation; Hearing; Humans; Models, Neurological; Nerve Net; Neural Networks (Computer); Periodicity; Sound Spectrography;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2004.837751