Download A Bio-Inspired Sound Source Separation Technique Based

A Bio-Inspired Sound Source Separation Technique Based On a Spiking
Neural Network: Application to three-source sounds.
Ramin Pichevar and Jean Rouat
A sound source separation technique based on a two-layered bio-inspired spiking neural
network is proposed. One of the two bio-inspired proposed spectral maps (Cochleotopic /
AMtopic or Cochleotopic / Spectrotopic) is used as a front-end to the neural network
depending on the nature of the intruding sound. These two-dimensional maps try to
mimic partially the auditory pathway. The building blocks of the neural network are
oscillatory relaxation neurons. We will show that the behavior of the more popular
integrate-and-fire neurons are an approximation of the latter-mentioned neurons. The
separation of different sound sources is based on the synchronization of neurons in the
second layer. Each neuron in the second layer is associated to a cochlear channel (a total
of 256 channels in our experiments). An enhanced version of the gammatone
analysis/synthesis filterbank is used to generate the cochlear channels.
In our previous works, two-source sound separation had been considered. In this work,
the application of the technique to three-source sounds will be considered and will be
compared to other techniques proposed in the literature and to our previous works. The
Log-Spectral Distortion (LSD) criterion will be used to compare performance. We will
also compare different performance criteria like LSD, PEL (Percentage of Energy Loss),
PNR (Percentage of Noise Residue), and SNR (Signal-to-Noise Ratio). We will
mathematically show (by using counter- examples) that these criteria don’t always reflect
the qualitative behavior of separation results.