Modeling the Repetition-based Recovering of Acoustic and Visual Sources with Dendritic Neurons

by   Giorgia Dellaferrera, et al.

In natural auditory environments, acoustic signals originate from the temporal superimposition of different sound sources. The problem of inferring individual sources from ambiguous mixtures of sounds is known as blind source decomposition. Experiments on humans have demonstrated that the auditory system can identify sound sources as repeating patterns embedded in the acoustic input. Source repetition produces temporal regularities that can be detected and used for segregation. Specifically, listeners can identify sounds occurring more than once across different mixtures, but not sounds heard only in a single mixture. However, whether such a behaviour can be computationally modelled has not yet been explored. Here, we propose a biologically inspired computational model to perform blind source separation on sequences of mixtures of acoustic stimuli. Our method relies on a somatodendritic neuron model trained with a Hebbian-like learning rule which can detect spatio-temporal patterns recurring in synaptic inputs. We show that the segregation capabilities of our model are reminiscent of the features of human performance in a variety of experimental settings involving synthesized sounds with naturalistic properties. Furthermore, we extend the study to investigate the properties of segregation on task settings not yet explored with human subjects, namely natural sounds and images. Overall, our work suggests that somatodendritic neuron models offer a promising neuro-inspired learning strategy to account for the characteristics of the brain segregation capabilities as well as to make predictions on yet untested experimental settings.


page 3

page 4

page 7

page 9

page 11


Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures

Separating an audio scene into isolated sources is a fundamental problem...

Unsupervised Sound Separation Using Mixtures of Mixtures

In recent years, rapid progress has been made on the problem of single-c...

Bio-NICA: A biologically inspired single-layer network for Nonnegative Independent Component Analysis

Blind source separation, the problem of separating mixtures of unknown s...

Frequency domain TRINICON-based blind source separation method with multi-source activity detection for sparsely mixed signals

The TRINICON ('Triple-N ICA for convolutive mixtures') framework is an e...

Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources

Extraction of latent sources of complex stimuli is critical for making s...

Data-Driven Source Separation Based on Simplex Analysis

Blind source separation (BSS) is addressed, using a novel data-driven ap...

Please sign up or login with your details

Forgot password? Click here to reset