Secure voice signature communications system

Abstract

Embodiments of the present invention provides a system and a method for connecting two or more parts of a distributed and spatio-temporal spiking neural network by a means of communication, such as the Internet, used for recognizing and identifying acoustic signals using acoustic signature recognition by means of a spatio-temporal neural network. The first artificial intelligent device identifies features in a series of spatio-temporal pulse streams received from an artificial cochlear, and learns to respond to the pulse streams. The features of the pulse stream identifying an event learned by the first artificial intelligent device are transmitted to the remote artificial intelligent device over a communication protocol via a Series Address Event Representation bus, where the remote artificial intelligent device learns to respond. Further, a computing device may be connected to the remote artificial intelligent device for analyzing and controlling one or more appliances from anywhere in the world.

We claim: 
     
         1 . An apparatus for identifying and learning an acoustic signature of a plurality of auditory signals comprising:
 an input sensor configured to capture a varying potential produced in the plurality of auditory signals from an auditory source;   a series of resonators configured to convert the varying potential into a plurality of streams of electrical pulses with spatio-temporal distribution;   an artificial intelligent device identifies one or more features of the pulse streams of acoustic signals, representing the acoustic signature, by association in a dynamic spatio-temporal neural network; and   the artificial intelligent device learns to respond to the acoustic signature of the acoustic signals by modifying synaptic strengths in the dynamic spatio-temporal neural network.   
     
     
         2 . The apparatus as claimed in  claim 1 , wherein the streams of electrical pulses with spatio-temporal distribution are indicative of frequency and amplitude over time of the plurality of pulse streams. 
     
     
         3 . The apparatus as claimed in  claim 1 , wherein the learning of the artificial intelligent device is through the modification of synaptic strengths by Synaptic Time Dependent Plasticity and feedback from Post Synaptic Neuron. 
     
     
         4 . The apparatus as claimed in  claim 1 , wherein the artificial intelligent device is further configured to learn to respond to at least one acoustic signature for a particular each one of a plurality of captured acoustic waveforms. 
     
     
         5 . The apparatus as claimed in  claim 1 , wherein the learning function can be mitigated by applying a value that represents a Neuromodulator in the dynamic spatio-temporal neural network of the artificial intelligent device. 
     
     
         6 . The apparatus as claimed in  claim 1 , wherein the series of resonators is comprised in an artificial cochlear. 
     
     
         7 . The apparatus as claimed in  claim 1 , wherein the auditory source may be a human speech 
     
     
         8 . A communication system comprising:
 a first artificial intelligent device consisting of a dynamic spatio-temporal neural network configured to receive and learn from a plurality of pulse streams of acoustic signals with spatio-temporal distribution; and   atleast one remote artificial intelligent device consisting of a dynamic spatio-temporal neural network communicating with the first artificial intelligent device, through a communication channel, configured to receive and respond to the pulse streams.   
     
     
         9 . The communication system as claimed in  claim 8 , wherein the first artificial intelligent device and the remote artificial intelligent device comprise spatio-temporal spiking neural network and dynamic synapse circuits configured to process the plurality of parallel streams of pulses. 
     
     
         10 . The communication system as claimed in  claim 8 , wherein the first artificial intelligent device is configured to:
 receive a plurality of pulse streams of acoustic signals with spatio-temporal distribution, representing an acoustic signature;   identify one or more features of the pulse streams, by association in a dynamic spatio-temporal neural network; and   learn to respond to the pulse streams of acoustic signals by modifying synaptic strengths in the dynamic spatio-temporal neural network.   
     
     
         11 . The communication system as claimed in  claim 8 , wherein the first artificial intelligent device communicates with the remote artificial intelligent device through the communication protocol via a Series Address Event Representation bus. 
     
     
         12 . The communication system as claimed in  claim 8 , wherein the first artificial intelligent device transmits plurality of spike times or pulse streams, originating neuron and destinations as packets of information, and receives feedback from the remote artificial intelligent device as packets of information. 
     
     
         13 . The communication system as claimed in  claim 8 , wherein the communication protocol for transmission of the packets of information can be any established standard, includes PCI (Peripheral Component Interconnect), PCIe (PCI express), USB (Universal Serial Bus), or TCP (Transmission Control Protocol). 
     
     
         14 . The communication system as claimed in  claim 8 , wherein the first artificial intelligent device receives plurality of pulse streams of acoustic signals with spatio-temporal distribution from an artificial cochlear, which further receives a varying potential in pulse streams from an input sensor configured to capture the varying potential in the pulse stream of the acoustic signals. 
     
     
         15 . The communication system as claimed in  claim 8 , wherein the remote artificial intelligent device is configured to communicate with a computing device that is intended to control one or more appliance. 
     
     
         16 . A method for identifying and learning acoustic signature of a plurality of auditory signals comprising:
 capturing, by an input sensor, a varying potential produced in the plurality of auditory signals from an auditory source;   converting the varying potential into a plurality of streams of electrical pulses with spatio-temporal distribution, by a series of resonators;   identifying, by an artificial intelligent device, one or more features of the pulse streams of acoustic signals, representing the acoustic signature, through association in a dynamic spatio-temporal neural network; and   learning, by the artificial intelligent device, to respond to the acoustic signature of the acoustic signals by modifying synaptic strengths in the dynamic spatio-temporal neural network.   
     
     
         17 . The method as claimed in  claim 16  further comprising transmitting said one or more features of the pulse streams of acoustic signals representing the acoustic signature from the artificial intelligent device to a remote artificial intelligent device and receiving feedback over a communication channel. 
     
     
         18 . The method as claimed in  claim 17 , the communication channel includes PCI (Peripheral Component Interconnect), PCIe (PCI express), USB (Universal Serial Bus), or TCP (Transmission Control Protocol). 
     
     
         19 . The method as claimed in  claim 17  further comprising communicating of the remote artificial intelligent device with a computing device for analyzing and controlling one or more appliances. 
     
     
         20 . The method as claimed in  claim 16 , wherein the first artificial intelligent device and the remote artificial intelligent device comprises a spatio-temporal neural network and dynamic synapse circuits configured to process parallel streams of pulses. 
     
     
         21 . The method as claimed in  claim 16 , wherein the streams of electrical pulses with spatio-temporal distribution are indicative of frequency and amplitude over time of the plurality of pulse streams. 
     
     
         22 . The method as claimed in  claim 16 , wherein the learning of the artificial intelligent device is through the modification of synaptic strengths by Synaptic Time Dependent Plasticity and feedback from Post Synaptic Neuron. 
     
     
         23 . The method as claimed in  claim 16 , wherein the remote artificial intelligent device identifies the pulse streams and learns to respond in the same manner as the artificial intelligent device.