* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Quasi-isometric Representation of Three Dimensional
Survey
Document related concepts
History of anthropometry wikipedia , lookup
Holonomic brain theory wikipedia , lookup
Development of the nervous system wikipedia , lookup
Metastability in the brain wikipedia , lookup
Artificial neural network wikipedia , lookup
Neural coding wikipedia , lookup
Synaptic gating wikipedia , lookup
Biological neuron model wikipedia , lookup
Neural modeling fields wikipedia , lookup
Nervous system network models wikipedia , lookup
Central pattern generator wikipedia , lookup
Catastrophic interference wikipedia , lookup
Hierarchical temporal memory wikipedia , lookup
Convolutional neural network wikipedia , lookup
Transcript
Voice Recognition by a Realistic Model of Biological Neural Networks by Efrat Barak Supervised by Karina Odinaev Igal Raichelgauz Structure • • • • • Project Objective The Model The Classification Process Results & Analysis Conclusion Project Objective Configure a neural network based system for voice recognition The Model The Main Principle The readout function recognizes the basin that the network has converged to, and classifies the input according to the indicator of that basin Correspondence with the Theory of Attractor Neural Networks • The system converges to a basin • The basins are periodic attractors Correspondence with the LSM theory • The neural network may be treated as a liquid • The readout function receives only the current state of the liquid and transforms it to an output signal • The system can perform several tasks simultaneously Neural Network Structure • 22 Input Neurons • 135 spiking neurons in a 3x3x15 formation • LIF model for neurons behavior • 20% of the neurons are inhibitory and 80% of them are excitatory • Dynamic synapses Creating the Stimulus 30 seconds of recorded speech are encoded into 1 second of spike trains, in the following methods: • Time Encoding – A straight forward conversion Creating the Stimulus • Mel Frequency Cepstral Coefficients (MFCCs) encoding - In this method the frequency bands are positioned logarithmically, on the mel scale. A periodic spikes train is added to the second of the voice segment. Performing a Simulation • A new network is created • A stimulus of one speech segment is fed to the network, followed by a periodic driving force (Repeated for every combination of segment and frequency). • The basins are categorized by their activity vector. The Classification Process The Indicators Map • N w (b) - The number of segments of the wanted voice that converged to the basin b. • NU (b) - The number of segments of the unwanted voice that converged to the basin b. • N (b) - The total number of initials that converged to the basin b. The Indicators Map 100 W (b) Nw RA 100 U (b) Nu RB The indicator of basin b: W ( b) U ( b) S (b) N The Indicators Map Examples: The Indicators Map The Indicators Map Indicators’ Average: The Classification Process Tuning Step 1. Select frequencies Tuning Preceding to Step 2. Why do we need a threshold? Tuning Step 2. Determine the threshold The Classification Process Results – Amplitude Encoded Input Input Examples Wanted Voice Unwanted Voice Results – Amplitude Encoded Input Results of a verification test Results – Amplitude Encoded Input Results of a Classification Test Input Classified as Our Classification True Classification Wanted Wanted 71% 100% Wanted Unwanted 29% 0% Unwanted Wanted 55.9% 0% Unwanted Unwanted 44.1% 100% Results – Amplitude Encoded Input Results of Classification by Two Different Systems Input Classified as System 1 System 2 True Classification Wanted Wanted 71% 94% 100% Wanted Unwanted 29% 6% 0% Unwanted Wanted 55.9% 61.23% 0% Unwanted Unwanted 44.1% 38.77% 100% Results – Amplitude Encoded Input Cross Classification Results – Amplitude Encoded Input Results of cross classification for systems 1 and 2: 50.2% Answered , 49.8% Unanswered Input Classified as System 1 System 2 Cross Classification Wanted Wanted 71% 94% 97.1% Wanted Unwanted 29% 6% 2.9% Unwanted Wanted 55.9% 61.23% 66.5% Unwanted Unwanted 44.1% 38.77% 33.5% Results – MFCC Encoded Input Input Examples Wanted Voice Unwanted Voice Results – MFCC Encoded Input Results of a classification test Two sets of new data were used Classified as Test I Segments: 100 wanted, 400 unwanted Test II Segments: 30 wanted, 30 unwanted Wanted Wanted (Hit) 87% 86.8% Wanted Unwanted (Miss-Hit) 13% 13.2% Unwanted Wanted (False Alarm) 55.3% 45% Unwanted Unwanted (Hit) 44.7% 55% True Classification Results – MFCC Encoded Input Input Classification f=18Hz, th=0.3 f=18Hz, th=0 f=18Hz, th=-0.12 f=18Hz, th=-0.2 Data set 3 Segments: Wanted (Hit) 58% 87% 96% 100% 100 wanted Unwanted (Miss-Hit) 42% 13% 4% 0% 400 unwanted Wanted (False Alarm) 32.2% 55.3% 77.5% 93.75% Unwanted (Hit) 67.8% 44.7% 22.5% 6.25% Data set 4 Segments: Wanted (Hit) 47.3% 86.8% 97% 100% 30 wanted Unwanted (Miss-Hit) 52.7% 13.2% 2.6% 0% 30 unwanted Wanted (False Alarm) 17.5% 45% 82.5% 92.5% Unwanted (Hit) 82.5% 55% 17% 7% Basins Creation Pattern (a) 324 initials (b) 100 initials (c) 60 initials Conclusion • A system for voice recognition, based on neuro-computations, was designed • The system succeeded in recognizing the wanted voice when the input was encoded by its amplitude. Conclusion • The MFCC method yielded very different inputs, therefore the ability of the system to recognize such input was proven partially. • The system’s stability was proved Suggestions for Future Projects • Prepare the system for various types of inputs • Perform automatic tuning by using statistical tools • Prove that the system can perform several tasks simultaneously THE END