Automatic performance optimization for perceptual devices

Abstract

Systems and methods may be used to modify a controllable stimulus generated by a digital audio device in communication with a human user. An input signal is provided to the digital audio device. In turn, the digital audio device sends a stimulus based on that input signal to the human user, who takes an action, usually in the form of an output signal, to characterize the stimulus that the user receives, based on the user's perception. An algorithm then determines a difference between the input signal and the output signal, and a perceptual model is constructed based at least in part on the difference. Thereafter, a new value for the parameter of the digital audio device is suggested based at least in part on the perceptual model. This process continues iteratively until the algorithm finds the user's optimal device parameters. While this process is highly complex and time consuming, the algorithm successfully reaches at least a near optimal settings, if not the optimal, in a short time.

What is claimed is: 
     
         1 . A system for modifying a controllable stimulus generated by a digital audio device in communication with a human user, the system comprising:
 a test set generator for generating a test set for the digital audio device, the digital audio device sending a stimulus to the human user, the stimulus defined at least in part by a parameter, the parameter comprising a value;   a signal receiver for receiving an output signal from the human user, the output signal based at least in part on a perception of the stimulus by the human user;   a perceptual model module receiving and storing information concerning the output signal from the signal receiver; and   a parameter value generator for applying at least one algorithm to the information and modifying a parameter value based at least in part on the application of the algorithm to the information, and wherein the at least one algorithm comprises at least one of a uniform sampling algorithm, a neighborhood sampling algorithm, an average sampling algorithm, and a predefined criteria algorithm.   
     
     
         2 . The system of  claim 1 , wherein the perceptual model comprises a surface model. 
     
     
         3 . The system of  claim 1  wherein the at least one algorithm comprises a plurality of algorithms. 
     
     
         4 . The system of  claim 1  wherein the at least one algorithm is a uniform sampling algorithm, and the parameter value generator comprises:
 a point identification module for identifying a plurality of sampled points; 
 an area identification module for identifying a plurality of projected areas, wherein each projected area is characterized by an absence of any of the plurality of sampled points; 
 a projected point identification module for identifying a projected point associated with a first projected area so as to reduce the size of an unsampled area for subsequent iterations. 
 
     
     
         5 . The system of  claim 4  wherein the first projected area comprises an area greater than an area of each of the plurality of projected areas. 
     
     
         6 . The system of  claim 4  wherein the projected areas are circles. 
     
     
         7 . The system of  claim 6  wherein the projected points are the centers of the circles. 
     
     
         8 . The system of  claim 1  wherein the algorithm comprises a neighborhood sampling algorithm and the parameter value generator comprises:
 a first point identification module for identifying a first sampled point; 
 a second point identification module for identifying a second sampled point on a first line from the second sampled point to the first sampled point; and 
 an projected point identification module for identifying at least one of (i) a projected point on the first line proximate the first sampled point and (ii) a null point condition. 
 
     
     
         9 . The system of  claim 8 , wherein the first point identification module identifies the first sampled point from a plurality of sampled points. 
     
     
         10 . The system of  claim 11 , wherein the first point identification module identifies the first sampled point by assigning a confidence to at least one of the plurality of sampled points. 
     
     
         11 . The system of  claim 8  wherein the first line has a slope describing a downward direction. 
     
     
         12 . The system of  claim 11  wherein the first line has a maximum downward slope. 
     
     
         13 . The system of  claim 12 , wherein the maximum downward slope is relative to a third sampled point located on a second line from the third sampled point to the first sampled point. 
     
     
         14 . The system of  claim 1  wherein the algorithm comprises an average sampling algorithm and the parameter value generator comprises:
 a mean error calculator for computing a mean error of each of a sampled value of a plurality of parameters; 
 a sorter for sorting the sampled value of each of the plurality of parameters based at least in part on the mean error; 
 a point selector for selecting at least one of (i) a projected point comprising a first predetermined mean error of a parameter and (ii) a projected point comprising a second predetermined mean error. 
 
     
     
         15 . The system of  claim 14  wherein the sorter sorts the sampled values in ascending order. 
     
     
         16 . The system of  claim 14  wherein the first predetermined mean error is a minimum error. 
     
     
         17 . The system of  claim 14 , wherein the second predetermined error comprises a next best point. 
     
     
         18 . The system of  claim 1  wherein the algorithm comprises a predefined criteria algorithm. 
     
     
         19 . The system of  claim 18  wherein the predefined criteria algorithm comprises a characterizing algorithm that characterizes the user based on a predefined criterion.