Patent Publication Number: US-7910820-B2

Title: Information processing apparatus and method, program, and record medium

Description:
CROSS REFERENCES TO RELATED APPLICATIONS 
     The present invention contains subject matter related to Japanese Patent Application JP 2006-286261 filed in the Japanese Patent Office on Oct. 20, 2006, and Japanese Patent Application JP 2006-296143 filed in the Japanese Patent Office on Oct. 31, 2006, the entire contents of which being incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to an information processing apparatus and method, a program, and a record medium, in particular, to those that allow a characteristic of data to be extracted. 
     2. Description of the Related Art 
     Techniques of processing information in a predetermined manner and extracting a characteristic quantity that represents a characteristic of the data therefrom are well known. In these techniques, a characteristic quantity that represents a characteristic of a predetermined region of data that chronologically continuous may be extracted. 
     In a related art reference, during the execution of an information process that uses the result of a sound recognition process, a target executed for the sound recognition process is changed. The settings of the sound recognition environment of the sound recognition process are changed according to the target. Thereafter, the sound recognition process is executed for the changed target according to the changed settings (for example, see Japanese Patent Application Laid-Open No. 2005-195834). 
     SUMMARY OF THE INVENTION 
     However, when data are pre-divided into regions and a characteristic of each region is extracted, it would be difficult to consider the influence of the preceding region (or regions) against the current region. 
     When the resolution of a characteristic quantity to be finally obtained is tried to be increased, it would be necessary to increase the overlap of data to be divided. As a result, the processing quantity would increase in proportion to the resolution. 
     When data are input in real time, since the process is performed whenever a predetermined quantity of data has been stored, the more complicated an algorithm that extracts a characteristic from the data is, the longer the time lag after data are input until a characteristic quantity is finally obtained would become. 
     In other words, the time lag (latency) after data are input until a characteristic quantity to be finally obtained is output is obtained by the sum of the time for which data for a region are input and the time for which the data are processed. Thus, the more complicated the algorithm that extracts a characteristic from data is, the longer the time necessary to process the data, namely the longer the time lag (latency) would become. 
     In addition, when a characteristic is directly extracted from data that represent a continuous quantity, it would be necessary to design a dedicated model and much teacher data used to learn parameters of a characteristic extracting device. In the related art, a general purpose characteristic extracting device was not used. In addition, parameters were not learnt with a small quantity of teacher data. 
     In view of the foregoing, it would be desirable to provide a technique that allows a characteristic of data to be easily and quickly extracted. 
     According to an embodiment of the present invention, there is provided an information processing apparatus. The information processing apparatus includes an analyzing section, a continuous characteristic quantity extracting section, a cutting section, a regional characteristic quantity extracting section, and a target characteristic quantity estimating section. The analyzing section chronologically continuously analyzes sound data which chronologically continue in each of predetermined frequency bands. The continuous characteristic quantity extracting section extracts a continuous characteristic quantity which is a characteristic quantity which chronologically continues from an analysis result of the analyzing section. The cutting section cuts the continuous characteristic quantity into regions each of which has a predetermined length. The regional characteristic quantity extracting section extracts a regional characteristic quantity which is a characteristic quantity represented by one scalar or vector from each of the regions into which the continuous characteristic quantity has been cut. The target characteristic quantity estimating section estimates a target characteristic quantity which is a characteristic quantity which represents one characteristic of the sound data from each of the regional characteristic quantities. 
     The target characteristic quantity estimating section may be pre-created by learning teacher data composed of sound data which chronologically continue and a characteristic quantity which represents one correct characteristic of sound data in each of the regions into which the continuous characteristic quantity has been cut. 
     The analyzing section may chronologically continuously analyze the sound data which chronologically continue as sounds of musical intervals of 12 equal temperaments of each octave. The continuous characteristic quantity extracting section may extract the continuous characteristic quantity from data which have been obtained as an analysis result of the analyzing section and which represent energies of the musical intervals of the 12 equal temperaments of each octave. 
     The target characteristic quantity estimating section may estimate the target characteristic quantity which identifies music or talk as a characteristic of the sound data. 
     The information processing apparatus may also include a smoothening section which smoothens the target characteristic quantities by obtaining a moving average thereof. 
     The information processing apparatus may also include a storing section which adds a label which identifies a characteristic represented by the estimated target characteristic quantity to the sound data and stores the sound data to which the label has been added. 
     The information processing apparatus may also include an algorithm creating section which creates an algorithm which extracts the continuous characteristic quantity from the sound data which chronologically continue according to GA (Genetic Algorithm) or GP (Genetic Programming). 
     According to an embodiment of the present invention, there is provided an information processing method. Sound data which chronologically continue are chronologically continuously analyzed in each of predetermined frequency bands. A continuous characteristic quantity which is a characteristic quantity which chronologically continues is extracted from the analysis result. The continuous characteristic quantity is cut into regions each of which has a predetermined length. A regional characteristic quantity which is a characteristic quantity represented by one scalar or vector is extracted from each of the regions into which the continuous characteristic quantity has been cut. A target characteristic quantity which is a characteristic quantity which represents one characteristic of the sound data is estimated from each of the regional characteristic quantities. 
     According to an embodiment of the present invention, there is provided a program which is executed by a computer. Sound data which chronologically continue are chronologically continuously analyzed in each of predetermined frequency bands. A continuous characteristic quantity which is a characteristic quantity which chronologically continues is extracted from the analysis result. The continuous characteristic quantity is cut into regions each of which has a predetermined length. A regional characteristic quantity which is a characteristic quantity represented by one scalar or vector is extracted from each of the regions into which the continuous characteristic quantity has been cut. A target characteristic quantity which is a characteristic quantity which represents one characteristic of the sound data is estimated from each of the regional characteristic quantities. 
     According to an embodiment of the present invention, there is provided a record medium on which a program which is executed by a computer has been recorded. Sound data which chronologically continue are chronologically continuously analyzed in each of predetermined frequency bands. A continuous characteristic quantity which is a characteristic quantity which chronologically continues is extracted from the analysis result. The continuous characteristic quantity is cut into regions each of which has a predetermined length. A regional characteristic quantity which is a characteristic quantity represented by one scalar or vector is extracted from each of the regions into which the continuous characteristic quantity has been cut. A target characteristic quantity which is a characteristic quantity which represents one characteristic of the sound data is estimated from each of the regional characteristic quantities. 
     According to an embodiment of the present invention, sound data which chronologically continue are chronologically continuously analyzed in each of predetermined frequency bands. The continuous characteristic quantity which is a characteristic quantity which chronologically continues is extracted from the analysis result. The continuous characteristic quantity is cut into regions each of which has a predetermined length. A regional characteristic quantity which is a characteristic quantity represented by one scalar or vector is extracted from each of the regions into which the continuous characteristic quantity has been cut. A target characteristic quantity which is a characteristic quantity which represents one characteristic of the sound data is estimated from each of the regional characteristic quantities. 
     According to an embodiment of the present invention, a characteristic can be extracted from data. 
     According to an embodiment of the present invention, a characteristic can be easily and quickly extracted from data. 
     These and other objects, features and advantages of the present invention will become more apparent in light of the following detailed description of a best mode embodiment thereof, as illustrated in the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The invention will become more fully understood from the following detailed description, taken in conjunction with the accompanying drawings, wherein similar reference numerals denote similar elements, in which: 
         FIG. 1  is a schematic diagram describing that a characteristic is obtained from each portion having a predetermined length of continuous data; 
         FIG. 2  is a block diagram showing a structure of an information processing apparatus according to an embodiment of the present invention; 
         FIG. 3  is a flow chart describing a process of extracting a target characteristic quantity; 
         FIG. 4  is a schematic diagram describing that a continuous characteristic quantity is extracted; 
         FIG. 5  is a schematic diagram describing that a continuous characteristic quantity is cut; 
         FIG. 6  is a schematic diagram describing that a regional characteristic quantity is extracted; 
         FIG. 7  is a schematic diagram describing that a target characteristic quantity is estimated; 
         FIG. 8  is a schematic diagram describing that it is determined whether sound data are music or talk at intervals of a unit time; 
         FIG. 9  is a block diagram showing another structure of an information processing apparatus according to an embodiment of the present invention; 
         FIG. 10  is a flow chart describing a process of adding a label to sound data; 
         FIG. 11  is a schematic diagram describing time-musical interval data; 
         FIG. 12  is a schematic diagram describing that a continuous music characteristic quantity is extracted from time-musical interval data; 
         FIG. 13  is a schematic diagram describing that a continuous music characteristic quantity is cut; 
         FIG. 14  is a schematic diagram describing that a regional characteristic quantity is extracted; 
         FIG. 15  is a schematic diagram describing that it is determined whether a frame is music or talk; 
         FIG. 16  is a schematic diagram describing that determination results of whether each frame is music or talk are smoothened; 
         FIG. 17  is a schematic diagram showing exemplary sound data to which labels have been added; 
         FIG. 18  is a schematic diagram describing an outline of a process of an algorithm creating section; 
         FIG. 19  is a schematic diagram describing an outline of a process of the algorithm creating section; 
         FIG. 20  is a schematic diagram describing an outline of a process of the algorithm creating section; 
         FIG. 21  is a block diagram showing a functional structure of the algorithm creating section; 
         FIG. 22  is a flow chart describing an algorithm creating process; 
         FIG. 23  is a schematic diagram describing an exemplary algorithm creating process; 
         FIG. 24  is a schematic diagram describing that a process represented by a gene is executed; 
         FIG. 25  is a schematic diagram describing that a gene is evaluated; and 
         FIG. 26  is a block diagram showing an exemplary structure of a personal computer. 
     
    
    
     DESCRIPTION OF PREFERRED EMBODIMENTS 
     Next, embodiments of the present invention will be described. The relationship between constituents of the present invention and embodiments described in this specification of the present invention is as follows. The description in this section denotes that embodiments that support the invention set forth in the specification are described in this specification. Thus, even if some embodiments are not described in this section as embodiments corresponding to constituents of the present invention, it is not implied that the embodiments do not correspond to the constituents. Conversely, even if embodiments are described as constituents in this section, it is not implied that these embodiments do not correspond to other than the constituents. 
     According to an embodiment of the present invention, an information processing apparatus includes an analyzing section (for example, an time-musical interval analyzing section  81  shown in  FIG. 9 ), a continuous characteristic quantity extracting section (for example, a continuous music characteristic quantity extracting section  82  shown in  FIG. 9 ), a cutting section (for example, a frame cutting section  83  shown in  FIG. 9 ), a regional characteristic quantity extracting section (for example, a regional characteristic quantity extracting section  84  shown in  FIG. 9 ), and a target characteristic quantity estimating section (for example, a music/talk determining section  85  shown in  FIG. 9 ). The analyzing section chronologically continuously analyzes sound data which chronologically continue in each of predetermined frequency bands. The continuous characteristic quantity extracting section extracts a continuous characteristic quantity which is a characteristic quantity which chronologically continues from an analysis result of the analyzing section. The cutting section cuts the continuous characteristic quantity into regions each of which has a predetermined length. The regional characteristic quantity extracting section extracts a regional characteristic quantity which is a characteristic quantity represented by one scalar or vector from each of the regions into which the continuous characteristic quantity has been cut. The target characteristic quantity estimating section estimates a target characteristic quantity which is a characteristic quantity which represents one characteristic of the sound data from each of the regional characteristic quantities. 
     The information processing apparatus may also include a smoothening section (for example, a data smoothening section  86  shown in  FIG. 9 ) which smoothens the target characteristic quantities by obtaining a moving average thereof. 
     The information processing apparatus may also include a storing section (for example, a sound storing section  87  shown in  FIG. 9 ) which adds a label which identifies a characteristic represented by the estimated target characteristic quantity to the sound data and storing the sound data to which the label has been added. 
     The information processing apparatus may also include an algorithm creating section (for example, a algorithm creating section  101  shown in  FIG. 18 ) which creates an algorithm which extracts the continuous characteristic quantity from the sound data which chronologically continue according to GA (Genetic Algorithm) or GP (Genetic Programming). 
     According to embodiments of the present invention, in an information processing method and a program, sound data which chronologically continue are chronologically continuously analyzed in each of predetermined frequency bands (for example, at step S 51  shown in  FIG. 10 ). A continuous characteristic quantity which is a characteristic quantity which chronologically continues is extracted from the analysis result (for example, at step S 52  shown in  FIG. 10 ). The continuous characteristic quantity is cut into regions each of which has a predetermined length (for example, at step S 53  shown in  FIG. 10 ). A regional characteristic quantity which is a characteristic quantity represented by one scalar or vector is extracted from each of the regions into which the continuous characteristic quantity has been cut (for example, at step S 54  shown in  FIG. 10 ). A target characteristic quantity which is a characteristic quantity which represents one characteristic of the sound data is estimated from each of the regional characteristic quantities (for example, at step S 55  shown in  FIG. 10 ). 
     First, as shown in  FIG. 1 , a technique of applying an automatic characteristic extracting algorithm to continuous data that are chronologically continuous data and obtaining a characteristic at intervals of a predetermined length from the continuous data according to the algorithm will be described. For example, a characteristic that is one of A, B, and C is obtained at intervals of a predetermined length from continuous data that are continuously input as for example waveform data. 
       FIG. 2  is a block diagram showing a structure of an information processing apparatus  11  according to an embodiment of the present invention. The information processing apparatus  11  extracts a characteristic at intervals of a predetermined length from continuous data. The information processing apparatus  11  is composed of a continuous characteristic quantity extracting section  31 , a continuous characteristic cutting section  32 , a regional characteristic quantity extracting section  33 , and a target characteristic quantity estimating section  34 . 
     The continuous characteristic quantity extracting section  31  obtains continuous data that are chronologically continuous data that are input from the outside and extracts continuous characteristic quantities that are chronological continuous characteristic quantities from the obtained continuous data. The continuous characteristic quantity extracting section  31  extracts at least one continuous characteristic quantities from the continuous data. The continuous characteristic quantity extracting section  31  successively supplies the extracted continuous characteristic quantities to the continuous characteristic cutting section  32 . 
     In other words, continuous characteristic quantities that are characteristic quantities that are chronologically continuous are supplied to the continuous characteristic cutting section  32  in the order of which they have been extracted. 
     The continuous characteristic cutting section  32  cuts each of the continuous characteristic quantities supplied from the continuous characteristic quantity extracting section  31  into regions each of which has a predetermined length. In other words, the continuous characteristic cutting section  32  creates at least one region of each of the continuous characteristic quantities. The continuous characteristic cutting section  32  successively supplies the regions of each of the continuous characteristic quantities to the regional characteristic quantity extracting section  33  in the order of which they have been cut into the regions. 
     The regional characteristic quantity extracting section  33  extracts a regional characteristic quantity that is a characteristic quantity represented by one scalar or vector from each of the regions into which each of the continuous characteristic quantities have been cut by the continuous characteristic cutting section  32 . In other words, the regional characteristic quantity extracting section  33  extracts at least one regional characteristic quantity from each of the regions of each of the continuous characteristic quantities. The regional characteristic quantity extracting section  33  supplies the extracted regional characteristic quantities to the target characteristic quantity estimating section  34  in the order of which they have been extracted. 
     The target characteristic quantity estimating section  34  estimates a target characteristic quantity to be finally obtained in each region having a predetermined length. In other words, the target characteristic quantity estimating section  34  estimates a target characteristic quantity that is a characteristic quantity that represents one characteristic of data in each region having the predetermined length from the regional characteristic quantities extracted by the regional characteristic quantity extracting section  33 . The target characteristic quantity estimating section  34  outputs the target characteristic quantity that has been estimated by the regional characteristic quantity extracting section  33 . 
     Next, with reference to a flow chart shown in  FIG. 3 , a process of extracting a target characteristic quantity will be described. At step S 11 , the continuous characteristic quantity extracting section  31  of the information processing apparatus  11  extracts at least one continuous characteristic quantity that continuously changes from continuous data that are chronologically continuous data that have been input from the outside. 
     For example, as shown in  FIG. 4 , the continuous characteristic quantity extracting section  31  extracts three continuous characteristic quantities that continuously change such as continuous characteristic quantity  1 , continuous characteristic quantity  2 , and continuous characteristic quantity  3 , from continuous data. 
     More specifically, when continuous data are sound data, the continuous characteristic quantity extracting section  31  extracts continuous characteristic quantity  1  that represents a sound volume at each time, continuous characteristic quantity  2  that represents a sound of a musical interval of 12 equal temperaments (for example, a sound of Do, Re, or Mi) at each time, and continuous characteristic quantity  3  that represents the balance of a right channel signal and a left channel signal at each time from the continuous data. 
     When continuous data are moving image data, the continuous characteristic quantity extracting section  31  extracts continuous characteristic quantity  1  that represents the brightness of the moving image at each time, continuous characteristic quantity  2  that represents a moving quantity at each time, and continuous characteristic quantity  3  that represents the color of the moving image at each time from the continuous data. 
     The continuous characteristic quantity extracting section  31  successively supplies the extracted continuous characteristic quantities to the continuous characteristic cutting section  32  in the order of which they have been extracted. 
     At step S 12 , the continuous characteristic cutting section  32  cuts at least one continuous characteristic quantity into regions each of which has a predetermined length. 
     For example, the continuous characteristic cutting section  32  divides each of continuous characteristic quantities such as continuous characteristic quantity  1 , continuous characteristic quantity  2 , and continuous characteristic quantity  3 , of continuous data into regions each of which has a predetermined length represented by adjacent vertical lines shown in  FIG. 5  and cuts each of the continuous characteristic quantities such as continuous characteristic quantity  1 , continuous characteristic quantity  2 , and continuous characteristic quantity  3  into regions each of which has the predetermined length. 
     A plurality of continuous characteristic quantities are cut in such a manner that they are cut in the same position and with the same length. 
     In this example, the length may be based on a time, a data quantity of continuous data, or a predetermined unit (for example, a frame) of continuous data. 
     The continuous characteristic cutting section  32  may cut each continuous characteristic quantity into regions each of which has a predetermined length such that each cut region overlaps with an adjacent cut region. 
     More specifically, for example, the continuous characteristic cutting section  32  cuts continuous characteristic quantity  1  that represents a sound volume at each time, continuous characteristic quantity  2  that represents a sound of a musical interval of the 12 equal temperaments at each time, and continuous characteristic quantity  3  that represents the balance of the right channel signal and the left channel signal at each time that have been extracted from continuous data that are sound data into regions each of which has a length of 5 seconds, 10 seconds, or 15 seconds of the sound data. 
     Instead, for example, the continuous characteristic cutting section  32  cuts continuous characteristic quantity  1  that represents the brightness of the moving image at each time, continuous characteristic quantity  2  that represents the moving quantity at each time, and continuous characteristic quantity  3  that represents the color of the moving image at each time extracted from continuous data that are moving image data into regions each of which has length of 30 frames, 150 frames, or 300 frames of the moving image data. 
     The continuous characteristic cutting section  32  supplies the regions into which continuous characteristic quantities have been cut to the regional characteristic quantity extracting section  33  in the order of which they have been cut. 
     At step S 13 , the regional characteristic quantity extracting section  33  extracts at least one regional characteristic quantity represented by one scalar or vector corresponding to at least one continuous characteristic quantity that has been cut into regions each of which has the predetermined length. 
     For example, the regional characteristic quantity extracting section  33  applies at least one predetermined process to each of regions into which each of the continuous characteristic quantities have been cut to extract at least one regional characteristic quantity that is a characteristic quantity represented by at least one scalar or vector from each of the continuous characteristic quantities. 
     One regional characteristic quantity represents a characteristic of one region as one scalar or one vector. 
     For example, as shown in  FIG. 6 , the regional characteristic quantity extracting section  33  obtains the average of continuous characteristic quantity  1  that represents the sound volume at each time of the first region extracted from continuous data that are sound data. Thus, the regional characteristic quantity extracting section  33  extracts 0.2 as a regional characteristic quantity of the first region. Likewise, the regional characteristic quantity extracting section  33  obtains the averages of continuous characteristic quantity  1  that represents the sound volume at each time of the second and third regions extracted from continuous data that are sound data. Thus, the regional characteristic quantity extracting section  33  extracts −0.05 and 0.05 as regional characteristic quantities of the second and third regions, respectively. 
     In addition, the regional characteristic quantity extracting section  33  obtains the variances of continuous characteristic quantity  1  that represents the sound volume at each time of the first, second, and third regions extracted from continuous data that are sound data. As a result, the regional characteristic quantity extracting section  33  extracts 0.2, 0.15, and 0.1 as regional characteristic quantities of the first, second, and third regions, respectively. 
     In addition, the regional characteristic quantity extracting section  33  obtains the gradients of continuous characteristic quantity  1  that represents the sound volume at each time of the first, second, and third regions extracted from continuous data that are sound data. Thus, the regional characteristic quantity extracting section  33  extracts 0.3, −0.2, and 0.0 as regional characteristic quantities of the first, second, and third regions, respectively. 
     Likewise, the regional characteristic quantity extracting section  33  extracts regional characteristic quantities that represent the averages, variances, and gradients of continuous characteristic quantity  1  of the fourth and later regions. 
     In addition, the regional characteristic quantity extracting section  33  extracts regional characteristic quantities that represent the averages, variances, and gradients of continuous characteristic quantity  2  that represents a sound of a musical interval of the 12 equal temperaments at each time and those of continuous characteristic quantity  3  that represents the balance of the right channel signal and the left channel signal at each time of individual regions extracted from continuous data that is sound data. 
     When continuous data are moving image data, the regional characteristic quantity extracting section  33  extracts regional characteristic quantities that represent the averages, variances, and gradients of continuous characteristic quantity  1  that represents the brightness of the moving image at each time, continuous characteristic quantity  2  that represents a moving quantity at each time, and continuous characteristic quantity  3  that represents the color of the moving image at each time of individual regions extracted from the continuous data. 
     At step S 14 , the target characteristic quantity estimating section  34  estimates a target characteristic quantity of each region from regional characteristic quantities. Thereafter, the process is completed. 
     In other words, at step S 14 , the target characteristic quantity estimating section  34  estimates a target characteristic quantity to be finally extracted from a regional characteristic quantity of each region extracted at step S 13 . For example, as shown in  FIG. 7 , when regional characteristic quantities such as regional characteristic quantity  1  to regional characteristic quantity  7  have been extracted, for example 0.2 as regional characteristic quantity  1 , 0.2 as regional characteristic quantity  2 , 0.3 as regional characteristic quantity  3 , −0.5 as regional characteristic quantity  4 , 1.23 as regional characteristic quantity  5 , 0.42 as regional characteristic quantity  6 , and 0.11 as regional characteristic quantity  7  have been extracted, the target characteristic quantity estimating section  34  estimates a target characteristic quantity from regional characteristic quantities  1  to  7 . 
     When continuous data are sound data, target characteristic quantities represent the presence or absence of vocal, the presence or absence of performance of a predetermined instrument, the presence or absence of noise, and so forth. 
     When continuous data are moving image data, target characteristic quantities represent the presence or absence of a person (or people), the presence or absence of a predetermined subject, the presence or absence of a predetermined motion of the subject (for example, whether or not the subject is dancing), and so forth. 
     Thus, at step S 14 , the target characteristic quantity estimating section  34  estimates a target characteristic quantity that is a characteristic quantity that represents one characteristic of data from a regional characteristic quantity in each region. 
     In other words, the target characteristic quantity estimating section  34  applies a predetermined process to a regional characteristic quantity in each region and estimates a target characteristic quantity in each region. 
     For example, the target characteristic quantity estimating section  34  is pre-created by learning teacher data composed of a regional characteristic quantity and a target characteristic quantity that represents one correct characteristic of data in each region. In other words, the target characteristic quantity estimating section  34  is pre-created by learning teacher data composed of chronologically continuous data from which a regional characteristic quantity is extracted in each region and a target characteristic quantity that represents one correct characteristic of entire data in each region. 
     For example, the target characteristic quantity estimating section  34  is created by machine-learning teacher data according to a technique such as regression, classify, SVM (Support Vector Machine), or GP (Genetic Programming). 
     In such a manner, a characteristic of continuous data in a predetermined region can be extracted. 
     A continuous characteristic quantity that chronologically continues is extracted from continuous data that chronologically continues. A region having a predetermined length is cut from the continuous characteristic quantity. A regional characteristic quantity that is a characteristic quantity represented by one scalar or vector is extracted from the region of the continuous characteristic quantity that has been cut from the continuous characteristic quantity. A target characteristic quantity that is a characteristic quantity that represents one characteristic of continuous data in each region. Thus, a characteristic of continuous data can be easily and quickly extracted in each region. 
     Next, more specifically, an embodiment of the present invention will be described. 
     As shown in  FIG. 8 , an automatic music/talk determination algorithm is applied to an input of sound data that are chronologically continuous data to determine whether the sound data are music or talk in each unit time and output a determination result of which the sound data are music or talk in each unit time. 
     For example, a determination result of sound data of waveform data that represents a waveform of a sound is output as talk (T), talk (T), talk (T), talk (T), music (M), music (M), music (M), music (M), music (M), and music (M) at each unit time having a predetermined length of the sound of the sound data. 
       FIG. 9  is a block diagram showing a structure of an information processing apparatus  51  according to an embodiment of the present invention. The information processing apparatus  51  determines whether input sound data are music or talk at each unit time. The information processing apparatus  51  is composed of a time-musical interval analyzing section  81 , a continuous music characteristic quantity extracting section  82 , a frame cutting section  83 , a regional characteristic quantity extracting section  84 , a music/talk determining section  85 , a data smoothening section  86 , and a sound storing section  87 . 
     The time-musical interval analyzing section  81  chronologically continuously analyzes sound data that chronologically continue in each of predetermined frequency bands. For example, the time-musical interval analyzing section  81  analyzes sound data that chronologically continue on two axes of musical intervals of 12 equal temperaments of each octave and times. The time-musical interval analyzing section  81  obtains time-musical interval data that represent energies of musical intervals of 12 equal temperaments of each octave and that chronologically continue as an analysis result and supplies the time-musical interval data to the continuous music characteristic quantity extracting section  82  in the order of which they have been analyzed. The time-musical interval data that chronologically continue are supplied to the continuous music characteristic quantity extracting section  82  such that they chronologically continue in the order of which they have been analyzed. 
     The continuous music characteristic quantity extracting section  82  extracts a continuous music characteristic quantity that is a chronologically continuous characteristic quantity from the time-musical interval data that are chronologically continuous data supplied from the time-musical interval analyzing section  81 . The continuous music characteristic quantity extracting section  82  supplies the extracted continuous music characteristic quantity to the frame cutting section  83  in the order of which it has been extracted. The continuous music characteristic quantity that is a chronologically continuous characteristic quantity is supplied to the frame cutting section  83  such that they chronologically continue in the order of which they have been extracted. 
     The frame cutting section  83  cuts the continuous music characteristic quantity supplied from the continuous music characteristic quantity extracting section  82  into frames each of which has a predetermined length. The frame cutting section  83  supplies the continuous music characteristic quantity that has been cut into frames as a frame based continuous music characteristic quantity to the regional characteristic quantity extracting section  84  in the order of which it has been cut into frames. 
     The regional characteristic quantity extracting section  84  extracts a regional characteristic quantity that is a characteristic quantity represented by one scalar or vector in each frame from the frame based continuous music characteristic quantity. The regional characteristic quantity extracting section  84  supplies the extracted regional characteristic quantities to the music/talk determining section  85  in the order of which they have been extracted. 
     The music/talk determining section  85  estimates a target characteristic quantity that is a characteristic of each frame of sound data and that represents a characteristic that identifies music or talk from each of the regional characteristic quantities extracted by the regional characteristic quantity extracting section  84 . In other words, the music/talk determining section  85  estimates a target characteristic quantity that identifies music or talk as one characteristic of sound data in each frame. 
     The music/talk determining section  85  supplies a frame based music/talk determination result that represents a characteristic of each frame that identifies music or talk obtained as the estimation result to the data smoothening section  86 . 
     The data smoothening section  86  obtains the moving average of the frame based music/talk determination result supplied from the music/talk determining section  85  and smoothens the target characteristic quantity according to the obtained moving average. The data smoothening section  86  obtains a continuous music/talk determination result as the smoothening result and supplies the continuous music/talk determination result to the sound storing section  87 . 
     The sound storing section  87  creates a label that identifies music or talk according to the continuous music/talk determination result supplied from the data smoothening section  86  and adds the created label to the sound data. The sound storing section  87  stores labeled sound data to for example a record medium (not shown). 
     In other words, the sound storing section  87  adds a label that represents an estimated target characteristic quantity to sound data and stores the resultant labeled sound data. 
     The sound storing section  87  may store labeled sound data in such a manner that the sound storing section  87  records them to a server (not shown) connected to the information processing apparatus  11  though a network. 
       FIG. 10  is a flow chart describing a process of adding a label to sound data. At step S 51 , the time-musical interval analyzing section  81  analyzes a waveform of sound data that chronologically continue on two axes of times and musical intervals of 12 equal temperaments of each octave and creates time-musical interval data according to the analysis result. 
     For example, as shown in  FIG. 11 , at step S 51 , the time-musical interval analyzing section  81  divides sound data into components of a plurality of octaves and obtains energies of musical levels of 12 equal temperaments of each octave, analyzes the sound data on two axes of times and musical intervals of 12 equal temperaments of each octave, and creates time-musical interval data according to the analysis result. 
     More specifically, when sound data are stereo data, the time-musical interval analyzing section  81  obtains energies of musical intervals of 12 equal temperaments of each of a plurality of octaves of each of right channel data and left channel data of the sound data and adds the energy obtained from the left channel data and the energy obtained from the right channel data of each octave to create time-musical interval data. 
     The time musical interval analyzing section  81  creates time-musical interval data that are chronologically continuous data. The time-musical interval analyzing section  81  supplies the created time-musical interval data to the continuous music characteristic quantity extracting section  82  in the order of which they have been created. 
     At step S 52 , the continuous music characteristic quantity extracting section  82  extracts several continuous music characteristic quantities from the time-musical interval data. 
     For example, at step S 52 , the continuous music characteristic quantity extracting section  82  extracts continuous music characteristic quantities that chronologically change such as continuous music characteristic quantity  1 , continuous music characteristic quantity  2 , and continuous music characteristic quantity  3  from the time-musical interval data that represent energies of musical intervals of 12 equal temperaments of each octave. For example, as shown in  FIG. 12 , the continuous music characteristic quantity extracting section  82  extracts continuous music characteristic quantity  1  that represents the level ratio of musical ranges at each time, continuous music characteristic quantity  2  that represents the energy difference or level difference of the right channel and the left channel at each time, and continuous music characteristic quantity  3  that represents parameters of envelops such as attack, decay, sustain, release, and so forth from the time-musical interval data that represent energies of musical intervals of 12 equal temperaments of each octave. Instead, for example, the continuous music characteristic quantity extracting section  82  extracts continuous music characteristic quantity  1  that represents the ratio of rhythms at each time, continuous music characteristic quantity  2  that represents the number of sounds at each time, and continuous music characteristic quantity  3  that represents a structure of harmonics at each time from the time-musical interval data that represent energies of musical intervals of 12 equal temperaments of each octave. 
     In addition, the continuous music characteristic quantity extracting section  82  may extract a continuous music characteristic quantity that represents a sound density, variation of musical intervals, or the like from the time-musical interval data that represent energies of musical intervals of 12 equal temperaments of each octave. 
     The continuous music characteristic quantity extracting section  82  supplies the extracted continuous music characteristic quantities to the frame cutting section  83  in the order of which they have been extracted. 
     At step S 53 , the frame cutting section  83  divides each of the continuous music characteristic quantities into frames and obtains frame based continuous music characteristic quantities. 
     For example, as shown in  FIG. 13 , the frame cutting section  83  divides each of continuous music characteristic quantities such as continuous music characteristic quantity  1 , continuous music characteristic quantity  2 , and continuous music characteristic quantity  3  into frames. In this example, a frame is a period between a time represented by a vertical line shown in  FIG. 13  and a time represented by a vertical line adjacent thereto. A frame is a period having a predetermined length. 
     The frame cutting section  83  cuts continuous music characteristic quantities such as continuous music characteristic quantity  1 , continuous music characteristic quantity  2 , and continuous music characteristic quantity  3  into frames. 
     The frame cutting section  83  cuts a plurality of continuous music characteristic quantities into frames such that they are cut at the same position and with the same length. 
     The frame cutting section  83  supplies frame based continuous music characteristic quantities divided into frames to the regional characteristic quantity extracting section  84  in the order of which they have been divided. 
     At step S 54 , the regional characteristic quantity extracting section  84  calculates the average and variance of frame based continuous music characteristic quantities divided to extract a regional characteristic quantity in each frame. 
     The regional characteristic quantity extracting section  84  applies at least one predetermined process to each of frame based continuous music characteristic quantities and extracts a regional feature quantity that is a characteristic quantity represented by at least one scalar or vector from each of the frame based continuous music characteristic quantities. 
     For example, as shown in  FIG. 14 , the regional characteristic quantity extracting section  84  obtains the average of the first frame of frame based continuous music characteristic quantity  1  that represents the level ratio of each musical range at each time. Thus, the regional characteristic quantity extracting section  84  extracts 0.2 as a regional characteristic quantity of the first frame. Likewise, the regional characteristic quantity extracting section  84  obtains the averages of the second and third frames of frame based continuous music characteristic quantity  1  that represents the level ratio of each musical range at each time. Thus, the regional characteristic quantity extracting section  84  extracts −0.05 and 0.05 as regional characteristic quantities of the second and third frames, respectively. 
     In addition, the regional characteristic quantity extracting section  84  obtains the variances of the first, second, and third frames of frame based continuous music characteristic quantity  1  that represents the level ratio of each musical range at each time. Thus, the regional characteristic quantity extracting section  84  extract 0.2, 0.15, and 0.1 as regional characteristic quantities of the first, second, and third frames, respectively. 
     The regional characteristic quantity extracting section  84  extracts regional characteristic quantities that represent the averages or variances of the fourth and later frames of frame based continuous music characteristic quantity  1 . 
     In addition, for example, as shown in  FIG. 14 , the regional characteristic quantity extracting section  84  obtains the average of the first frame of frame based continuous music characteristic quantity that represents the energy difference or level difference of the right channel and the left channel at each time. Thus, the regional characteristic quantity extracting section  84  obtains 0.1 as a regional characteristic quantity of the first frame. Likewise, the regional characteristic quantity extracting section  84  obtains the averages of the second and third frames of frame based continuous music characteristic quantity  2 . Thus, the regional characteristic quantity extracting section  84  extracts 0.4 and 0.5 as regional characteristic quantities of the second and third frames, respectively. 
     In addition, the regional characteristic quantity extracting section  84  obtains the variances of the first, second, and third frames of frame based continuous music characteristic quantity  2  that represents the energy difference or level difference of the right channel and the left channel at each time. Thus, the regional characteristic quantity extracting section  84  extracts 0.3, −0.2, and 0.0 as regional characteristic quantities of the first, second, and third frames, respectively. 
     Likewise, the regional characteristic quantity extracting section  84  extracts regional characteristic quantities that represent the averages or variances of the fourth frame and later frames of frame based continuous music characteristic quantity  2 . 
     The regional characteristic quantity extracting section  84  extracts regional characteristic quantities from frames of frame based continuous music characteristic quantity  3 . 
     The regional characteristic quantity extracting section  84  supplies the extracted regional characteristic quantities to the music/talk determining section  85 . 
     At step S 55 , the music/talk determining section  85  determines whether each frame is music or talk according to the regional characteristic quantities. 
     For example, the music/talk determining section  85  applies a relatively simple operation (for example, four-rule arithmetic operations, an exponentiation operation, or the like) represented by a pre-created target characteristic quantity extraction formula to at least one regional characteristic quantity of those that have been input and obtains a frame based music/talk determination result that is a target characteristic quantity that represents a probability of music as an operation result. The music/talk determining section  85  pre-stores the target characteristic quantity extraction formula. 
     When a target characteristic quantity represents a probability of music and the target characteristic quantity of a predetermined region is 0.5 or larger, the music/talk determining section  85  outputs a frame based music/talk determination result that denotes that the frame is music. When a target characteristic quantity represents a probability of music and the target characteristic quantity of a predetermined region is smaller than 0.5, the music/talk determining section  85  outputs a frame based music/talk determination result that denotes that the frame is talk. 
     For example, as shown in  FIG. 15 , when regional characteristic quantities such as regional characteristic quantity  1  to regional characteristic quantity  7  have been extracted in each frame, the music/talk determining section  85  determines whether this frame is music or talk according to 0.2 as regional characteristic quantity  1 , 0.2 as regional characteristic quantity  2 , 0.3 as regional characteristic quantity  3 , −0.5 as regional characteristic quantity  4 , 1.23 as regional characteristic quantity  5 , 0.42 as regional characteristic quantity  6 , and 0.11 as regional characteristic quantity  7 . 
     For example, the music/talk determining section  85  is pre-created by learning teacher data composed of a regional characteristic quantity in each frame and a target characteristic quantity that correctly represents whether each frame is music or talk. In other words, the music/talk determining section  85  is pre-created by learning a target characteristic quantity extraction formula using teacher data composed of chronologically continuous sound data from which a regional characteristic quantity is extracted in each frame and a target characteristic quantity that correctly denotes whether each frame is music or talk. 
     A target characteristic quantity extraction formula pre-stored in the music/talk determining section  85  is pre-created by genetically learning teacher data composed of chronologically continuous sound data and a target characteristic quantity that correctly denotes whether each frame is music or talk. 
     Examples of a learning algorithm that creates a target characteristic quantity extraction formula include regression, classify, SVM (Support Vector Machine), and GP (Genetic Programming). 
     The music/talk determining section  85  supplies a frame based music/talk determination result that represents a determination result of whether each frame is music or talk to the data smoothening section  86 . 
     At step S 56 , the data smoothening section  86  smoothens the determination result of whether each frame is music or talk. 
     For example, the data smoothening section  86  filters the determination result of whether each frame is music or talk to smoothen the determination result. More specifically, the data smoothening section  86  is composed of a moving average filter. At step S 56 , the data smoothening section  86  obtains the moving average of the music/talk determination results of the frames to smoothen them. 
     In  FIG. 16 , the frame based music/talk determination results of 21 frames are talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), music (M), music (M), music (M), talk (T), music (M), music (M), music (M), talk (T), music (M), music (M), music (M), and music (M). Thus, the thirteenth frame and the seventeenth frame are talk (T), and the twelfth frame, fourteenth frame, sixteenth frame, and eighteenth frame are music (M). Next, this case will be described. 
     When the length of each frame is sufficiently decreased, a predetermined number of frames of talk continue or a predetermined number of frames of music continue. In other words, a frame of music is not preceded or followed by frames of talk. Likewise, a frame of talk is not preceded or followed by frames of music. Thus, 21 frames are arranged in the order of talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), music (M), music (M), music (M), music (M), music (M), music (M), music (M), music (M), music (M), music (M), music (M), and music (M) as represented by the first sequence shown in  FIG. 16 . In other words, the frame based music/talk determination result represented by the second sequence shown in  FIG. 16  contains determination errors of frames of talk at the thirteenth frame and the seventeenth frame. 
     The data smoothening section  86  obtains the moving average of the music/talk determination result of the frames to smoothen them. As a result, the data smoothening section  86  obtains continuous music/talk determination results of a 21-frame sequence of talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), talk (T), music (M), music (M), music (M), talk (T), music (M), music (M), music (M), talk (T), music (M), music (M), music (M), and music (M) where the thirteenth frame and the seventeenth frame are music (M). 
     Thus, by smoothening the determination results, errors can be effectively filtered. 
     The data smoothening section  86  supplies the continuous music/talk determination results smoothened by obtaining the moving average of the frame based music/talk determination results to the sound storing section  87 . 
     At step S 57 , the sound storing section  87  adds a label that identifies music or talk to each frame of sound data and stores the labeled sound data. Thereafter, the process is completed. 
     For example, as shown in  FIG. 17 , the sound storing section  87  adds a label that identifies music or talk to each frame of the sound data. In other words, the sound storing section  87  adds a label that identifies music to a frame of sound data determined to be music as a continuous music/talk determination result and adds a label that identifies talk to a frame of sound data determined to be talk as a continuous music/talk determination result. The sound storing section  87  records and stores the sound data to which labels that identify music or talk have been added to a record medium such as a hard disk or an optical disc. 
     When music data to which labels that identify music or talk have been added are reproduced, with reference to the labels, only music regions or talk regions of the sound data can be reproduced. In contrast, when sound data to which labels that identify music or talk are reproduced, with reference to the labels, the sound data may be reproduced in such a manner that only music regions or talk regions are successively skipped from the sound data. 
     As described above, when a continuous characteristic quantity that is affected by past values of continuous data due to a time constant has been extracted, a target characteristic quantity taking into account of influence of past regions of continuous data against the current region can be obtained. 
     In the process of obtaining a target characteristic quantity, most of arithmetic operations are used to extract a continuous characteristic quantity. Thus, an increase of the time resolution corresponding to an increase of an overlap in a range of which a continuous characteristic quantity is cut does not largely increase the arithmetic operations of the process. In other words, the time resolution of a target characteristic quantity can be increased in a simpler structure than before without necessity of increasing the arithmetic operations in the process. 
     Continuous characteristic quantities can be extracted while continuous data are input. Thus, the latency after continuous data are input until a characteristic is obtained in this embodiment is smaller than that of the related art in which continuous data are divided into regions and characteristics are extracted therefrom. 
     Regardless of the case of which continuous data are divided into regions and characteristics are extracted therefrom according to the related art or the case of which a continuous characteristic quantity is extracted from continuous data, the extracted continuous characteristic quantity is divided into regions, and characteristics are obtained therefrom according to this embodiment of the present invention, the time lag (latency) after the continuous data are input until a characteristic quantity to be finally obtained is output is given by adding the period for which data for regions are input and the period for which the data are processed. 
     When continuous data are divided into regions and characteristics are extracted therefrom, the period for which data for regions are input is smaller than the period for which the data are processed. 
     In contrast, when a continuous characteristic quantity is extracted from continuous data, the continuous characteristic quantity is divided into regions, and characteristics are extracted therefrom, although the period for which data for the regions is nearly the same as the period in the case that continuous data are divided into regions and characteristics are extracted therefrom, the period for which the data are processed is small. 
     Thus, when a continuous characteristic quantity is extracted from continuous data, the extracted continuous characteristic quantity is divided into regions, and then characteristics are obtained therefrom, the time lag (latency) can become smaller than that in the case that continuous data are divided into regions and characteristics are extracted therefrom. 
     In addition, as the target characteristic quantity estimating section  34  or the music/talk determining section  85 , a simple structure that obtains a target characteristic quantity that represents correct data from a regional characteristic quantity represented by a scalar or a vector can be used. Thus, the target characteristic quantity estimating section  34  or the music/talk determining section  85  can be created with one of various types of algorithms used in an ordinary machine learning process or statistical analyzing process without necessity of preparing a special model according to an objective problem. 
     In addition, a continuous characteristic quantity extraction algorithm that is used to extract a continuous characteristic quantity from continuous data and that is stored in the continuous characteristic quantity extracting section  31  shown in  FIG. 1  or the time-musical interval analyzing section  81  and the continuous music characteristic quantity extracting section  82  shown in  FIG. 9  may be automatically created by learning continuous data and teacher data composed of continuous data to which a label representing one correct characteristic has been added at each time (sample point). 
     Next, with reference to  FIG. 18  to  FIG. 25 , a process of automatically creating a continuous characteristic quantity extraction algorithm will be described. 
     When a continuous characteristic quantity extraction algorithm is automatically created, an algorithm creating section  101  shown in  FIG. 18  is newly disposed in the information processing apparatus  11  shown in  FIG. 2  or the information processing apparatus  51  shown in  FIG. 9 . The algorithm creating section  101  automatically creates a continuous characteristic quantity extraction algorithm that automatically extracts a continuous characteristic quantity from continuous data that are input from the outside. 
     Specifically, as shown in  FIG. 19 , the algorithm creating section  101  performs a machine learning process according to GA (Genetic Algorithm) or GP (Genetic Programming) by inputting continuous data and teacher data composed of a label that represents one correct characteristic at each time of the continuous data, creates a continuous characteristic quantity extraction algorithm as a result of the machine learning process, and outputs the created continuous characteristic quantity extraction algorithm. 
     More specifically, as shown in  FIG. 20 , the algorithm creating section  101  creates various combinations of filters (functions), evaluates the accuracy level of a characteristic that each label represents in the continuous data according to a continuous characteristic quantity that is output as the result of the combinations of the created filters, and searches a combination of filters that outputs a continuous characteristic quantity with which a characteristic of the continuous data can be estimated with a higher accuracy from infinitive combinations of filters according to GA (Genetic Algorithm) or GP (Genetic Programming). 
       FIG. 21  is a block diagram showing a functional structure of the algorithm creating section  101 . The algorithm creating section  101  is composed of a first generation gene creating section  121 , a gene evaluating section  122 , and a second or later generation gene creating section  123 . 
     The first generation gene creating section  121  creates first generation genes that represent various combinations of filters. 
     The gene evaluating section  122  evaluates the accuracy level in which a characteristic of continuous data represented by a label of teacher data can be estimated according to a continuous characteristic quantity extracted from the continuous data of the teacher data by a filter process represented by each gene created by the first generation gene creating section  121  or the second or later generation gene creating section  123 . The gene evaluating section  122  is composed of an executing section  141 , an evaluating section  142 , and a teacher data storing section  143 . 
     The executing section  141  inputs continuous data of teacher data stored in the teacher data storing section  143 , successively executes filter processes represented by the individual genes, and extracts a continuous characteristic quantity of the input continuous data. The executing section  141  supplies the extracted continuous characteristic quantity to the evaluating section  142 . 
     As will be described later with reference to  FIG. 22 , the evaluating section  142  calculates an evaluation value that represents the estimated accuracy level in which a characteristic of continuous data represented by a label of teacher data can be estimated according to a continuous characteristic quantity extracted from the continuous data of the teacher data by the executing section  141  for each gene created by the first generation gene creating section  121  or the second or later generation gene creating section  123 . The evaluating section  142  supplies the evaluated genes and information that represents the evaluated values to a selecting section  151 , a crossing-over section  152 , and a mutating section  153  of the second or later generation gene creating section  123 . In addition, the evaluating section  142  commands a randomly creating section  154  to create a predetermined number of genes. When the evaluating section  142  has determined that the evaluation values have become stable and the evolutions of genes have converged, the evaluating section  142  supplies these genes and their evaluation values to the selecting section  151 . 
     The teacher data storing section  143  stores teacher data that are input from the outside. 
     The second or later generation gene creating section  123  creates genes of the second or later generations. As described above, the second or later generation gene creating section  123  is composed of the selecting section  151 , the crossing-over section  152 , the mutating section  153 , and the randomly creating section  154 . 
     As will be described later with reference to  FIG. 22 , the selecting section  151  selects genes that are caused to succeed from the current generation to the next generation according to the evaluation values obtained by the evaluating section  142  and supplies the selected genes as genes of the next generation to the gene evaluating section  122 . When the selecting section  151  has determined that the evolutions of genes have converged, the selecting section  151  selects a predetermined number of genes from those having higher evaluation values and outputs combinations of filters represented by the selected genes as a continuous characteristic quantity extraction algorithm. 
     As will be described later with reference to FIG.  22 , the crossing-over section  152  crosses over two genes by changing part of filters represented by two genes selected from those having higher evaluation values of the current generation. The crossing-over section  152  supplies the genes that have been crossed over as genes of the next generation to the gene evaluating section  122 . 
     As will be described later with reference to  FIG. 22 , the mutating section  153  mutates a gene by randomly changing part of a filter of a gene randomly selected from those having higher evaluation values of the current generation. The mutating section  153  supplies the mutated gene as that of the next generation to the gene evaluating section  122 . 
     As will be described later with reference to  FIG. 22 , the randomly creating section  154  creates new genes by randomly combining various types of filters. The randomly creating section  154  supplies the created genes as those of the next generation to the gene evaluating section  122 . 
     Filters that compose genes created by the algorithm creating section  101  are time series data that are input in real time, namely filters used for continuous data. Examples of these filters include arithmetic operation filters (for four rule arithmetic operations, an exponential operation, a differentiation operation, an integration operation, and an absolute value operation), an LPF (Low Pass Filter), an HPF (High Pass Filter), a BPF (Band Pass Filter), an IIR (Infinite Impulse Response) filter, an FIR (Finite Impulse Response) filter, a real time level maximizer that equalizes the sound volume, a pitch tracer that traces a musical interval, and a level meter that creates an envelop of continuous data. 
     Genes are represented in the form of which filters are arranged in the order of which they are executed for example “pitch tracer→differentiation filter→absolute value filter (ABS)→LPF”. 
       FIG. 22  is a flow chart describing an algorithm creating process executed by the algorithm creating section  101 . 
     Next, in the information processing apparatus  51  that determines whether input sound data are music or talk at each unit time described with reference to  FIG. 9 , a process of which the algorithm creating section  101  creates a continuous music characteristic quantity extraction algorithm that extracts a continuous music characteristic quantity from sound data will be exemplified as shown in  FIG. 23 . In other words, a process of which the algorithm creating section  101  creates a continuous characteristic quantity extraction algorithm corresponding to the time-musical interval analyzing section  81  and the continuous music characteristic quantity extracting section  82  shown in  FIG. 9  will be exemplified. 
     At step S 101 , the first generation gene creating section  121  creates genes of the first generation. Specifically, the first generation gene creating section  121  creates a predetermined number of genes by randomly combining various types of filters used for time series data that are input in real time, namely continuous data. The first generation gene creating section  121  supplies the created genes to the gene evaluating section  122 . 
     At step S 102 , the executing section  141  selects one gene that has not been evaluated from those supplied from the first generation gene creating section  121 . In this case, the executing section  141  selects one gene that has not been evaluated as an evaluation target from those of the first generation created by the first generation gene creating section  121 . 
     At step S 103 , the executing section  141  selects one piece of teacher data that have not been processed. Specifically, the executing section  141  selects one piece of teacher data that have not been processed by the gene as the current evaluation target from those stored in the teacher data storing section  143 . 
     At step S 104 , the executing section  141  extracts a continuous characteristic quantity of the selected teacher data with the gene as the evaluation target. Specifically, the executing section  141  extracts a continuous characteristic quantity of the selected teacher data by inputting continuous data of the selected teacher data and successively executing the processes of the filters represented by the gene as the evaluation target. 
     When a continuous music characteristic quantity extraction algorithm is created, as shown in  FIG. 24 , a waveform of which sound data have been filtered is extracted as a continuous music characteristic quantity by performing processes represented by the gene as the evaluation target for sound data as teacher data, namely successively executing filter processes represented by the gene as the evaluation target. 
     The executing section  141  supplies the extracted continuous characteristic quantity to the evaluating section  142 . 
     At step S 105 , the executing section  141  determines whether or not all the teacher data have been processed. When there are teacher data from which a continuous characteristic quantity has not been extracted for the gene as the evaluation target in those stored in the teacher data storing section  143 , the executing section  141  determines that all the teacher data have not been processed. Thereafter, the flow returns to step S 103 . Thereafter, step S 103  to step S 105  are repeated until all the teacher data have been processed at step S 105 . 
     When the determination result at step S 105  denotes that all the teacher data have been processed, the flow advances to step S 106 . 
     At step S 106 , the evaluating section  142  evaluates the gene. 
     When a continuous music characteristic quantity extraction algorithm is created, as shown in  FIG. 25 , the evaluating section  142  calculates an evaluation value that represents the accuracy level of a characteristic quantity that represents a characteristic of continuous data represented by a label of teacher data, namely a characteristic quantity that represents music or talk as a target characteristic quantity of the information processing apparatus  51 , from a filtered waveform that is a continuous music characteristic quantity extracted according to the gene as the evaluation target. 
     Next, a method of calculating an evaluation value will be exemplified. 
     When the values of labels of teacher data, namely characteristic quantities that represent a characteristic of continuous data, are represented by successive numerical values, for example, characteristic quantities represented in the correct data sequence are a sense of speed of a music represented by successive numerical values in the range from 0.0 to 1.0, for example the absolute values of Pearson&#39;s correlation coefficients are used as an evaluation value of the gene. Specifically, assuming that the values of labels of teacher data are represented by variable X and the values of corresponding continuous characteristic quantities are represented by variable Y, correlation coefficient r of variable X and variable Y is obtained by the following formula (1).
 
 r =(covariance of variable  X  and variable  Y )/{(standard deviation of variable  X )×(standard deviation of variable  Y )}
 
                   r   =         1     n   -   1       ⁢       ∑     i   =   1     n     ⁢           ⁢       (     Xi   -     X   _       )     ⁢     (     Yi   -     Y   _       )                 1     n   -   1       ⁢       ∑     i   =   1     n     ⁢           ⁢         (     Xi   -     X   _       )     2     ⁢         1     n   -   1       ⁢       ∑     i   =   1     n     ⁢           ⁢       (     Yi   -     Y   _       )     2                             (   1   )               
where  X  is the average of X,  Y  is the average of Y.
 
     The weaker the correlation of the values of continuous characteristic quantities extracted from continuous data and the values of characteristic quantities of continuous data represented by labels of teacher data, the more the correlation coefficient r has or approaches to 0. In contrast, the stronger the correlation, the more the correlation coefficient r has or approaches to 1.0 or −1.0. In other words, it is likely that the higher the accuracy of an estimated characteristic quantity of continuous data with continuous characteristic quantities extracted according to combinations of filters represented by a gene as an evaluation target, the more the correlation coefficient r approaches to 1.0 or −1.0, whereas the lower the accuracy, the more the correlation coefficient r approaches to 0. 
     When the value of a label of teacher data, namely a characteristic quantity that represents a characteristic of continuous data, is categorized as a predetermined class, as exemplified above a target characteristic quantity is categorized as talk or music or a vocal present state or a vocal absent state, then for example, Fisher&#39;s discriminant ratio (FDR) is used as an evaluation value. 
     For example, when a target characteristic quantity is categorized as two classes, in other words, a target characteristic quantity is represented as a binary value, the values of continuous characteristic quantities extracted in a process represented by a gene as a evaluation target are categorized as two sets according to the values of corresponding labels of teacher data and the sets are represented by set X and set Y, then the FDR is obtained by the following formula (2).
 
FDR=(average of  X −average of  Y ) 2 /{(standard deviation of  X +standard deviation of  Y )}  (2)
 
     The weaker the correlation of the values of the continuous characteristic quantities extracted in a process represented by the gene as the evaluation target and the set to which the values belongs, namely the weaker the correlation of the values of the continuous characteristic quantities extracted in the process represented by the gene as the evaluation target and the characteristic quantities represented by the labels of teacher data is, the smaller the value of the FDR is. In contrast, the stronger the correlation of the values of the continuous characteristic quantities extracted in the process represented by the gene as the evaluation target and the set to which the values belong is, namely the stronger the correlation of the values of the continuous characteristic quantities extracted in the process represented by the gene as the evaluation target and the characteristic quantities represented by the labels of the teacher data is, the larger the value of the FDR is. In other words, it is likely that the larger the value of the FDR is, the higher the accuracy of the estimated characteristic quantity of the continuous data with the continuous characteristic quantities extracted according to combinations of filters represented by the gene as the evaluation target is, whereas the smaller the value of the FDR is, the lower the accuracy is. 
     The foregoing method of calculating the evaluation value of a gene is exemplary. Rather, it is preferred to use a proper method with a continuous characteristic quantity extracted in a process represented by a gene and a characteristic quantity represented by a label of teacher data. 
     When the number of calculations increases because there are many samples of a continuous characteristic quantity, if necessary, the number of samples of the continuous characteristic quantity may be decimated. 
     At step S 107 , the evaluating section  142  determines whether all genes have been evaluated. When the determination result at step S 107  denotes that all the genes have not been evaluated, the flow returns to step S 102 . Step S 102  to step S 107  are repeated until the determination result at step S 107  denotes that all the genes have been evaluated. 
     When the determination result at step S 107  denotes that all the genes have been evaluated, in this case, all the genes of the first generation have been evaluated, the flow advances to step S 108 . 
     At step S 108 , the evaluating section  142  compares the evaluation values of the genes of the past generations with those of the genes of the current generation. In this case, since the genes of the first generation are being evaluated and the evaluation values of the genes of the past generations have not been stored, the evaluating section  142  stores the maximum value of the evaluation values of the genes of the first generation as the evaluation value of the current gene. 
     At step S 109 , the evaluating section  142  determines whether or not the evaluation values have been updated in predetermined generations. In this case, since the evaluation values have been changed at step S 108 , the flow advances to step S 110 . 
     At step S 110 , the selecting section  151  selects genes. Specifically, the evaluating section  142  supplies all genes of the current generation and information that represents the evaluation values of the genes to the selecting section  151 . The selecting section  151  selects a predetermined number of the genes from those having higher evaluation values and supplies the selected genes as those of the next generation to the gene evaluating section  122 . 
     At step S 111 , the crossing-over section  152  crosses over genes. Specifically, the evaluating section  142  supplies all the genes of the current generation and information that represents the evaluation values of the genes to the crossing-over section  152 . The crossing-over section  152  randomly selects two genes from those having higher evaluation values than a predetermine value and crosses over filters between the selected genes. Thus, the crossing-over section  152  crosses over the two genes by recombining filters represented by the genes. The crossing-over section  152  crosses over a predetermined number of genes and supplies the genes that have been crossed over as those of the next generation to the gene evaluating section  122 . 
     At step S 112 , the mutating section  153  mutates genes. Specifically, the evaluating section  142  supplies all genes of the current generation and information that represents the evaluation values of the genes to the mutating section  153 . The mutating section  153  mutates the genes by randomly selecting a predetermined number of genes from those having higher evaluation values than a predetermined value and randomly changing part of filters of the selected genes. The mutating section  153  supplies the mutated genes as genes of the next generation to the gene evaluating section  122 . 
     At step S 113 , the randomly creating section  154  randomly creates genes. Specifically, the evaluating section  142  commands the randomly creating section  154  to create a predetermined number of genes. The randomly creating section  154  randomly creates a predetermined number of genes in the same process as does the first generation gene creating section  121 . The randomly creating section  154  supplies the created genes as genes of the next generation to the gene evaluating section  122 . 
     Thereafter, the flow returns to step S 102 . Step S 102  to step S 107  are repeated until it has been determined that all genes of the second generation have been evaluated at step S 107 . 
     When the determined result at step S 107  denotes that all the genes have been evaluated, namely all the genes of the second generation have been evaluated, the flow advances to step S 108 . 
     At step S 108 , in this case, the evaluating section  142  compares the evaluation values of the genes of the immediately preceding generation that have been stored, namely the evaluation values of the genes of the first generation, with the maximum value of the evaluation values of the genes of the second generation. When the maximum value of the evaluation values of the genes of the second generation is larger than the evaluation values of the genes of the first generation, the evaluating section  142  updates the evaluation value of the current gene with the maximum value of the evaluation values of the genes of the second generation. When the maximum value of the evaluation values of the genes of the second generation is equal to or smaller than the evaluation values of the genes of the first generation, the evaluating section  142  does not update the evaluation value of the current gene with the maximum value of the evaluation value of the second generation and uses the evaluation value of the current gene. 
     Step S 102  to step S 113  are repeated until it has been determined at step S 109  that the evaluation value has not been updated in a predetermined number of generations. In other words, genes of new generations are created and evaluated, evaluation values of genes of an immediately preceding generation and the maximum value of evaluation values of genes of a new generation are compared, and the evaluation values of the genes of the current generation are updated when the maximum value of the evaluation values of the genes of the new generation is larger than the evaluation values of the genes of the immediately preceding generation until the evaluation values of genes have not been updated in a predetermined number of generations. 
     When the determination result at step S 109  denotes that the evaluation values of genes have not been updated in the predetermined number of generations, namely the evaluation values of genes are stable and the evolutions of the genes have converged, the flow advances to step S 114 . 
     Instead, at step S 109 , it may be determined whether or not the maximum value of the evaluation values of genes of the current generation is equal to or larger than a predetermined threshold value. In this case, when the determination result at step S 109  denotes that the maximum value of the evaluation values of the genes of the current generation is smaller than the predetermined threshold value, namely the accuracy of a characteristic quantity estimated with combinations of filters represented by the genes of the current generation does not satisfy a desired value, the flow advances to step S 110 . In contrast, when the determination result at step S 109  denotes that the maximum value of the evaluation values of the genes of the current generation is equal to or larger than the predetermined threshold value, namely the accuracy of a characteristic quantity estimated with combinations of filters represented by the genes of the current generation satisfies a desired value, the flow advances to step S 114 . 
     At step S 114 , the selecting section  151  selects a gene used for the continuous characteristic quantity extraction algorithm. Thereafter, the algorithm creating process is completed. Specifically, the evaluating section  142  supplies all the genes of the current generation and the evaluation values of the genes to the selecting section  151 . The selecting section  151  selects a predetermined number of genes (at least one) having the highest evaluation value from all the genes of the current generation and outputs combinations of filters represented by the selected genes as a continuous characteristic quantity extraction algorithm. 
     Instead, at step S 114 , all genes having evaluation values higher than a predetermined threshold value may be selected from all the genes of the current generation and combinations of filters represented by the selected genes may be output as a continuous characteristic quantity extraction algorithm. 
     In such a manner, a continuous characteristic quantity extraction algorithm that is used in the information processing apparatus  11  shown in  FIG. 2  or the information processing apparatus  51  shown in  FIG. 9  and that extracts a continuous characteristic quantity from continuous data is created. 
     Since the continuous characteristic quantity extraction algorithm is automatically created according to GA or GP, combinations of filters that extract continuous characteristic quantities that are more suitable to estimate a target characteristic quantity can be obtained from much more combinations of filters than an algorithm that is manually created. Thus, it can be expected to improve the estimation accuracy for a target characteristic quantity. 
     In the information processing apparatus  11  shown in  FIG. 2  or the information processing apparatus  51  shown in  FIG. 9 , a continuous characteristic quantity extraction algorithm that extracts continuous characteristic quantities may be created only by the algorithm creating section  101 . Instead, a continuous characteristic quantity extraction algorithm may be manually created. Instead, both a continuous characteristic quantity extraction algorithm that is created by the algorithm creating section  101  and a continuous characteristic quantity extraction algorithm that is manually created may be used in parallel. 
     In the foregoing description, the information processing apparatus that processes continuous data such as sound data or moving image data was exemplified. However, as embodiments, the present invention may be applied to a recording/reproducing apparatus that records and reproduces sound data or moving image data, an recording apparatus that records sound data or moving image data, a reproducing apparatus that reproduces sound data or moving image data, and so forth. More specifically, as embodiments, the present invention may be applied to a record player having a built-in optical disc drive or hard disk, a portable recorder or player, a digital video camera, a mobile phone, and so forth having a built-in semiconductor memory. 
     In the foregoing description, a target characteristic quantity represents a characteristic to be finally obtained that is for example music or talk. Instead, a target characteristic quantity may be a value representing a probability of a characteristic to be finally obtained such as a probability of music or talk. 
     When a target characteristic quantity extraction formula is created by a learning process and arithmetic operations are performed according to the target characteristic quantity extraction formula, then a characteristic of data can be extracted. When chronologically continuous sound data are chronologically continuously analyzed in each of predetermined frequency bands, a continuous characteristic quantity is extracted as a chronologically continuous characteristic quantity from the analysis result, the continuous characteristic quantity is cut into regions each of which has a predetermined length, a regional characteristic quantity that is a characteristic quantity represented by one scalar or vector is extracted from each region, and a target characteristic quantity that is a characteristic quantity that represents one characteristic of sound data is estimated from the regional characteristic quantity, then a characteristic of the sound data can be easily and quickly extracted. 
     The foregoing sequence of processes may be executed by hardware or software. When the sequence of processes is executed by software, programs that compose the software are built in dedicated hardware of a computer or installed from a program record medium to for example a general purpose personal computer that executes various types of functions according to various programs installed thereto. 
       FIG. 26  is a block diagram showing an exemplary structure of a personal computer that executes the foregoing sequence of processes according to the programs. A CPU (Central Processing Unit)  201  executes various types of processes according to programs stored in a ROM (Read Only Memory)  202  or a storing section  208 . When necessary, a RAM (Random Access Memory)  203  stores programs, data and so forth, which cause the CPU  201  to execute processes. The CPU  201 , the ROM  202 , and the RAM  203  are connected to each other by a bus  204 . 
     Connected to the CPU  201  through the bus  204  is also an input and output interface  205 . Connected to the input and output interface  205  are an input section  206  composed of a keyboard, a mouse, a microphone, and so forth and an output section  207  composed of a display, a speaker, and so forth. The CPU  201  executes various types of processes according to commands that are input from the input section  206 . The CPU  201  outputs the results of the processes to the output section  207 . 
     A storing section  208  connected to the input and output interface  205  is composed of for example a hard disk. The storing section  208  stores programs and various types of data that cause the CPU  201  to execute processes. A communication section  209  communicates with an external device through a network such as the Internet or a local area network. 
     Instead, programs may be obtained through the communication section  209  and stored in the storing section  208 . 
     When a removable medium  211  such as a magnetic disc, an optical disc, a magneto-optical disc, a semiconductor memory, or the like is attached to a drive  210  connected to the input and output interface  205 , the drive  210  causes the removable medium  211  to read and obtain programs, data, and so forth therefrom. When necessary, the obtained programs and data are transferred to the storing section  208  and stored therein. 
     As shown in  FIG. 26 , a program record medium that stores programs installed to and executed by the computer is composed of the removable medium  211  that is a package medium such as a magnetic disc (including a flexible disc), an optical disc (including a CD-ROM (Compact Disc-Read Only Memory), a DVD (Digital Versatile Disc), a magneto-optical disc), or a semiconductor memory), the ROM  202  that temporarily or permanently stores programs, or the hard disk that composes the storing section  208 . When necessary, programs are stored to the program record medium through the communication section  209  that is an interface such as a router or a modem or through a wired or wireless communication medium such as a local area network, the Internet, or a digital satellite broadcast. 
     In this specification, steps that describe a program stored in the program record medium are chronologically processed in the order of which they are described. Instead, these steps may be executed in parallel or discretely. 
     It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and alternations may occur depending on design requirements and other factors insofar as they are within the scope of the appended claims or the equivalents thereof.