JP5272900B2 - Music difficulty calculation device and music difficulty calculation program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To appropriately calculate a music difficulty level by considering various elements constituting music. <P>SOLUTION: A CPU 11 calculates individual difficulty levels for a plurality of elements (fingering, rhythm, and tonality) necessary for performing music based on pitch information, fingering information and time information in music sound data, multiplies each of the plurality of calculated difficulty levels by a weight coefficient, and calculates the overall difficulty level of the music by accumulating the weighted difficulty levels. The CPU 11 optimizes the weight coefficient so that a correlation is largest between a music difficulty level calculation value calculated based on the individual difficulty levels and a difficulty level evaluation value of the same music by a specialist, the evaluation value having been given to the music beforehand. <P>COPYRIGHT: (C)2011,JPO&amp;INPIT

Description

  The present invention relates to a music difficulty level calculation device and a music difficulty level calculation program for evaluating the difficulty level of music.

  Conventionally, when evaluating the difficulty level of a music piece, music education specialists and performers have made subjective evaluations based on various elements in the music piece, for example, a series of musical notes. In the conventional subjective evaluation, for example, there is no objective difficulty level regarding the constituent elements of the music, and the evaluation of the entire music (for example, 10-level evaluation, etc.) is often performed. .

  On the other hand, an apparatus for calculating the degree of difficulty of fingering based on fingering information when playing a musical piece with a keyboard instrument has been proposed. In Patent Document 1, assuming all cases where two sounds of a certain pitch are pressed with two fingers, it is difficult to define the costs in all the cases and perform the fingering indicated by the fingering information. A difficulty level calculation method for calculating the degree has been proposed.

JP 2006-78656 A

  However, the conventional apparatus for calculating the difficulty level has a problem that it is not easy to define cost values for all cases where two sounds are pressed with two fingers. In addition, the degree of difficulty of music is not limited to fingering, but includes various other elements. Therefore, it is desirable to calculate the difficulty level in consideration of other factors.

  In addition, it is not desirable that the difficulty calculated in consideration of various factors deviates from the evaluation given to the same music by the music education specialists and performers, and the calculated difficulty is also It is desirable that it is almost the same as an expert's evaluation.

  An object of the present invention is to provide a music difficulty level calculation device and a music difficulty level calculation program that appropriately calculate the difficulty level of a music in consideration of various elements constituting the music.

An object of the present invention is to store musical tone data including pitch information for each note constituting a musical piece, fingering information for pressing the note, and time information about the note;
Based on the pitch information and fingering information, a fingering difficulty calculating means for calculating a fingering difficulty relating to fingering for pressing an adjacent note;
Rhythm difficulty calculating means for calculating a rhythm difficulty related to the note length and the timing of the note based on time information about the note;
Based on the pitch information of the notes, a tonality difficulty calculating means for calculating a tonality difficulty relating to the tonality of the music,
Multiply each of the calculated fingering difficulty, rhythm difficulty, and tonality difficulty by a weighting factor to accumulate each of the weighted fingering difficulty, rhythm difficulty, and tonality difficulty. To calculate the difficulty level of the entire music, and to store the calculated difficulty level in the storage means,
Weight coefficient optimization means for optimizing a weight coefficient for multiplying each of the fingering difficulty, the rhythm difficulty, and the tonality difficulty;
The weight coefficient optimization means comprises:
Weight coefficient candidate value acquisition means for acquiring a weight coefficient candidate value that is a peripheral value of the basic value of the weight coefficient for each of the fingering difficulty, the rhythm difficulty, and the tonality difficulty;
The difficulty calculation value is calculated using a combination of the calculated fingering difficulty, rhythm difficulty and tonality difficulty, and a weighting factor candidate value created by combining the respective weighting factor candidate values. In addition, a difficulty calculation value calculation means for performing the operation of changing the combination and creating a new weight coefficient candidate value pair and calculating the difficulty calculation value again for all combinations;
A correlation coefficient between each of the difficulty calculation values calculated by the difficulty calculation value calculation means and the difficulty evaluation value by the expert stored in the storage means is calculated, and the calculated correlation coefficient is the maximum. Correlation calculating means that sets the weighting factor candidate values constituting the combination of weighting factor candidate values to be the weighting factors of the fingering difficulty, the rhythm difficulty, and the tonality difficulty,
Music difficulty level calculating device characterized in that it comprises a.

  In a more preferred embodiment, the weighting factor optimizing means calculates the weighting factor candidate value by multiplying the basic value by a factor that changes within a predetermined range.

Another object of the present invention is to provide storage means for storing musical tone data including pitch information for each note constituting a musical piece, fingering information for pressing the note, and time information about the note. Computer
Based on the pitch information and fingering information, a fingering difficulty calculation step for calculating a fingering difficulty for a finger pressing a neighboring note;
A rhythm difficulty calculation step for calculating a rhythm difficulty related to the note length and the timing of the note based on time information about the note;
Based on the pitch information of the notes, a tonality difficulty calculating step for calculating a tonality difficulty relating to the tonality of the music,
Multiply each of the calculated fingering difficulty, rhythm difficulty, and tonality difficulty by a weighting factor to accumulate the weighted fingering difficulty, rhythm difficulty, and tonality difficulty, respectively. Calculating the degree of difficulty of the entire music, and storing the calculated degree of difficulty in the storage means; and
Performing a weighting factor optimization step of optimizing a weighting factor that multiplies each of the fingering difficulty, the rhythm difficulty, and the tonality difficulty ;
The weighting factor optimization step includes:
A weighting coefficient candidate value acquisition step for acquiring a weighting coefficient candidate value that is a peripheral value of a basic value of the weighting coefficient for each of the fingering difficulty, the rhythm difficulty, and the tonality difficulty;
The difficulty calculation value is calculated using a combination of the calculated fingering difficulty, rhythm difficulty and tonality difficulty, and a weighting factor candidate value created by combining the respective weighting factor candidate values. In addition, a difficulty calculation value calculation step for changing the combination to create a new weight coefficient candidate value pair and calculating the difficulty calculation value again for all combinations;
A correlation coefficient between each of the calculated difficulty calculation values and a difficulty evaluation value by a specialist stored in the storage unit is calculated, and a weight coefficient candidate value that maximizes the calculated correlation coefficient is calculated. A correlation calculation step in which the weighting factor candidate values constituting the combination are weighting factors of the fingering difficulty, the rhythm difficulty, and the tonality difficulty;
It is accomplished by the music difficulty level calculation program for causing the execution.

  According to the present invention, it is possible to provide a music difficulty level calculation device and a music difficulty level calculation program that appropriately calculate the difficulty level of music in consideration of various elements constituting the music.

FIG. 1 is a block diagram showing a configuration of a music difficulty level calculating device according to an embodiment of the present invention. FIG. 2 is a flowchart showing an outline of processing executed in the music difficulty level calculation device 10 according to the present embodiment. FIG. 3 is a diagram illustrating a data configuration example of music data according to the present embodiment. FIG. 4 is a flowchart schematically showing an example of the difficulty level evaluation process according to the present embodiment. FIG. 5 is a flowchart showing an example of the fingering difficulty evaluation process according to the present embodiment. FIG. 6 is a flowchart showing an example of the fingering difficulty evaluation process according to the present embodiment. FIG. 7 is a flowchart showing an example of technique classification processing according to the present embodiment. FIG. 8 is a flowchart showing an example of technique classification processing according to the present embodiment. FIG. 9 is a flowchart showing in detail an example of the finger opening specifying process according to the present embodiment. FIG. 10 is a flowchart showing an example of the rhythm difficulty level evaluation process according to the present embodiment. FIG. 11 is a flowchart showing an example of the rhythm difficulty evaluation process according to the present embodiment. FIG. 12 is a flowchart showing an example of rhythm cost calculation processing according to the present embodiment. FIG. 13 is a flowchart showing an example of rhythm cost calculation processing according to the present embodiment. FIG. 14 is a flowchart illustrating an example of tonality difficulty level evaluation processing according to the present embodiment. FIG. 15 is a flowchart illustrating an example of tonality difficulty level evaluation processing according to the present embodiment. FIG. 16 is a diagram for explaining the key position information in the present embodiment. FIG. 17A is a diagram illustrating an example of the base scale array iBaseScale [] according to the present embodiment, and FIG. 17B is a diagram illustrating an example of the non-base scale array iATonal [] according to the present embodiment. It is. FIG. 18 is a diagram for explaining the calculation of the degree of coincidence iSum with the major scale according to the present embodiment. FIG. 19 is a diagram for explaining the calculation of the inconsistency iATonality with the major scale according to the present embodiment. FIG. 20 is a flowchart illustrating an example of the coefficient optimization process according to the present embodiment. FIG. 21 is a flowchart showing an example of optimum value calculation processing according to the present embodiment. FIG. 22 is a flowchart showing an example of the optimum value calculation process according to the present embodiment. FIG. 23 is a flowchart illustrating an example of the difficulty level calculation process according to the present embodiment.

  Embodiments of the present invention will be described below with reference to the accompanying drawings. FIG. 1 is a block diagram showing a configuration of a music difficulty level calculating device according to an embodiment of the present invention. As shown in FIG. 1, the music difficulty level calculation device 10 according to this embodiment includes a CPU 11, a ROM 12, a RAM 13, a large-scale storage device 14, an input device 15, a display device 16, a sound system 17, and a keyboard 18. ing.

  The CPU 11 executes various processes (a fingering difficulty evaluation process, a rhythm difficulty evaluation process, a tonality difficulty evaluation process, and an overall difficulty calculation process) for evaluating the difficulty level of the music described later. The ROM 12 is used for programs and processes of various processes (fingering difficulty evaluation process, rhythm difficulty evaluation process, tonality difficulty evaluation process, and overall difficulty calculation process) for evaluating the musical sound difficulty level. Stores parameters and so on. The RAM 13 temporarily stores parameters, input data, output data and the like generated in the course of program execution. The large-scale storage device 14 is a hard disk device or a memory card, and stores music data and the like.

  The input device 15 includes switches, a keyboard, a mouse, and the like, and gives the CPU 11 various instructions based on an operator's switch operation, key operation, and mouse operation. On the screen of the display device 12, image data including a score based on the music data, the evaluation result of the music difficulty, and the like are displayed. The sound system 40 includes a musical sound generation circuit, an amplifier, a speaker, and the like, generates a predetermined musical sound according to music data or according to a key pressing operation of the keyboard 18 by an operator, and outputs an acoustic signal based on the musical sound. . In the above embodiment, the keyboard 18 is not essential.

  Further, the music difficulty level evaluation device 10 according to the present embodiment may be in the form of a keyboard instrument, or may be in the form of a personal computer. In the form of a keyboard instrument, the operator refers to the score displayed on the screen of the display device 16 and presses the key on the keyboard 18, and the sound system 17 responds to the key pressed by the operator. Thus, the musical sound can be output from the sound system 17 by generating musical tone data corresponding to the pitch of the pressed key.

  FIG. 2 is a flowchart showing an outline of processing executed in the music difficulty evaluation device 10 according to the present embodiment. As shown in FIG. 2, the CPU 11 of the music difficulty level evaluation apparatus 10 according to the present embodiment initializes including clearing parameters stored in the RAM 13, clearing the image displayed on the screen of the display device 16, and the like. Is executed (step 201). Next, the CPU 11 determines whether the operator has operated the input device 15 to input a music designation (step 202). For example, input of music designation is realized by inputting a music data number.

  If it is determined Yes in step 202, the CPU 11 reads the music data of the designated music from the music data group stored in the large-scale storage device 14, and temporarily stores it in the RAM 13 (step 203). ). Next, the CPU 11 executes a difficulty level evaluation process, and calculates a value iCost indicating the difficulty level of the music whose data has been read (step 204). If it is determined No in step 202, the CPU 11 determines whether the operator operates the input device 15 to input a coefficient optimization instruction (step 205). When it is determined Yes in step 205, the CPU 11 executes a coefficient optimization process (step 206). The difficulty level evaluation process and the coefficient optimization process will be described in detail below.

  FIG. 3 is a diagram illustrating a data configuration example of music data according to the present embodiment. As shown in FIG. 3, the music data of a certain music has a record of musical sound data (see reference numerals 301 and 302) indicating the musical sound to be generated for each note (note). A record of musical sound data (for example, record Note [0] of the first note: code 301) includes pitch information “Pit”, keyboard position information “Pos”, fingering information “Fig”, pronunciation time “Time”, and pronunciation. It includes items of a time “Gate”, a pointer “* Next” to the record of the next note, and a pointer “* prev” to the record of the previous note.

The pitch information “Pit” is, for example, a value obtained by incrementing “1” every time the pitch is increased by setting the pitch name “C-1” to “0”. The keyboard position information “Pos” is a coordinate value indicating the lateral center position of the key corresponding to the pitch. For example, as shown in FIG. 16, if the keyboard position information Pos = X ₀ of C at a certain position (for example, C4: Pit = 60), the keyboard position information from C # 4 to C5 is X ₁ to the values as shown in X _12. As can be understood from FIG. 16, the difference in coordinate values between the adjacent white key and black key, and between the black key and white key is “1”. On the other hand, the difference in coordinate values between consecutive white keys (E and F, B and C) is “2”.

  FIG. 4 is a flowchart schematically showing an example of the difficulty level evaluation process according to the present embodiment. As shown in FIG. 4, the CPU 11 performs a fingering difficulty evaluation process (step 401), a rhythm difficulty evaluation process (step 402), a tonality difficulty evaluation (step 403), and an overall difficulty calculation process (step 404). ). In the fingering difficulty evaluation process, the CPU 11 calculates the fingering difficulty iFCost based on the fingering information in the musical sound data. In the rhythm difficulty level evaluation process, the CPU 11 calculates the rhythm difficulty level RCost based on the sound generation time and sound generation time in the musical sound data. In the tonality difficulty level evaluation process, the CPU 11 calculates the tonality difficulty level iTCost based on the pitch information of the musical tone data. The fingering difficulty level, the rhythm difficulty level, and the tonality difficulty level calculated in steps 401 to 403 respectively correspond to individual difficulty levels.

  The CPU 11 multiplies the weighting factors RF, RR, RT, and RC based on the calculated fingering difficulty iFCost, rhythm difficulty iRCost, tonality difficulty iTCost, and the number of notes (iCnt) in the music data, respectively. The degree of difficulty iCost of the music is calculated by adding (step 404). That is, the difficulty level iCost is expressed by the following formula.

iCost = iFCost × RF + iRCost × RR
+ ITCost x RT + iCnt x RC
Hereinafter, the fingering difficulty evaluation process, the rhythm difficulty evaluation process, and the tonality difficulty evaluation process will be described in detail. 5 and 6 are flowcharts showing an example of the fingering difficulty evaluation process according to the present embodiment. In the present embodiment, the music data is composed of musical tone data for musical notes of a music piece (melody) played with the right hand. Therefore, in the fingering difficulty level, the fingering difficulty level when playing with the right hand is calculated. In the music data, the fingering information of the musical sound data record indicates with which finger the right hand is pressed. Finger numbers “1” to “5” indicate that the keys should be pressed with the thumb to the little finger, respectively.

  As shown in FIG. 5, the CPU 11 initializes a parameter iCnt indicating the number of notes (musical notes) of the music indicated in the musical sound data to “0” (step 501), and the first musical sound data in the musical piece data in the RAM 13. Record Note [0] is stored in array me [] (step 502). The CPU 11 repeats steps 504 to 515 until the processing is completed for all musical sound data records in the music data (Yes in step 503).

  First, the CPU 11 specifies a record of data with the previous musical sound of the musical sound data stored in the array me [], and stores it in the array prev [] (step 504). Next, the CPU 11 executes technique classification processing based on the values stored in the array me [] and the array prev [] (step 505). In the technique classification process, the fingering between adjacent notes of the music is classified into four classification items: position type (PosType), finger opening type (iSpreadType), reverse type (RevType), and finger usage technique (Tech). It is judged which of each corresponds.

  Here, the classification item of position type (PosType) indicates at what kind of finger position fingering is performed. The finger opening type (iSpreadType) indicates the opening degree of the finger pressing the adjacent note. The reverse type (RevType) indicates the presence of a finger penetration or a finger turn (getting over) described later. Further, the fingering technique (Tech) indicates what kind of fingering (finger change) is performed when the adjacent note is pressed.

  In the technique classification process, for each classification item, a classification value indicating a specific requirement in the classification item is calculated. 7 and 8 are flowcharts showing an example of technique classification processing according to the present embodiment. The CPU 11 sets f1 = Fig, p1 = Pit, and ps1 = Pos for fingering information “Fig”, pitch information “Pit”, and keyboard position information “Pos” in the array me []. Further, based on the pitch information “Pit”, it is determined whether the key is a white key or a black key, and information indicating either the white key or the black key is b1. The value b1 takes either a value “1” indicating that the key is a black key or a value “0” indicating that the key is a white key. These values f1, p1, ps1, and b1 are stored in the RAM 13.

  The CPU 11 sets f2 = Fig, p2 = Pit, and ps2 = Pos for the fingering information “Fig”, pitch information “Pit”, and keyboard position information “Pos” in the array prev []. Further, based on the pitch information “Pit”, it is determined whether the key is a white key or a black key, and information indicating either the white key or the black key is b2. These values f2, p2, ps2, and b2 are stored in the RAM 13. The CPU 11 calculates an index iDist indicating a pitch difference based on p1, p2, f1, and f2 (step 703).

  The index iDist indicating the pitch difference indicates that the finger number is increased or decreased when the progress of the note is upward in the music, and the finger number is decreased or increased when the progress is downward. It is an index indicating whether or not In the present embodiment, iDist is obtained as follows.

iDist = (p1-p2) * (f1-f2)
Therefore, when iDist> 0, the finger is used to increase the finger number and increase the number of notes, or the finger is used to increase the finger number and decrease the number of fingers. Means. That is, when iDist> 0, it is indicated that fingering is performed in a form in which the finger is opened (no finger penetration or finger turning is performed).

  The CPU 11 determines whether f2 = f1 and p2 = p1 (step 704). The case of “Yes” in step 704 indicates that keys having the same pitch are pressed with the same finger between adjacent notes. If it is determined as Yes in step 704, the CPU 11 gives a value of “Tech = 3” as the classification value Tech for the classification item “finger use technique (Tech)” (step 705). The given value is stored in the RAM 13. Next, the CPU 11 determines whether f2 ≠ f1 and p2 = p1 (step 706). The case of “Yes” in step 706 indicates that keys having the same pitch are pressed with different fingers between adjacent notes. If it is determined Yes in step 706, the CPU 11 gives a value of “Tech = 2” for the classification item “finger use technique (Tech)” (step 707).

  Next, the CPU 11 determines whether f2 = f1 and p2 ≠ p1 (step 708). The case of Yes in step 708 indicates that keys having different pitches between adjacent notes are pressed with the same finger. When it is determined Yes in step 708, the CPU 11 gives a value of “Tech = 4” for the classification item “finger use technique (Tech)” (step 709). When it is determined No in step 708, the CPU 11 gives a value of “Tech = 1” for the classification item “finger use technique (Tech)” (step 710).

  Next, the CPU 11 determines whether iDist> 0 is satisfied (step 801). As described above, iDist> 0 indicates that fingering is performed in such a manner that the finger is opened (no finger penetration or finger turning is performed). When it is determined Yes in step 801, the CPU 11 sets PosType = iPType as the classification value PoType based on the predetermined table iPType [] [] [] [] based on b2, b1, f2, and f1. [B2] [b1] [f2] [f1] are acquired (step 802). The table iPType [] [] [] [] includes values based on the finger number and the type of white key / black key for adjacent notes, and is stored in the RAM 13.

  The values in the table iPType [] [] [] [] will be briefly described below. The table iPtype [] [] [] [] is a table that stores the position type of a finger based on the relationship between the finger number of the finger to be placed on an adjacent note and its position (white key or black key). . For example, if both the current note and the previous note are white keys, iPType [0] [0] [f2] [f1] = 1. If the previous note is a thumb and a white key is pressed, iPType [0] [1] [1] [f1] = 2 when the current note is a black key. . If the previous note is a thumb and a black key is pressed, iPType [1] [1] [1] [f1] = 3 when the current note is a black key. .

  As described above, in the table iPtype, the value increases as the difficulty of the peeling of the hand or the position of the finger when pressing adjacent notes is increased.

  After step 802 is executed, the CPU 11 executes finger opening identification processing (step 803). FIG. 9 is a flowchart showing in detail an example of the finger opening specifying process according to the present embodiment.

  As shown in FIG. 9, the CPU 11 acquires iMin, iL1 and iL2 (iMin <iL1 <iL2), which are parameters indicating the degree of finger opening, from the RAM 13 (step 901). For example, iMin is “2” corresponding to a width of 2 degrees in the white key, iL1 is “4” corresponding to 3 degrees in the white key, and iL1 is completely 8 degrees in the white key. It is “14”. In the finger opening specifying process, iDist is compared with iMin, iL1, and iL2, and the classification value of the finger opening type (iSpreadType) is determined.

  The CPU 11 calculates a distance iKDist = | ps2-ps1 | based on the keyboard position (step 902).

  When iDist <0 calculated in Step 703 (Yes in Step 903), the CPU 11 gives a value of “iSpreadType = 0” as a classification value iSpreadType for the classification item of the finger opening type (iSpreadType) ( Step 904). When FIG. 9 is executed, it is assumed that iDist> 0 (see step 801 in FIG. 8). Therefore, since this cannot actually occur, step 904 is an error process.

  When iKDist = iMin (Yes in Step 905), the CPU 11 gives a value of “iSpreadType = 1” as the classification value iSpreadType for the classification item of the finger opening type (iSpreadType) (Step 906). When iMin <iKDist ≦ iL1 (Yes in Step 907), the CPU 11 gives a value of “iSpreadType = 2” to the classification item of the finger opening type (iSpreadType) (Step 908). When iKDist <iMin (Yes in Step 909), the CPU 11 gives a value of “iSpreadType = 3” to the classification item of the finger opening type (iSpreadType) (Step 910).

  When iKDist <iL2 (Yes in Step 911), the CPU 11 gives a value of “iSpreadType = 4” to the classification item of the finger opening type (iSpreadType) (Step 912). On the other hand, if iKDist ≧ iL2 (No in step 911), the CPU 11 gives a value of “iSpreadType = 5” to the classification item of the finger opening type (iSpreadType) (step 913).

  By the finger opening specifying process, the following iSpreadType values are assigned according to the iKDist value.

0 ≦ iKDist <Min iSpreadType = 3
iKDist = iMin iSpreadType = 1
iMin <iKDist ≦ iL1 iSpreadType = 2
iL1 <iKDist <iL2 iSpreadType = 4
iL2 ≦ iKDist iSpreadType = 5
When the finger opening identification process ends, the CPU 11 gives a classification value “RevType = 1” as the classification value RevType for the classification item of the reverse type (RevType) (step 804). Thereafter, the CPU 11 stores the classification values calculated for the musical tone data stored in the array me [], such as PosType, iSpreadType, RevType, and Tech, in the storage device (RAM 13) in association with the musical tone data (step 805).

  If it is determined No in step 801, RevType = iRTtype [p2] [p1] [f2] [f1] is acquired based on a predetermined table iRTType [] [] [] [] (step 806). . The table iRTType [] [] [] [] includes values based on finger numbers and pitches for adjacent notes, and is stored in the RAM 13.

  The values in the table iRTType [] [] [] [] will be briefly described below. The table iRtype [] [] [] [] stores information indicating finger penetration and finger turn-over (overcoming) in fingering based on the relationship between finger numbers and pitches of fingers to be placed on adjacent notes. It is. In the present embodiment, when iDist ≦ 0, in the ascending sound form, the finger having the finger number larger than “1” is shifted to the finger having the finger number “1” (so-called “finger penetration”), or the descending sound In terms of shape, it means that a finger having a finger number “1” can be shifted to a finger having a finger number larger than “1” (so-called “finger turn” or “finger over”).

  In the present embodiment, iRtype [p2] [p1] [f2] [f1] = 3 in the case of finger penetration, that is, when p2 <p1, f2> 1 and f1 = 1. When the finger is turned back, that is, when p2> p1, f2 = 1, and f1> 1, iRTtype [p2] [p1] [f2] [f1] = 2. In general, it is said that fingering is more difficult for fingering than fingering, so RevType = 3 for fingering and RevType = 2 for fingering.

  After step 806, the CPU 11 gives a classification value of “PosType = 1” for the classification item of position type (PosType), and a classification value of “iSpreadType = 1” for the classification item of the finger opening type (iSpreadType) (step 806). 807). Thereafter, the CPU 11 stores the classification values calculated for the musical tone data stored in the array me [], such as PosType, iSpreadType, RevType, and Tech, in the storage device (RAM 13) in association with the musical tone data (step 805).

  Next, in the two-dimensional map iFMap [i] [j] (i is a classification item, j is a value assigned to the classification item), a process of incrementing a value corresponding to the classification item obtained by the technique classification process is executed. (Steps 506 to 512 in FIG. 5). In the present embodiment, there are four classification items: a position type (PosType), a finger opening type (iSpreadType), a reverse type (RevType), and a fingering technique (Tech). Accordingly, each element of the two-dimensional map Map [i] [j] corresponds to the following classification item.

iFMap [0] [j]: j is a value of “PosType” iFMap [1] [j]: j is a value of “iSpreadType” iFMMap [2] [j]: j is a value of “RevType” iFMMap [3] [ j]: j is a value of “Tech” Note that, as can be understood from the following steps 506, 508, 510, 512, there is no value when j = 0, 2 and there is a value only for j> 1. .

  If PosType> 1 (Yes in Step 506), the CPU 11 increments iFMap [0] [PosType], which is the value of the corresponding map (Step 507). If iSpreadType> 1 (Yes in Step 508), the CPU 11 increments iFMap [1] [iSpreadType], which is the value of the corresponding map (Step 509). If RevType> 1 (Yes in Step 510), the CPU 11 increments iFMap [2] [RevType], which is the value of the corresponding map (Step 511). Further, when Tech> 1 (Yes in step 512), the CPU 11 increments iFMap [3] [Tech], which is the value of the corresponding map (step 513).

  Next, the CPU 11 increments the parameter iCnt indicating the number of notes (number of notes) (step 514), stores the record of the next musical sound data in the array me [] (step 515), and then returns to step 503. .

  If it is determined Yes in step 503, the CPU 11 initializes the parameters i and iFCost to “0” (step 601). The CPU 11 determines whether the parameter i is “4” or more (step 602). The parameter i is for specifying “i” of iFMap [i] [j]. If it is determined No in step 602, the parameter j is initialized to “0” (step 603), and the processes in steps 605 to 607 are repeated until j becomes larger than a predetermined number (step 604).

  The CPU 11 determines whether the map value iFMap [i] [j] is greater than “0” (step 605). If it is determined Yes in step 605, the CPU 11 adds the cost value iFCost [i] [j] associated with the map value iFMap [i] [j] to the fingering difficulty value iFCost. (Step 606).

  Hereinafter, the cost table (iFCost [] [] []) storing the cost value iFCost [i] [j] will be described. The cost table stores “i × j” number of cost values iFCost [i] [j] corresponding to each of the cost values iFCost [i] [j]. For example, iFCost [0] [j] is a cost value corresponding to each value of “PosType” acquired in step 802 of the technique classification process (FIG. 8), and is a value unique to each value of the position type. It has become. The same applies to other cost values of i = 1 or more.

  In the present embodiment, as the value increases in each of the position type “PosType”, the finger opening type “iSpreadType”, the reverse type “RevType”, and the fingering technique “Tech”, the operation is performed for each classification item. The difficulty level of fingers is increasing. Therefore, the cost value iFCost [i] [j] also increases as j increases.

  Also, in the present embodiment, the number of map values iFMap [i] [j] is not considered, and if it is greater than “0” (ie, “1” or more), the cost value iFCost [ i] [j] is added to the fingering difficulty value iFCost. That is, regardless of the number of map values, the added value is the cost value iFCost [i] [j]. This is because if there is one note having a value greater than 1 in iFMap [i] [j] in the classification item “i” (for example, position type or finger-open type), in this case, the iFMap [i ] [J] is included, the number of notes is based on the knowledge that the cost value indicating the difficulty level will not be significantly affected.

Of course, iFCost [i] [j] to be added may be given a weight according to the map value iFMap [i] [j]. In this case,
iCost = iCost +
f (iFCost [i] [j]) × iFCost [i] [j]
It becomes. Here, f (iFCost [i] [j]) increases as the variable iFCost [i] [j] increases, but the function value increases as the variable iFCost [i] [j] increases. The increment of f is a decreasing function. For example, a logarithmic function may be used as f.

  When it is determined No in step 605 or after step 606 is executed, the CPU 11 increments j (step 607) and returns to step 604. If YES is determined in step 604, the CPU 11 increments i (step 608) and returns to step 602.

  As described above, in the present embodiment, when a note included in a musical piece has a map value iMap [i] [j] for a specific aspect of the classification item related to fingering, the classification item is not “0”. The cost value iFCost [i] [j] corresponding to the specific mode is added to the fingering difficulty iFCost.

  Next, the rhythm difficulty level evaluation process will be described. 10 and 11 are flowcharts showing an example of the rhythm difficulty evaluation process according to the present embodiment. As shown in FIG. 10, the CPU 11 stores the record Note [0] of the first musical sound data in the music data in the RAM 13 in the array me [] (step 1001). The CPU 11 repeats steps 1003 to 1007 until the processing is completed for all musical sound data records in the music data (Yes in step 1002).

  The CPU 11 acquires a rhythm evaluation process parameter (rhythm parameter) stored in the RAM 13 (step 1003). The rhythm parameter includes iMeasLen indicating the length of one measure, Resolution indicating the resolution of the rhythm, and the like, which are used in the next rhythm cost calculation process. The CPU 11 executes a rhythm cost calculation process (step 1004). 12 and 13 are flowcharts showing an example of rhythm cost calculation processing according to the present embodiment. In the rhythm cost calculation process, it corresponds to the classification value for the step time (iStepTime), which is the classification item corresponding to the note length of the note (note length based on the interval to the next note), and the position in the measure of the note. A classification value for the position (iPos), which is a classification item, is obtained.

  As shown in FIG. 12, the CPU 11 refers to the next record of the musical sound data records shown in the array me [], and acquires the pronunciation time Time (step 1201). Next, the CPU 11 calculates the interval iT to the next note based on the pronunciation time Time in the array me [] and the pronunciation time Time in the record of the next pronunciation data acquired in step 1201 (step 1202). . Based on the time interval iT and the length iMeasLen of one measure, the CPU 11 calculates the note length iStepTime within the measure of the time interval (step 1203). The obtained iStepTime is a classification value for the classification item of note length.

  In the present embodiment, the note length iStepTime is obtained as follows.

iStepTime = 32 × iT / iMeasLen
In this embodiment, the music has a 4/4 time signature, and iStepTime indicates how many 32nd notes correspond to iT.

  Next, the CPU 11 calculates the note position iTickInMeas within the measure, the note position iTickInBeat within the beat, and the beat iBeat to which the note belongs based on the sound generation time Time in the array me [] (step 1304). iTickInMeas indicates the position of a note when a measure is divided into 32 parts, that is, the number of 32nd notes from the beginning of the measure. ITickInBeat indicates the number of the 32nd note from the beginning of the beat.

  The CPU 11 initializes the parameters i and iPos to “0” (step 1205), and executes the processing of steps 1207 to 1210 as long as i is less than 8 (Yes in step 1206). The CPU 11 calculates a = iTickInBeat-iResolution × i / 8 in order to specify the position of iTickInBeat (step 1207). Here, iTickInBeat takes any value from “0” to “7”. Therefore, for any i, a = 0 and it is determined Yes in the next step 1208. If it is determined No in step 1208, the CPU 11 increments the parameter i (step 1209) and returns to step 1206. If YES is determined in step 1208, the CPU 11 sets the parameter iPos = i (step 1210).

  If it is determined No in step 1206, or after the processing in step 1210 is completed, the process proceeds to step 1301 in FIG. In this embodiment, when iTickInBeat is calculated in step 1204, it is quantized to the resolution of a 32nd note. Therefore, it is impossible to determine No in step 1210. Therefore, in this case, an error has occurred in the calculation.

  The iPos obtained in this way indicates the position of the note at the 32nd note from the beginning of the beat. Although this may be used as it is, in the present embodiment, different values are given as iPos as the beginning of the measure, the center of the measure, and the beginning of the beat. If iTickInMeas = 0, that is, if the note is located at the beginning of the beat (Yes in step 1301), the CPU 11 sets iPos = 32 (step 1302). If iTickInBeat = 0 and iBeat = 2, that is, if the note is located at the beginning of the third beat (the center of the measure) (Yes in step 1303), the CPU 11 sets iPos = 16 (step 1304). ). Furthermore, if iTickInBeat = 0, that is, if the note is positioned at the beginning of the second or fourth beat (Yes in step 1305), the CPU 11 sets iPos = 8 (step 1306).

  The classification value of the position (iPos), which is one of the classification items obtained by the rhythm cost calculation process, takes the following values depending on the position in the measure of the note.

The note position is the beginning of the measure: iPos = 32
Note position is at the beginning of the third beat: iPos = 16
The position of the note is the beginning of another beat (2nd beat, 4th beat): iPos = 8
When the position of the note is other: iPos = a value indicating the position when the beat is divided into eight equal parts The above-mentioned iPos is an example in the case where the music has 4/4 time, and other time signatures (for example, 3/4) In some cases, iPos is different. If the music is 3/4 time, the classification value “iPos” can take the following values.

The note position is the beginning of the measure: iPos = 32
Note position is the beginning of other beats (2nd beat, 3rd beat): iPos = 8
When the position of the note is other: iPos = a value indicating the position when the beat is divided into eight equal parts When the rhythm cost calculation process is completed, the CPU 11 increments iRMa [0] [iStepTime] which is the value of the two-dimensional map At the same time as (Step 1005), iRMMap [1] [iPos] is incremented (Step 1006). Thereafter, the CPU 11 stores a record of the next musical sound data in the array me [] (step 1007), and then returns to step 1002.

  When it is determined Yes in step 1002, the CPU 11 initializes the parameters i and iRCost to “0” (step 1101). The CPU 11 determines whether the parameter i is “2” or more (step 1102). The parameter i is for specifying “i” of iRMMap [i] [j]. When it is determined No in step 1102, the parameter j is initialized to “0” (step 1103), and the processing of steps 1105 to 1107 is repeated until j becomes larger than a predetermined number (step 1104).

  The CPU 11 determines whether the map value iRMMap [i] [j] is greater than “0” (step 1105). When it is determined Yes in step 1105, the CPU 11 adds the cost value iRCost [i] [j] associated with the map value iRMMap [i] [j] to the fingering difficulty value iRCost. (Step 1106).

  Hereinafter, the cost table iRCost [] [] storing the cost value iRCost [i] [j] will be described. Similar to the cost table storing the cost values iR [i] [j] for the fingering difficulty, the cost table iRCost [] [] corresponds to each of the cost values iRCost [i] [j]. Xj ”cost values iRCost [i] [j] are stored. For example, iRCost [0] [j] is a cost value corresponding to each note length iStepTime acquired in step 1203 of the rhythm cost calculation process (FIG. 12), and is a value specific to each note length. Yes. IRCost [1] [j] is a cost value corresponding to each note position iPos in the beat obtained in steps 1210, 1302, 1304, and 1306, and is a unique value for each note position. ing.

  For example, in iRCost [0] [iStepTime], the cost value iRCost [0] [iStepTime] increases for iStepTime = 32, 16, 8, 4, and other values. That is, when the note length is a full note, its cost value iRCost [0] [32] is the smallest, and the cost value becomes as the second note, quarter note, eighth note, and other notes become. growing.

  Also in iRCost [1] [iPod], the cost value iRCost [1] [iPos] increases for iPos = 32, 16, 8 and other values. That is, when the note is positioned at the beginning of the measure, the cost value iRCost [1] [32] is the smallest, and the beginning of the third beat, the beginning of the second beat, the beginning of the fourth beat, and other positions. The cost value will increase.

  Similar to the calculation of the fingering difficulty level cost, the number of map values iRMMap [i] [j] is not considered, and if it is greater than 0 (that is, 1 or more), the cost value iRCost [i] [J] is added to the rhythm difficulty value iRCost. That is, regardless of the number of map values, the added value is the cost value iRCost [i] [j].

  When it is determined No in step 1105 or after step 1106 is executed, the CPU 11 increments j (step 1107) and returns to step 1104. If YES is determined in step 1104, the CPU 11 increments i (step 1108) and returns to step 1102.

  Thus, in the present embodiment, the corresponding cost value iRCost [i] [j] is added to the rhythm difficulty iRCost for each of the rhythms of the notes, particularly the note length and note position.

  Next, the tonality difficulty level evaluation process according to the present embodiment will be described. 14 and 15 are flowcharts showing an example of the tonality difficulty level evaluation process according to the present embodiment. As shown in FIG. 15, the CPU 11 stores a record of the first musical sound data in the array me [] (step 1401). The CPU 11 repeats steps 1403 and 1404 until the processing is completed for all musical sound data records in the music data (Yes in step 1402).

  The CPU 11 increments the count value iPC [Pit mod 12] based on the pitch information “Pit” in the array me [] (step 1403). In step 1403, the count value iPC corresponding to any note name from C to B of the note is incremented. Next, the CPU 11 stores the record of the next musical sound data in the array me [] (step 1404), and returns to step 1402. Through the processing in steps 1402 to 1404, an array iPC [] storing the accumulated values is obtained for each note name of notes (notes) included in the music.

  If it is determined as Yes in step 1402, the CPU 11 initializes the parameters iTonality, iMax, iATMax and i to “0” (steps 1405 to 1407). The CPU 11 executes steps 1501 to 1512 in FIG. 15 until the parameter i becomes 12 or more (No in step 1408).

  As shown in FIG. 15, the CPU 11 initializes parameters iSum and iATonality to “0”. Here, iSum is a parameter indicating the degree of coincidence between the pitch value of notes (notes) included in the music and the major scale of each key, and iATonality is the cumulative value of the pitch name, This is a parameter indicating the degree of inconsistency with the major scale of each key. In the present embodiment, the degree of coincidence and inconsistency with the major scale of each key is found for the music of the major scale, but the present invention is not limited to this. As for the music of minor scales, it is only necessary to find the degree of coincidence or inconsistency with the minor scale of each key.

  The CPU 11 initializes the parameter j to “0” (step 1504), and adds iPC [(j + i) mod 12] × iBaseScale [j] to iSum. The CPU 11 adds iPC [(j + i) mod 12] × iATonal [j] to iATonality (step 1505). Thereafter, the CPU 11 increments j and returns to step 1503.

  FIG. 17A is a diagram illustrating an example of the base scale array iBaseScale [] according to the present embodiment. As shown in FIG. 17A, iBaseScale [] (reference numeral 1701) has 11 elements (j = 0 to 11), and elements corresponding to the major scale pitch names (j = 0, 2, 4). 5, 7, 9, 11) is “1”, and the others are “0”. In FIG. 17A, numbers, 数字 (flat), and # (sharp) arranged on the elements of the array indicate the interval (degree) with respect to the leftmost element (root sound) of j = 0. ing. FIG. 17B is a diagram illustrating an example of the non-base scale array iATonal [] according to the present embodiment. iATonal [] (see reference numeral 1702) has 11 elements (j = 0 to 11), similar to iBaseScale [] 1701, and its value is the reverse of iBaseScale. That is, the elements (j = 1, 3, 6, 8, 10) that do not correspond to the note names of the major scale are “1”, and other elements are “0”.

  The value iPC [(j + i) mod 12] × iBaseScale [j] added to iSum in step 1504 will be described below. For example, in FIG. 18, reference numeral 1801 is a diagram illustrating an example of the array iPC [] obtained in steps 1402 to 1404, and reference numeral 1802 is a diagram illustrating an example of the base scale array iBaseScale [].

In iPC [j + i mod 12] × iBaseScale [j], when i = 0, j = 0, 2, 4, 5, 7, and 9 take values other than 0, and processing is performed for all j The final value of iSum is
iSum = Σ (iPC [j mod 12] × iBaseScale [j])
= 7 + 2 + 3 + 10 + 4 + 5 = 31.

When i = 1, j = 4, 5, 9, 11 takes a value other than 0, and when the process is executed for all j, the final value of iSum is
iSum = 1 + 3 + 5 + 8 = 17.

Further, when i = 5, j = 0, 2, 4, 5, 7, 9, 22 takes a value other than 0, and the final value of iSum when processing is executed for all j is
iSum = 10 + 4 + 5 + 8 + 7 + 2 + 3 = 39.

Further, when i = 7, when j = 0, 2, 5, 7, and 9, a value other than 0 is taken, and the final value of iSum when processing is executed for all j is
iSum = 4 + 5 + 7 + 2 + 3 = 22.

  The operation of adding iPC (j + i mod 12) × iATonal [j] to iATonality can be performed in the same manner as the iSum operation.

  In FIG. 19, reference numeral 1901 is a diagram illustrating an example of the array iPC [] obtained in steps 1402 to 1404, and reference numeral 1902 is a diagram illustrating an example of the non-base scale array iATonity []. The array iPC [] is the same as the reference numeral 1801 in FIG.

When i = 0, the final value of iATonality is iATonality = Σ (iPC [j mod 12] × iATonal [j]
= 1 + 5 + 8 = 14.

Also, the final value of iATonality when i = 1 is
iATonality = 7 + 2 + 10 + 4 + 5 = 28 (see reference numeral 1903)
The final value of iATonality when i = 5 is
iATonality = 5 + 1 = 6 (see reference numeral 1904)
The final value of iATonality when i = 7 is
iATonality = 5 + 8 + 1 + 10 = 24 (see reference numeral 1905).

  When it is determined No in step 1503, that is, when iSum and iATonality of j = 0 to 11 are calculated for a specific i, the CPU 11 calculates the difference value a (== the matching degree iSum and the mismatching degree iATonality. iSum-iATonality) is calculated (step 1507).

  The CPU 11 determines whether or not the difference value a obtained in step 1507 is larger than the maximum value iMax of the difference value a stored in the RAM 13 (step 1508). If it is determined Yes in step 1508, the CPU 11 sets the difference value a as the maximum value iMax (step 1509) and sets i to the parameter iTonality indicating the tonality when the maximum value iMax is obtained. (Step 1510). Further, the CPU 11 sets the obtained inconsistency degree iATonality as the corresponding inconsistency degree iAMax when the maximum value of iSum is obtained (step 1511).

  When it is determined No in step 1508 or after step 1511 is executed, the CPU 11 increments i and returns to step 1408. When it is determined No in step 1408, the CPU 11 calculates iTCost indicating the tonality difficulty level as follows (step 1409).

iTCost = (iATMax × A) / iMax
Here, A is a predetermined constant. Therefore, iTCost is a constant A based on the ratio of the degree of mismatch at this time to the difference value between the degree of coincidence iSum with the musical note scale and the degree of mismatch iATonality at the time when the degree of coincidence of tonality is maximized. Multiplied by.

  As described above, when the fingering difficulty iFCost, the rhythm difficulty iRCost, and the tonality difficulty iTCost are calculated, the difficulty of the music is as follows based on the difficulty and the number of notes iCnt of the music. iCost is calculated.

iCost = iFCost × RF + iRCost × RR
+ ITCost x RT + iCnt x RC

  Next, the coefficient optimization process (step 206 in FIG. 2) according to the present embodiment will be described in more detail. In the coefficient optimization process, when calculating the difficulty level iCost of the music, the weighting coefficients RF, RR, and RT multiplied by each element of the fingering difficulty level, the rhythm difficulty level, and the tonality difficulty level are optimized. Schematically, the weighting coefficient is calculated so that the degree of difficulty of each element in the music becomes the same result as when a person evaluates. FIG. 20 is a flowchart showing an example of coefficient optimization processing according to this embodiment. As shown in FIG. 20, the CPU 11 initializes a parameter opt indicating the number of repetitions of the process to “0” (step 2001), and until the parameter opt exceeds a predetermined number (Yes in step 2002), step Repeat 2003-2010.

  The CPU 11 initializes a parameter i for specifying the element of the difficulty level of the music to “0” (step 2003). Here, i = 0 indicates a fingering difficulty, i = 1 indicates a rhythm difficulty, and i = 2 indicates a tonality difficulty. The CPU 11 repeats steps 2005 to 2008 as long as the parameter i is less than the element number Emax (in this case, “3”) (step 2004).

  Next, the CPU 11 determines whether or not the parameter opt indicating the number of repetitions is “0”, that is, the first process (step 2005). When it is determined Yes in step 2005, the basic value a [i] of the weighting coefficient is set to a predetermined initial value ini [i] (step 2006). The initial value ini [i] may be an arbitrary value (for example, ini [i] = 1). If it is determined No in step 2005, the basic value a [i] is set as the maximum value bmax [i] of the candidate weight coefficient values obtained in the previous processing (step 2007). Note that finally obtained bmax [0] is a weighting factor RF for the fingering difficulty, bmax [1] is a weighting factor RR for the rhythm difficulty, and bmax [2] is a weighting factor for the tonality difficulty. RT. When steps 2006 and 2007 are executed, the CPU 11 increments the parameter i (step 2008) and returns to step 2004.

  When it is determined No in step 2004, the CPU 11 executes an optimum value calculation process (step 2009). Thereafter, the CPU 11 increments the parameter opt indicating the number of repetitions (step 2010), and returns to step 2002.

  21 and 22 are flowcharts showing an example of the optimum value calculation process according to the present embodiment. The basic value a [i] is considered as the center value of the weighting coefficient to be obtained. In the optimum value calculation process, candidate values b [] are obtained for all combinations of values iVar around the basic value a [] by a triple loop using the parameters i, j, and k. Further, the difficulty level (difficulty level calculation value) using the candidate value b [] is obtained, the correlation between the difficulty level and the difficulty level evaluation value by the expert is calculated, and the maximum value rmax of the obtained correlation values. Is obtained. Hereinafter, the optimum value calculation process will be described in more detail.

  As shown in FIG. 21, the CPU 11 initializes the maximum correlation value rmax (step 2101) and initializes the parameter i (step 2102). Next, as long as the parameter i is smaller than the parameter iVar indicating the predetermined number of peripheral values (Yes in step 2103), the CPU 11 executes the processing from step 2104 onward. If i ≧ iVar (No in step 2103), the process ends.

  If it is determined Yes in step 2103, the CPU 11 initializes the parameter j (step 2104), and executes the processing from step 2106 onward as long as the parameter j is smaller than the predetermined parameter iVar (Yes in step 2105). To do. If j ≧ iVar (No in step 2105), the CPU 11 increments the parameter i (step 2112) and returns to step 2103.

  If it is determined Yes in step 2105, the CPU 11 initializes the parameter k (step 2106), and executes the processing from step 2108 onward as long as the parameter k is smaller than the predetermined parameter iVar (Yes in step 2107). To do. If k ≧ iVar (No in step 2107), the CPU 11 increments the parameter j (step 2111) and returns to step 2105.

  If it is determined as Yes in step 2107, the CPU 11 sets a value for each of the candidate values b [0] to b [2] (steps 2108 to 2110). In steps 2108 to 2110, a candidate value b [] is calculated as follows.

b [0] = a [0] × d [i]
b [1] = a [1] × d [j]
b [2] = a [2] × d [k]
d [i], d [j], and d [k] are predetermined coefficients for multiplying the basic value a [] in order to calculate the candidate value b []. Takes a value between 0.0 and 9.9. For example, parameters i, j, and k are set such that d [0] = 0.0, d [1] = 0.2, d [2] = 0.6... D [6] = 9.9. The coefficient increases as it increases.

  After step 2110, the CPU 11 executes difficulty level calculation processing (step 2201). In the difficulty level calculation process, a difficulty level calculation value out [] for calculating a correlation value is calculated based on the difficulty level of a predetermined number (SongN) of songs for which the difficulty level has been calculated in advance. FIG. 23 is a flowchart illustrating an example of the difficulty level calculation process according to the present embodiment. As shown in FIG. 23, in the difficulty level calculation process, the CPU 11 initializes a parameter p for specifying a song to “0” (step 2301), as long as the parameter p is less than the total number of songs SongN (step 2302). Yes), the processing of steps 2303 to 2308 is repeated.

  The CPU 11 initializes the difficulty level calculation value out [p] to “0” (step 2303), and specifies parameters j for specifying the difficulty level elements (the fingering difficulty level, the rhythm difficulty level, the tonality difficulty level). Is initialized to “0” (step 2304). The CPU 11 executes steps 2306 to 2307 as long as the parameter q is less than the element number Emax (Yes in step 2305). The CPU 11 calculates the difficulty calculation value out [p] = out [p] + data [q] [p] × b [q] (step 2306). Here, data [q] [p] is an element specified by the parameter q (fingering if q = 0, rhythm if q = 1, and q = 2 for the music specified by the parameter p). The degree of difficulty calculated for the tonality if any. data [q] [p] is calculated in advance and stored in the RAM 13. Thereafter, the CPU 11 increments the parameter q (step 2307) and returns to step 2305.

  If NO is determined in step 2305, the CPU 11 increments the parameter p (step 2308) and returns to step 2302.

  When the difficulty level calculation process ends, the CPU 11 correlates the difficulty level calculation value out [k] obtained in the difficulty level calculation process with the expert difficulty level evaluation value dat [k] stored in the RAM 13 in advance. r is obtained (step 2202). For example, the correlation value can use the following general correlation coefficient.

上記式において、ｘｉ＝ｏｕｔ［ｉ］、ｙｉ＝ｄａｔ［ｉ］、ｎ＝ＳｏｎｇＮである。ＣＰＵ１１は、ステップ２２０２で算出された相関値ｒが、以前の処理にて得られていた相関値の最大値ｒｍａｘより大きいかを判断する（ステップ２２０３）。ステップ２２０３でＮｏと判断された場合には、ＣＰＵ１１は、パラメータｋをインクリメントして（ステップ２２０９）、ステップ２１０７に戻る。

In the above formula, xi = out [i], yi = dat [i], and n = SongN. The CPU 11 determines whether the correlation value r calculated in step 2202 is larger than the maximum correlation value rmax obtained in the previous process (step 2203). If it is determined No in step 2203, the CPU 11 increments the parameter k (step 2209) and returns to step 2107.

  On the other hand, if it is determined Yes in step 2203, the CPU 11 initializes the parameter n to “0”, and as long as n is less than the number of elements Emax (Yes in step 2205), steps 2206 to 2208 are performed. Run. The CPU 11 temporarily stores the current candidate value b [n] as the maximum value bmax [n] in the RAM 13 (step 2206) and temporarily stores the current correlation value r as the maximum correlation value rmax in the RAM 13. Store (step 2207). Thereafter, the CPU 11 increments the parameter n (step 2208) and returns to step 2205. If it is determined No in step 2205, the CPU 11 increments the parameter k (step 2209) and returns to step 2109. Through the steps 2205 to 2208, candidate values b [0] to b [2] of the coefficient and the maximum value rmax of the correlation value r are obtained for each difficulty level element.

  As described above, the optimum value calculation process (step 2209) is executed a predetermined number of times, and the process is repeated, so that bmax [0] to bmax [2] gradually approach the value determined by the expert. Go. The obtained bmax [0], bmax [1], and bmax [2] are stored in a storage device such as the RAM 13, and in the calculation of the total difficulty (step 404) in the difficulty evaluation process (FIG. 4), respectively. The weighting factor RF multiplied by the tonality difficulty level iFCost, the weighting factor RR multiplied by the rhythm difficulty level iRCost, and the weighting factor RT multiplied by the tonality difficulty level iTCost.

  In the present embodiment, the CPU 11 calculates individual difficulty levels for elements such as fingering, rhythm, and tonality necessary for the performance of a music, and multiplies each of the calculated individual difficulty levels by a weight coefficient. Then, the difficulty level of the entire music is calculated by accumulating the individual difficulty levels assigned with weights. In addition, the CPU 11 calculates the difficulty level calculation value of the music calculated based on the individual difficulty level calculated in advance and stored in the storage unit, and the difficulty evaluation value of the same music by an expert previously assigned to the music. The weighting factor by which the individual difficulty level is multiplied is optimized so as to maximize the correlation with.

  Therefore, it is possible to calculate the difficulty level so that the result does not deviate from the expert evaluation while adding the individual difficulty levels for each objective element.

  In the present embodiment, for each element of fingering, rhythm, and tonality, a weight coefficient candidate value b [i] that is a peripheral value of the basic value a [i] of the weight coefficient is obtained, and all elements The difficulty calculation value is calculated using a set of weight coefficient candidate values b [0] to b [2] for. By changing the combination, creating a new set of weighting factors, and calculating the difficulty level calculation value, the weighting factor candidate value that calculated the difficulty level calculation value when the correlation with the difficulty evaluation value was the largest Can be acquired properly.

  In the present embodiment, the CPU 11 calculates the weighting coefficient candidate value b [i] by multiplying the basic value a [i] by the coefficient d [j] that changes within a predetermined range. That is, the maximum value bmax [i] is obtained from the combination of candidate values b [i] that change around the basic value a [i].

  Further, in the present embodiment, the CPU 11 determines the note length based on the fingering difficulty regarding fingering for pressing an adjacent note based on the pitch information and the fingering information, and the time information about the note. Based on the rhythm difficulty relating to the timing of the note and the pitch information of the note, the tonality difficulty relating to the tonality of the music is calculated. Therefore, it is possible to appropriately calculate the difficulty level of the music in consideration of various elements constituting the music.

  The present invention is not limited to the above embodiments, and various modifications can be made within the scope of the invention described in the claims, and these are also included in the scope of the present invention. Needless to say.

10 Music difficulty calculation device 11 CPU
12 ROM
13 RAM
14 Large-scale storage device 15 Input device 16 Display device 17 Sound system 18 Keyboard

Claims (3)

Storage means for storing musical tone data including pitch information for each note constituting the musical piece, fingering information for pressing the note, and time information about the note;
Based on the pitch information and fingering information, a fingering difficulty calculating means for calculating a fingering difficulty relating to fingering for pressing an adjacent note;
Rhythm difficulty calculating means for calculating a rhythm difficulty related to the note length and the timing of the note based on time information about the note;
Based on the pitch information of the notes, a tonality difficulty calculating means for calculating a tonality difficulty relating to the tonality of the music,
Multiply each of the calculated fingering difficulty, rhythm difficulty, and tonality difficulty by a weighting factor to accumulate each of the weighted fingering difficulty, rhythm difficulty, and tonality difficulty. To calculate the difficulty level of the entire music, and to store the calculated difficulty level in the storage means,
Weight coefficient optimization means for optimizing a weight coefficient for multiplying each of the fingering difficulty, the rhythm difficulty, and the tonality difficulty;
The weight coefficient optimization means comprises:
Weight coefficient candidate value acquisition means for acquiring a weight coefficient candidate value that is a peripheral value of the basic value of the weight coefficient for each of the fingering difficulty, the rhythm difficulty, and the tonality difficulty;
The difficulty calculation value is calculated using a combination of the calculated fingering difficulty, rhythm difficulty and tonality difficulty, and a weighting factor candidate value created by combining the respective weighting factor candidate values. In addition, a difficulty calculation value calculation means for performing the operation of changing the combination and creating a new weight coefficient candidate value pair and calculating the difficulty calculation value again for all combinations;
A correlation coefficient between each of the difficulty calculation values calculated by the difficulty calculation value calculation means and the difficulty evaluation value by the expert stored in the storage means is calculated, and the calculated correlation coefficient is the maximum. Correlation calculating means that sets the weighting factor candidate values constituting the combination of weighting factor candidate values to be the weighting factors of the fingering difficulty, the rhythm difficulty, and the tonality difficulty,
Music difficulty level calculating device characterized in that it comprises a. 2. The music difficulty level calculation device according to claim 1 , wherein the weight coefficient optimization unit calculates the weight coefficient candidate value by multiplying a basic value by a coefficient that changes within a predetermined range. A computer comprising storage means for storing musical tone data including pitch information for each note constituting the music, fingering information for pressing the note, and time information about the note,
Based on the pitch information and fingering information, a fingering difficulty calculation step for calculating a fingering difficulty for a finger pressing a neighboring note;
A rhythm difficulty calculation step for calculating a rhythm difficulty related to the note length and the timing of the note based on time information about the note;
Based on the pitch information of the notes, a tonality difficulty calculating step for calculating a tonality difficulty relating to the tonality of the music,
Multiply each of the calculated fingering difficulty, rhythm difficulty, and tonality difficulty by a weighting factor to accumulate the weighted fingering difficulty, rhythm difficulty, and tonality difficulty, respectively. Calculating the degree of difficulty of the entire music, and storing the calculated degree of difficulty in the storage means; and
Performing a weighting factor optimization step of optimizing a weighting factor that multiplies each of the fingering difficulty, the rhythm difficulty, and the tonality difficulty ;
The weighting factor optimization step includes:
A weighting coefficient candidate value acquisition step for acquiring a weighting coefficient candidate value that is a peripheral value of a basic value of the weighting coefficient for each of the fingering difficulty, the rhythm difficulty, and the tonality difficulty;
The difficulty calculation value is calculated using a combination of the calculated fingering difficulty, rhythm difficulty and tonality difficulty, and a weighting factor candidate value created by combining the respective weighting factor candidate values. In addition, a difficulty calculation value calculation step for changing the combination to create a new weight coefficient candidate value pair and calculating the difficulty calculation value again for all combinations;
A correlation coefficient between each of the calculated difficulty calculation values and a difficulty evaluation value by a specialist stored in the storage unit is calculated, and a weight coefficient candidate value that maximizes the calculated correlation coefficient is calculated. A correlation calculation step in which the weighting factor candidate values constituting the combination are weighting factors of the fingering difficulty, the rhythm difficulty, and the tonality difficulty;
A program for calculating the degree of difficulty of music.

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