Music generation device, music generation method, music generation program, model generation device, model generation method, and model generation program

Abstract

To provide a technique to facilitate creation of musical pieces with various degrees of difficulty.SOLUTION: A musical piece creation device according to an aspect of the present invention acquires object musical piece data indicating at least a part of a musical piece, acquires the value of a difficulty degree parameter for designating the degree of difficulty after a change of the musical piece, and creates new musical piece data indicating at least a part of a new musical piece obtained by changing the degree of difficulty of the musical piece to the degree of difficulty designated by the difficulty degree parameter from the acquired object musical piece data and value of the difficulty degree parameter by using a trained creation model.SELECTED DRAWING: Figure 1

Claims

1. A data acquisition unit that acquires target song data representing at least a portion of the song, A parameter acquisition unit that obtains the value of the difficulty parameter, A generation unit that uses a trained generation model to generate new song data that represents at least a portion of a new song obtained by changing the difficulty level of the song to the difficulty level specified by the difficulty parameter, from the acquired target song data and the value of the difficulty parameter, An output unit that outputs the newly generated music data, Equipped with, The value of the difficulty parameter is configured to indicate the maximum number of controls to be operated simultaneously on the instrument used for the new song after the difficulty has been changed. Music generation device.

2. The value of the difficulty parameter is configured to indicate the range of the playable notes of the new song after the difficulty level has been changed. The music generation apparatus according to claim 1.

3. The aforementioned target song data consists of a sequence of input tokens arranged to represent at least a portion of the song, The new song data consists of a sequence of output tokens output from the trained generative model and arranged to represent at least a portion of the new song. A music generation apparatus according to claim 1 or 2.

4. Computers A step of obtaining target song data that represents at least a portion of the song, Steps to obtain the value of the difficulty parameter, The steps include: using a trained generative model to generate new song data that represents at least a portion of a new song obtained by changing the difficulty level of the song to the difficulty level specified by the difficulty parameter, from the acquired target song data and the value of the difficulty parameter; The steps include outputting the newly generated music data, Execute, The value of the difficulty parameter is configured to indicate the maximum number of controls to be operated simultaneously on the instrument used for the new song after the difficulty has been changed. Music generation method.

5. On the computer, A step of obtaining target song data that represents at least a portion of the song, Steps to obtain the value of the difficulty parameter, The steps include: using a trained generative model to generate new song data that represents at least a portion of a new song obtained by changing the difficulty level of the song to the difficulty level specified by the difficulty parameter, from the acquired target song data and the value of the difficulty parameter; The steps include outputting the newly generated music data, Make it run, The value of the difficulty parameter is configured to indicate the maximum number of controls to be operated simultaneously on the instrument used for the new song after the difficulty has been changed. A music generation program.

6. A learning data acquisition unit that acquires multiple learning datasets, each composed of a combination of training data and ground truth data, The aforementioned training data includes training music data representing at least a portion of the music and difficulty parameters for learning. The aforementioned correct answer data includes new learning song data that shows at least a portion of the new songs generated by changing the difficulty level of the songs in the learning song data to the difficulty level specified by the difficulty parameter. The training data acquisition unit, A learning processing unit that performs machine learning of a generative model using the acquired plurality of training datasets, wherein the machine learning is comprised of training the generative model so that, for each of the training datasets, the music data generated by the generative model from the training music data and the difficulty parameter values ​​included in the training data fits the training music data included in the ground truth data, Equipped with, The value of the difficulty parameter is configured to indicate the maximum number of controls to be operated simultaneously on the instrument used for the new song after the difficulty has been changed. Model generation device.

7. The value of the difficulty parameter included in the training data is configured to indicate the range of the playable notes of the new song after the difficulty level has been changed. The model generation apparatus according to claim 6.

8. Computers A step of obtaining multiple training datasets, each composed of a combination of training data and ground truth data, The aforementioned training data includes training music data representing at least a portion of the music and difficulty parameters for learning. The aforementioned correct answer data includes new learning song data that shows at least a portion of the new songs generated by changing the difficulty level of the songs in the learning song data to the difficulty level specified by the difficulty parameter. Steps and A step of performing machine learning on a generative model using the acquired plurality of training datasets, wherein the machine learning consists of training the generative model for each training dataset so that the music data generated by the generative model from the training music data and the difficulty parameter values ​​included in the training data fits the training music data included in the ground truth data. Execute, The value of the difficulty parameter is configured to indicate the maximum number of controls to be operated simultaneously on the instrument used for the new song after the difficulty has been changed. Model generation method.

9. On the computer, A step of obtaining multiple training datasets, each composed of a combination of training data and ground truth data, The aforementioned training data includes training music data representing at least a portion of the music and difficulty parameters for learning. The aforementioned correct answer data includes new learning song data that shows at least a portion of the new songs generated by changing the difficulty level of the songs in the learning song data to the difficulty level specified by the difficulty parameter. Steps and A step of performing machine learning on a generative model using the acquired plurality of training datasets, wherein the machine learning consists of training the generative model for each training dataset so that the music data generated by the generative model from the training music data and the difficulty parameter values ​​included in the training data fits the training music data included in the ground truth data. Make it run, The value of the difficulty parameter is configured to indicate the maximum number of controls to be operated simultaneously on the instrument used for the new song after the difficulty has been changed. Model generation program.

Images