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Music model training method, music creation method, devices, terminal and storage medium

A technology for model training and music, applied in computing models, electroacoustic instruments, computing, etc., can solve problems such as labor consumption, reduce creation costs, improve feature extraction capabilities, and improve work efficiency.

Pending Publication Date: 2018-11-13
PING AN TECH (SHENZHEN) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Currently, this is a labor-intensive job done manually by audio technicians who often specialize in specific areas of creation

Method used

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  • Music model training method, music creation method, devices, terminal and storage medium
  • Music model training method, music creation method, devices, terminal and storage medium
  • Music model training method, music creation method, devices, terminal and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0063] figure 1 It is a flowchart of the music model training method provided in the first embodiment of the present invention. According to different needs, the execution order in this flowchart can be changed, and some steps can be omitted.

[0064] S11. Obtain a MIDI music data set, where the MIDI music data set includes multiple MIDI scores.

[0065] MIDI (Musical Instrument Digital Interface) is the most widely used music standard format in the composer world. It can be called a "computer understandable score". It uses digital control signals of musical notes to record music. What MIDI transmits is not sound signals, but instructions such as notes and control parameters. It instructs the MIDI device what to do and how to do it, such as which note to play, how loudly, what pitch to end, what accompaniment to add, and so on. That is, MIDI data includes: MIDI channels, sending information such as time, pitch, velocity, volume, and reverberation of a certain musical instrument to...

Embodiment 2

[0135] figure 2 It is a flowchart of the music creation method provided in the second embodiment of the present invention. According to different needs, the execution order in this flowchart can be changed, and some steps can be omitted.

[0136] S21. Collect a MIDI score composed of several MIDI notes created by the user as the MIDI score to be created.

[0137] The user can play several notes at will on the piano, and when the user has played a few notes, he can stop playing. At this time, the several notes are collected and inputted into the pre-trained music model. You can play a complete piece of music by yourself.

[0138] S22. Extract the pitch sequence of the MIDI score to be created as a third feature vector.

[0139] S23. Extract the time sequence sequence of the MIDI score to be composed as a fourth feature vector.

[0140] S24. Connect the third feature vector and the fourth feature vector to obtain a feature vector of the MIDI score.

[0141] If the method of sequential c...

Embodiment 3

[0150] image 3 It is a functional block diagram of a preferred embodiment of the music model training device of the present invention.

[0151] In some embodiments, the music model training device 30 runs in a terminal. The music model training device 30 may include multiple functional modules composed of program code segments. The program code of each program segment in the music model training device 30 can be stored in a memory and executed by at least one processor to execute (see figure 1 And related descriptions) training of music models.

[0152] In this embodiment, the music model training device 30 of the terminal can be divided into multiple functional modules according to the functions it performs. The functional modules may include: an acquisition module 301, an extraction module 302, a training module 303, and a verification module 304. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least on...

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Abstract

The invention discloses a music model training method. The music model training method comprises the following steps: acquiring a MIDI music data set, wherein the MIDI music data set comprises a plurality of MIDI music scores; extracting the feature vector of each MIDI music score; inputting the feature vectors into a structured support vector machine for training, so that a music model is obtained, the step specifically comprises the following substeps: constructing a discrimination function f(x;w), wherein x is a feature vector, w is a parameter vector, carrying out outputting by adopting the data value (with the calculation formula shown in the description) of the maximal discrimination function f(x;w) as the predicted value, calculating the predicted value and the true value accordingto a preset loss function (shown in the description), wherein P is the probability distribution of data, which is replaced with the empirical risk (shown in the description) obtained through calculation with the trained sample data, solving the unique parameter vector omega by adopting the optimizing formula (as shown in the description) of SVM, so that the empirical risk (shown in the description) obtained through the trained sample data is 0, solving the discrimination function f(x; omega), and finally, outputting the music time sequence. The invention further provides a music creation method, devices, a terminal and a storage medium. In the technical scheme, artificial intelligence is used for music model training for the first time, for the trained music model, the feature extraction capacity of the MIDI music score can be improved.

Description

Technical field [0001] The invention relates to the field of music technology, in particular to a music model training, music creation method, device, terminal and storage medium. Background technique [0002] In all areas of audio creation (for example, studio recording, live performance, broadcasting), a series of signal processing tools are usually used to process audio signals. This includes processing individual audio signals, such as a master mix, and processing and combining multiple audio signals created by different sound sources (e.g., component instruments in an ensemble). The goal of the processing is to improve the aesthetic characteristics of the resulting audio signal, for example, to create a high-quality mix when combining multiple signals; or to adhere to some functional constraints related to transmission, for example, to minimize Signal degradation due to data compression, or to reduce the effects of background noise on the aircraft. Currently, this work is ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10H7/00G06N99/00
CPCG10H7/00G10H2240/021G10H2210/111
Inventor 王义文刘奡智王健宗肖京
Owner PING AN TECH (SHENZHEN) CO LTD
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