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Method and system for piano tuning based on neural network

A technology of neural network and neural network model is applied in the field of piano tuning method and system based on neural network, which can solve problems such as large damage to strings, damage to physical characteristics of strings, and overshoot.

Active Publication Date: 2022-07-01
HEFEI UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It is difficult to achieve the effect purely by machine or manual judgment, and it is very likely that overshooting will occur. At this time, it needs to be adjusted back, because repeated tuning will cause great damage to the strings (making the physical characteristics of the strings destroyed)

Method used

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  • Method and system for piano tuning based on neural network
  • Method and system for piano tuning based on neural network
  • Method and system for piano tuning based on neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0043] see Figure 1 to Figure 2 , is a flowchart of the neural network-based piano tuning method provided by the first embodiment of the present invention, including the steps:

[0044] Step S10, when receiving the tuning instruction, query the standard pitch according to the tuning instruction;

[0045] Wherein, the tuning instruction is transmitted in the form of an electrical signal, a voice signal, a text signal or a wireless signal, and the tuning instruction is used to trigger the tuning step for the target sound key. Preferably, in this step, according to the The query method adopted by the tuning instruction to query the standard pitch is a matching query, a number query or an image query, and the standard pitch is the standard sound corresponding to the target key. Further, the standard pitch can also be The pitch is set independently according to the user's needs, and in this embodiment, the standard pitch is an international standard pitch;

[0046] Step S20, obt...

Embodiment 2

[0059] see image 3 , is a flow chart of the neural network-based piano tuning method provided by the second embodiment of the present invention, including the steps:

[0060] Step S11, when receiving the tuning instruction, query the standard pitch according to the tuning instruction;

[0061] Wherein, the tuning instruction is transmitted in the form of an electrical signal, a voice signal, a text signal or a wireless signal, and the tuning instruction is used to trigger the tuning step for the target sound key. Preferably, in this step, according to the The query method adopted by the tuning instruction to query the standard pitch is a matching query, a number query or an image query, and the standard pitch is the standard sound corresponding to the target key. Further, the standard pitch can also be The pitch is set independently according to the user's needs, and in this embodiment, the standard pitch is an international standard pitch;

[0062] Step S21, acquiring the ...

Embodiment 3

[0093] see Figure 4 , is a schematic structural diagram of the neural network-based piano tuning system 100 provided by the third embodiment of the present invention, including:

[0094] The difference calculation module 10 is used to query the standard pitch according to the tuning instruction when receiving the tuning instruction; obtain the actual pitch of the target key, and calculate the difference between the actual pitch and the standard pitch The difference between the two to obtain the difference pitch, wherein the tuning instruction is transmitted in the form of an electrical signal, a voice signal, a text signal or a wireless signal, and the tuning instruction is used to trigger the tuning of the target key. step, preferably, in this step, the query method used to query the standard pitch according to the tuning instruction is a matching query, a number query or an image query, and the standard pitch is the standard corresponding to the target key. Further, the st...

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Abstract

The invention provides a piano tuning method and system based on neural network. The method includes: querying the standard pitch when receiving a tuning instruction; obtaining the actual pitch of the target key, and calculating the difference between the actual pitch and the standard pitch to obtain the difference pitch; obtaining the current environment Input the temperature and humidity value and the difference pitch into the preset neural network model for calculation to obtain the characteristic parameters; input the characteristic parameters and the difference pitch into the preset PID control system for calculation to output Digital control signal; according to the digital control signal, the tuning execution device is controlled to tune the target sound key. The present invention is designed by inputting the characteristic parameters and the difference pitch into the preset PID control system for calculation, so as to accurately calculate and output the digital control signal, so as to facilitate the subsequent automatic and accurate tuning of the target key, and can ensure the correctness of the target key. The smooth accuracy of the target key adjustment, so as to achieve the effect of fast and accurate piano tuning.

Description

technical field [0001] The invention relates to the technical field of piano tuning, in particular to a method and system for piano tuning based on a neural network. Background technique [0002] Piano tuning is a very tedious job. Traditional manual tuning takes a long time and requires extensive experience. Because environmental factors have a great influence on the piano tuning process, the environmental factors affect the tightness of the strings and the internal sounding environment of the piano, which directly affects the final tuning result. The higher the tuning, the more sensitive it is, and adjusting a little will result in a jump in pitch, which is a non-linear process. It is difficult to achieve the effect by simply relying on machine or manual judgment, and it is very likely that overshoot will occur. At this time, it needs to be adjusted back, which will cause great damage to the strings due to repeated tuning (making the physical characteristics of the string...

Claims

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Application Information

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IPC IPC(8): G05B11/42
CPCG05B11/42
Inventor 彭佳谦刘建
Owner HEFEI UNIV OF TECH
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