CN-116153278-B - Method, system and storage medium for predicting beating points of electric drum

CN116153278BCN 116153278 BCN116153278 BCN 116153278BCN-116153278-B

Abstract

The application provides a method, a system and a storage medium for predicting electric drum beating points, which comprise the steps of obtaining actual beating point information at the current moment and predicted beating point information corresponding to the current moment, judging whether the actual beating point information is identical to the predicted beating point information, if so, using a first prediction model to obtain future beating point information, if not, obtaining a time difference between initial time and the current moment, judging whether the time difference exceeds a preset time period, if so, using the first prediction model to obtain the future beating point information, if not, obtaining different times of the actual beating point information and the predicted beating point information, judging whether the times exceeds the preset times, if not, using the first prediction model to obtain the future beating point information, and if so, adjusting the weight of the first prediction model to obtain a second prediction model, and using the second prediction model to obtain the future beating point information. The application can relieve the problem of delay of the electric drum.

Inventors

TANG ZHENYU
ZHANG JIANXIONG
SHEN PING

Assignees

深圳市魔耳乐器有限公司

Dates

Publication Date: 20260508
Application Date: 20230220

Claims (10)

1. A method of predicting a knock point of an electric drum, the method comprising: Acquiring actual beating point information at the current moment and predicted beating point information corresponding to the current moment, judging whether the actual beating point information is identical to the predicted beating point information, and if so, using a first prediction model corresponding to the current performance attribute to acquire future numerical value future beating point information which represents the future moment of a performer and needs to be beaten; If the time difference is different, obtaining the time difference between the initial time and the current time representing the performance start, judging whether the time difference exceeds a preset time period, and if so, obtaining future numerical value future tapping point information by using the first prediction model; if the number of times is not exceeded, acquiring the number of times that the actual tapping point information is different from the predicted tapping point information in the time difference, judging whether the number of times exceeds a preset number of times, and if the number of times does not exceed the preset number of times, acquiring future numerical value future tapping point information by using a first prediction model; If the number of times exceeds the preset number, adjusting the weight of the first prediction model based on the actual tapping point information and the predicted tapping point information to obtain a second prediction model corresponding to the replaced performance attribute, and using the second prediction model to obtain future numerical value future tapping point information; The method comprises the steps that a current performance attribute corresponds to a stored track, each stored track has respective tapping point information, a deep learning model is adopted to predict tapping point information at a future moment, the tapping point information of the stored track is used for training a BP neural network to obtain a first prediction model, the input of the first prediction model is a past tapping point information set corresponding to the current moment, the past tapping point information set comprises the tapping point information at the current moment and a plurality of tapping point information of a predicted value taking the current moment as a reference past moment direction, the number of the input tapping point information of the first prediction model is a preset value plus one, the output of the first prediction model is the tapping point information required to be tapped at the future moment, and the number of the output tapping point information is a future value; The first prediction model is trained according to a plurality of tracks, has a basic function of predicting future tapping point information according to a past tapping point information set of different tracks, and is obtained by online adjusting weights in the first prediction model to obtain a second prediction model, wherein the first prediction model and the second prediction model both comprise first numerical layer weights.
2. The method of claim 1, wherein using the first predictive model corresponding to the current performance attribute to obtain future number of future tap point information indicative of a future time of a performer that needs to be tapped comprises: Acquiring a past tapping point information set corresponding to the current moment, wherein the past tapping point information set comprises the actual tapping point information and a plurality of past preset numerical value tapping point information taking the current moment as a reference; And taking the past tapping point information set as input, and using a first prediction model to obtain future numerical value future tapping point information, wherein the first prediction model is obtained by training based on the tapping point information corresponding to the stored tracks in the electric drum.
3. The method of claim 2, wherein the using the first predictive model to obtain future values of future tap point information comprises: And replacing the predicted tapping point information with the actual tapping point information to obtain an updated past tapping point information set, and using the past tapping point information set as input, and using a first prediction model to obtain future numerical value future tapping point information.
4. The method of claim 1, wherein the adjusting weights of the first predictive model based on the actual tapping point information and the predicted tapping point information comprises: And acquiring information difference between the actual beating point information and the predicted beating point information, acquiring weight difference between the weight in the first prediction model and the weight in the prediction model corresponding to the actual performance according to the information difference, and adjusting the weight of the first prediction model according to the weight difference.
5. The method of claim 4, wherein the first predictive model includes a first numerical layer weight, wherein obtaining a weight difference between weights in the first predictive model and weights in a predictive model corresponding to an actual performance from the information difference includes: Taking the current moment as a reference point, acquiring a middle tapping point information set which is obtained by subtracting a layer of weight from a previous first numerical value in a first prediction model and corresponds to the previous moment; and obtaining a weight error of the last layer weight of the first prediction model at the last moment according to the intermediate tapping point information set and the information difference, wherein the weight error is the weight difference.
6. The method of claim 2, wherein the value after the preset value is added by one is not less than the future value.
7. The method of claim 1, wherein determining that the actual tapping point information is not the same as the predicted tapping point information further comprises generating a cue signal indicative of a performance error of the performer.
8. A predictive electric drum beating point system is characterized by comprising a processing module, wherein the processing module comprises an acquisition unit, a judging unit, a first predictive unit, an adjusting unit and a second predictive unit, The acquisition unit is used for acquiring actual beating point information at the current moment and predicted beating point information corresponding to the current moment; The judging unit is used for judging whether the actual tapping point information is the same as the predicted tapping point information; The first prediction unit is configured to obtain future number of future tapping point information that characterizes future time points of the performer by using a first prediction model corresponding to the current performance attribute when the actual tapping point information is the same as the predicted tapping point information; the acquisition unit is further used for acquiring a time difference between the initial time and the current moment representing the performance when the actual tapping point information is different from the predicted tapping point information; the judging unit is further used for judging whether the time difference exceeds a preset time period; the first prediction unit is used for obtaining future numerical value future tapping point information by using the first prediction model when the time difference exceeds a preset time period; the acquisition unit is further used for acquiring the times of the actual tapping point information and the predicted tapping point information which are different in the time difference when the time difference does not exceed a preset time period; the judging unit is also used for judging whether the times exceeds preset times; The first prediction unit is further configured to obtain future number of future tapping point information by using a first prediction model if the number of times does not exceed a preset number of times; The adjusting unit is configured to adjust the weight of the first prediction model based on the actual tapping point information and the predicted tapping point information if the number of times exceeds a preset number of times, so as to obtain a second prediction model corresponding to the replaced performance attribute; The second prediction unit is used for obtaining future numerical value future tapping point information by using the second prediction model; The method comprises the steps that a current performance attribute corresponds to a stored track, each stored track has respective tapping point information, a deep learning model is adopted to predict tapping point information at a future moment, the tapping point information of the stored track is used for training a BP neural network to obtain a first prediction model, the input of the first prediction model is a past tapping point information set corresponding to the current moment, the past tapping point information set comprises the tapping point information at the current moment and a plurality of tapping point information of a predicted value taking the current moment as a reference past moment direction, the number of the input tapping point information of the first prediction model is a preset value plus one, the output of the first prediction model is the tapping point information required to be tapped at the future moment, and the number of the output tapping point information is a future value; The first prediction model is trained according to a plurality of tracks, has a basic function of predicting future tapping point information according to a past tapping point information set of different tracks, and is obtained by online adjusting weights in the first prediction model to obtain a second prediction model, wherein the first prediction model and the second prediction model both comprise first numerical layer weights.
9. The system of claim 8, wherein the processing module further comprises a prompting unit for generating a prompting signal indicative of a performance error of the performer after the actual tapping point information is different from the predicted tapping point information.
10. A computer readable storage medium having stored thereon a computer program executable on a processor, wherein the computer program when executed by the processor implements a method of predicting electric drum tapping as claimed in any one of claims 1 to 7.

Description

Method, system and storage medium for predicting beating points of electric drum Technical Field The application relates to the technical field of predicting electric drum beating points, in particular to a method, a system and a storage medium for predicting electric drum beating points. Background The drum kit has varied rhythms and high performance skills of drummers, so that the drum kit rapidly becomes an indispensable instrument in rock music and is welcomed by vast teenagers and friends. At present, people are in a rapid life rhythm, the difficulty of hands on the drum set is relatively high, players are required to have a certain foundation and exercise for a long time, and people can hardly spend a great deal of time to finish the exercise of the drum set. With the development of society, the drum has the same effect as the drum kit because of the quick operation, so that the drum kit is quickly replaced by the drum kit. However, the working principle of the drum is different from that of the drum kit, the drum is a sensor for receiving signals, and when a drummer plays by using the drum, a certain time delay exists, so that the experience of a listener is affected. In the related art, the electric drum has a plurality of beatable points, and a prediction model suitable for predicting the beatable points of the electric drum corresponding to a plurality of tracks is stored, and the prediction model is obtained by training according to the beatable points corresponding to the stored tracks. When a performer plays a certain track, a striking point which needs to be struck at the next moment is already obtained according to the prediction model, and the striking point is ready to be struck. That is, when a player plays a stored track, according to the prediction model, when the player beats a beat point corresponding to the current performance, the electric drum is ready to predict the beat point at the next moment, so that when the player only needs to just start to contact the beat point at the next moment, the electric drum immediately emits a sound which needs to be emitted by the beat point. However, the performer may have errors or play the non-stored tracks in the playing process, and the requirements of people may not be met only according to the existing prediction model, so that the existing problem of the delay of the electric drum cannot be solved. Disclosure of Invention In order to alleviate the problem of drum delay, the embodiment of the application provides a method, a system and a storage medium for predicting a knock point of a drum. In a first aspect, the present embodiment provides a method for predicting a knock point of an electric drum, the method including: Acquiring actual beating point information at the current moment and predicted beating point information corresponding to the current moment, judging whether the actual beating point information is identical to the predicted beating point information, and if so, using a first prediction model corresponding to the current performance attribute to acquire future numerical value future beating point information which represents the future moment of a performer and needs to be beaten; If the time difference is different, obtaining the time difference between the initial time and the current time representing the performance start, judging whether the time difference exceeds a preset time period, and if so, obtaining future numerical value future tapping point information by using the first prediction model; if the number of times is not exceeded, acquiring the number of times that the actual tapping point information is different from the predicted tapping point information in the time difference, judging whether the number of times exceeds a preset number of times, and if the number of times does not exceed the preset number of times, acquiring future numerical value future tapping point information by using a first prediction model; And if the number of times exceeds the preset number, adjusting the weight of the first prediction model based on the actual tapping point information and the predicted tapping point information so as to obtain a second prediction model corresponding to the replaced performance attribute, and using the second prediction model to obtain future numerical value future tapping point information. In some embodiments, the obtaining future number of future tap point information characterizing a future time of the performer that needs to be tapped using the first predictive model corresponding to the current performance attribute includes: Acquiring a past tapping point information set corresponding to the current moment, wherein the past tapping point information set comprises the actual tapping point information and a plurality of past preset numerical value tapping point information taking the current moment as a reference; and using the past tapping point information set as input, and replacing future