Music monitoring analysis method, server and storage medium
By setting song titles and performance mode information, using crawler technology to obtain music information, and perform similarity calculations, the problem of the existing technology being unable to monitor music popularity in different singers or performance modes is solved, and a more extensive and comprehensive music popularity monitoring is achieved.
Patent Information
- Application Number
- CN202411601094.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-11
- Publication Date
- 2025-03-04
AI Technical Summary
The prior art cannot monitor the music itself, especially the popularity of music in different singers or performance modes.
By setting the music information to be monitored, including song name information and performance method information, using crawler technology to obtain relevant information, and matching and summarizing the music information through similarity calculation methods, the music popularity monitoring of different performance methods can be achieved.
The scope of music monitoring has been expanded, and the popularity of music in different singers and performance modes can be monitored, avoiding the problem of traditional methods not being able to detect other performance modes.
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of music popularity monitoring, and in particular relates to a music monitoring and analysis method, a server and a storage medium. Background Art
[0002] Music heat analysis mainly includes methods based on audio feature analysis and methods based on video data association analysis. The method based on audio feature analysis mainly involves extracting the audio features of music, such as spectrum features, melody features, harmony features, and instrument features, and processing and analyzing these features through a neural network model to predict the heat of music. This method does not require the accumulation of historical behavior data of users, thereby reducing labor costs and time costs. The specific steps include determining the audio features of music, processing these features through a neural network model, identifying the temporal position of music clips, classifying based on temporal features, determining the structural type of music clips, determining the heat weight based on the structural type, and performing weighted operations on the heat value, and finally obtaining the heat value of the entire music. The method based on video data association analysis extracts audio data from the video, identifies the corresponding music data, and constructs a corresponding relationship between the video and the music data. This method determines the predicted heat of music based on the association data of the video and the music data in a preset time interval, without relying on manpower, and realizes the heat prediction of music data from the video dimension. These two methods have their own characteristics. The former focuses on predicting popularity through the intrinsic audio features of music, while the latter uses the correlation between audio data and music data in the video to predict music popularity. Both provide effective means for monitoring music popularity.
[0003] Obviously, the current analysis of music popularity is mainly based on prediction. However, the market often needs to monitor the real-time popularity of music. Currently, the monitoring method is also based on the analysis of the designated music of the designated singer as the monitoring object. The analysis method mainly reads the data of the number of music plays, the number of reposts and the number of collections. However, for the music itself, some music will also receive different degrees of attention from other singers. The existing music popularity prediction or monitoring has no way to monitor different singers or different performance methods. In essence, it lacks the means to analyze the music itself.
[0004] Therefore, an effective music analysis method is needed to solve the current problem that the music itself cannot be monitored. Summary of the invention
[0005] The purpose of the present invention is to provide a music monitoring and analysis method to solve the problem in the prior art that the music itself cannot be monitored.
[0006] In order to achieve the above object, the present invention adopts the following technical solutions:
[0007] In a first aspect, the present invention provides a music monitoring and analysis method, comprising the following steps:
[0008] Determine the music information to be monitored, wherein the music information includes at least one designated song title information and at least one performance method information corresponding to the song title information;
[0009] Using crawler technology to search for the music information to be monitored on a designated platform to obtain all basic information of the music information to be monitored on the platform;
[0010] All basic information obtained is processed uniformly, and the processed information is obtained and stored in the database;
[0011] Extracting basic information to be matched from the processed information according to target information of the music information to be monitored;
[0012] Calculate the similarity between the target information of the music information to be monitored and the basic information to be matched to obtain a similarity value;
[0013] The music information corresponding to the results reaching the predetermined similarity threshold is taken out and summarized, and the summarized music information is output.
[0014] According to the above technology, by setting the music information to be monitored to include at least one specified song title information and at least one performance method information corresponding to the song title information, different performance methods, such as cover, pure music, etc. are all within the monitoring range, making the monitoring range wider and more comprehensive, avoiding the traditional direct reading of the song forwarding volume, likes, and collection volume, which leads to the inability to detect the popularity monitoring of other performance methods, and realizing the monitoring of other performance methods by cooperating with the music similarity calculation method to determine whether it is related music information; at the same time, by using one or more song title information as the music information to be monitored, all relevant music information of the specified platform is crawled and analyzed to realize the simultaneous monitoring of multiple music information; thus, by realizing the acquisition of all relevant information of the same song, it is convenient to find cover, different interpretation methods or different performance methods. Information can be matched.
[0015] Based on the above technology, further, the similarity calculation method includes:
[0016] Initializing a preset two-dimensional matrix; the rows of the two-dimensional matrix represent the elements of the first sequence of music information to be monitored, and the columns represent the processed information elements of the second sequence;
[0017] Fill the two-dimensional matrix with the elements of the first sequence of music information to be monitored and the processed information elements of the second sequence, and calculate the similarity between the two sequences;
[0018] In the process of filling the matrix, a recursive relationship algorithm is used to determine the similarity value of each element according to the similarity of the sequence. By using a two-dimensional matrix to compare the music element information with the corresponding elements, the music elements to be monitored can be quickly compared.
[0019] On the basis of the above technology, further, the recursive relationship algorithm is as follows:
[0020] Assume that the size of the two-dimensional matrix is (len(a)+1)*(len(b)+1), where len(a) and len(b) are two sequences to be compared; for each element (i, j) in the matrix, it represents the similarity between the first i elements of sequence a and the first j elements of sequence b;
[0021] If a[i]==b[j], then matrix[i+1][j+1]=matrix[i][j]+1; where a[i]==b[j] means that the first i elements of sequence a match the first j elements of sequence b, and the matrix similarity value increases;
[0022] If a[i]!=b[j], then matrix[i+1][j+1]=max(matrix[i+1][j],matrix[i]); where a[i]!=b[j] means that the first i elements of sequence a do not match the first j elements of sequence b, then the larger value of the left and the top of the two-dimensional matrix is taken as the value of the current element;
[0023] When the matrix is filled, the similarity is obtained based on the lower right corner element of the matrix, that is, matrix[len(a)][len(b)].
[0024] On the basis of the above technology, further, the unified processing includes removing special symbols, garbled information and converting to uppercase and lowercase.
[0025] On the basis of the above technology, further, the basic information includes at least three types of information among song title, performer, composer information, lyrics information, word segmentation information and keyword information.
[0026] On the basis of the above technology, further, a method for uniformly processing all the basic information obtained includes: uniformly processing all the basic information obtained by using a regular expression method.
[0027] A second aspect of the present invention provides a server for music monitoring and analysis, which is communicatively connected to user terminals respectively, and is used to execute the music monitoring and analysis method as described in any possible design of the first aspect.
[0028] A third aspect of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the music monitoring and analysis method as described in any possible design of the first aspect.
[0029] Beneficial effect: By setting the music information to be monitored to include at least one designated song title information and at least one performance information corresponding to the song title information, different performances, such as covers, pure music, etc., are all within the monitoring range, making the monitoring range wider and more comprehensive, avoiding the traditional direct reading of the song forwarding volume, the number of likes, and the number of collections, which leads to the inability to detect the heat monitoring of other performances. By coordinating the music similarity calculation method to determine whether it is related music information, the monitoring of other performances can be achieved; at the same time, by using one or more song title information as the music information to be monitored, all relevant music information on the designated platform is crawled and analyzed to achieve simultaneous monitoring of multiple music information; thus, by achieving the acquisition of all relevant information of the same song, it is convenient to find covers, different interpretations, or different performances. Information can be matched. By using a two-dimensional matrix to compare the corresponding elements of the music element information, the music elements to be monitored can be quickly compared. DETAILED DESCRIPTION
[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly introduced below in combination with the description of the embodiments or the prior art. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.
[0031] Example:
[0032] In a first aspect, the present invention provides a music monitoring and analysis method, comprising the following steps:
[0033] Determine the music information to be monitored, wherein the music information includes at least one designated song title information and at least one performance method information corresponding to the song title information;
[0034] Using crawler technology to search for the music information to be monitored on a designated platform to obtain all basic information of the music information to be monitored on the platform;
[0035] All basic information obtained is processed uniformly, and the processed information is obtained and stored in the database;
[0036] Extracting basic information to be matched from the processed information according to target information of the music information to be monitored;
[0037] Calculate the similarity between the target information of the music information to be monitored and the basic information to be matched to obtain a similarity value;
[0038] The music information corresponding to the results reaching the predetermined similarity threshold is taken out and summarized, and the summarized music information is output.
[0039] According to the above technology, one or more song title information can be used as the music information to be monitored to crawl and analyze all relevant music information on the specified platform, so as to obtain all relevant information of the same song, and facilitate matching of cover songs, different interpretations or different performances.
[0040] Based on the above technology, in a possible implementation, the similarity calculation method includes:
[0041] Initializing a preset two-dimensional matrix; the rows of the two-dimensional matrix represent the elements of the first sequence of music information to be monitored, and the columns represent the processed information elements of the second sequence;
[0042] Fill the two-dimensional matrix with the elements of the first sequence of music information to be monitored and the processed information elements of the second sequence, and calculate the similarity between the two sequences;
[0043] In the process of filling the matrix, a recursive relation algorithm is used to determine the similarity value of each element based on the similarity of the sequence.
[0044] Specifically, a matrix is constructed to compare the characters at each position of the two sequences. According to the similarity between the characters, the values in the matrix are filled with corresponding scores, and finally an optimal comparison result is obtained. This process involves not only sequence alignment, but also similarity calculation, so as to evaluate the similarity between the two sequences.
[0045] Specifically, it includes the following steps:
[0046] Construct a matrix: First, construct a two-dimensional matrix for the two sequences, where each position of the matrix represents the character at the corresponding position in the two sequences.
[0047] Filling the matrix: Fill the values in the matrix with corresponding scores based on the similarity between the characters. These scores can reflect the match, mismatch, gap, etc. between two characters, thus forming a scoring matrix.
[0048] Calculating similarity: By analyzing the scoring matrix, the similarity between two sequences can be calculated. This usually involves weighted summing of the values in the matrix to come up with an overall similarity score. This method uses a recursive relation algorithm to determine the similarity value of each element based on the similarity of the sequences during the process of filling the matrix.
[0049] Based on the above technology, in one implementation, the recursive relationship algorithm is as follows:
[0050] Assume that the size of the two-dimensional matrix is (len(a)+1)*(len(b)+1), where len(a) and len(b) are two sequences to be compared; for each element (i, j) in the matrix, it represents the similarity between the first i elements of sequence a and the first j elements of sequence b;
[0051] If a[i] == b[j], then matrix[i+1][j+1] = matrix[i][j]+1; where a[i] == b[j] means that the first i elements of sequence a match the first j elements of sequence b, and matrix[i+1][j+1] = matrix[i][j]+1 means that the matrix similarity value is increased by 1;
[0052] If a[i]!=b[j], then matrix[i+1][j+1]=max(matrix[i+1][j],matrix[i]); where a[i]!=b[j] means that the first i elements of sequence a do not match the first j elements of sequence b; then matrix[i+1][j+1]=max(matrix[i+1][j],matrix[i]) means taking the larger of the values on the left and above of the two-dimensional matrix as the value of the current element;
[0053] When the matrix is filled, the similarity is obtained based on the lower right corner element of the matrix, which is expressed as matrix[len(a)][len(b)].
[0054] On the basis of the above technology, in a possible implementation, the unified processing includes removing special symbols, garbled information and unified conversion to upper and lower case. Specifically, text cleaning includes removing useless tags, special symbols, stop words, etc. These steps help reduce noise in the data and improve the quality of the data. Standardization involves character set conversion, word unification, etc. to ensure the consistency and comparability of the text. For example, removing special symbols and stop words can reduce redundant information in the data, while unified conversion to upper and lower case can make text processing more standardized, which is convenient for subsequent analysis and processing. Specifically, removing special symbols and garbled information is achieved through regular expressions or specific library functions in programming languages. These tools can identify and remove illegal characters or unrecognizable garbled characters in the text. Unified conversion to upper and lower case is usually achieved through string processing functions in programming languages, converting all characters in the text to upper or lower case uniformly to ensure that errors will not be caused by different upper and lower case when comparing or processing text. Unified processing improves the standardization and consistency of text data by removing special symbols, garbled information and unified conversion to upper and lower case, which provides convenience for subsequent data analysis and processing.
[0055] Based on the above technology, in a possible implementation manner, the basic information includes at least three types of information among song title, performer, composer information, lyrics information, word segmentation information and keyword information.
[0056] Based on the above technology, in a possible implementation manner, a method for performing unified processing on all acquired basic information includes: performing unified processing on all acquired basic information by using a regular expression method.
[0057] In summary, this technology sets the music information to be monitored to include at least one designated song title information and at least one performance information corresponding to the song title information, so that different performances, such as covers, pure music, etc., are all within the monitoring range, making the monitoring range wider and more comprehensive, avoiding the traditional direct reading of the song forwarding volume, the number of likes, and the number of collections, which leads to the inability to detect the popularity monitoring of other performances, and realizes the monitoring of other performances by coordinating the music similarity calculation method to determine whether it is related music information; at the same time, by using one or more song title information as the music information to be monitored, crawling and analyzing all relevant music information on the specified platform, multiple music information can be monitored simultaneously; thus, by realizing the acquisition of all relevant information of the same song, it is convenient to find covers, different interpretations or different performances, and all information can be matched. By using a two-dimensional matrix to compare the corresponding elements of the music element information, the music elements to be monitored can be quickly compared.
[0058] A second aspect of this embodiment provides a server for music monitoring and analysis, which is communicatively connected to user terminals respectively, and is used to execute the music monitoring and analysis method as described in any possible implementation manner of the first aspect.
[0059] The third aspect of this embodiment provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the music monitoring and analysis method described in any possible design of the first aspect is implemented. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can be executed by the processor. The steps of the above-mentioned method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera terminal device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.
[0060] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0061] In the embodiments provided in the present application, it should be understood that the disclosed terminal device and method can be implemented in other ways. For example, the terminal device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.
[0062] Specifically, the music monitoring and analysis method described in any possible implementation of the first aspect is applied to music heat monitoring. After obtaining the summarized music information, the heat label information of each summarized music information is obtained, and the heat label information includes one or more of the number of plays, the number of likes, the number of reposts, and the number of collections. For a specific example, the music information to be monitored is determined, and the music information includes at least one specified song title information and at least one performance method information corresponding to the song title information; crawler technology is used to search the music information to be monitored on the specified platform to obtain all the basic information of the music information to be monitored on the platform; all the basic information obtained is unified to obtain the processed information and store it in the database; according to the target information of the music information to be monitored, the basic information to be matched is extracted from the processed information; the target information of the music information to be monitored is calculated with the basic information to be matched to obtain the similarity value; the music information corresponding to the result that reaches the predetermined similarity threshold is taken out for aggregation, and the aggregated music information is output; after obtaining the aggregated music information, the heat label information of each summarized music information is obtained, and the heat label information includes one or more of the number of plays, the number of likes, the number of reposts, and the number of collections.
[0063] In a fourth aspect of the present embodiment, a computer program product comprising instructions is provided, and when the instructions are executed on a computer, the computer is caused to perform the music monitoring method as described in the first aspect or any possible implementation of the first aspect. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.
[0064] The embodiments described above are merely illustrative. If a unit described as a separate component is involved, it may or may not be physically separated; if a component shown as a unit is involved, it may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment. Ordinary technicians in this field can understand and implement it without creative work.
[0065] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. A music monitoring and analysis method, characterized in that: The following steps are involved: Determine the music information to be monitored, wherein the music information includes at least one designated song title information and at least one performance method information corresponding to the song title information; Using crawler technology to search for the music information to be monitored on a designated platform to obtain all basic information of the music information to be monitored on the platform; All basic information obtained is processed uniformly, and the processed information is obtained and stored in the database; Extracting basic information to be matched from the processed information according to target information of the music information to be monitored; Calculate the similarity between the target information of the music information to be monitored and the basic information to be matched to obtain a similarity value; The music information corresponding to the results reaching the predetermined similarity threshold is taken out and summarized, and the summarized music information is output.
2. The music monitoring and analysis method according to claim 1, characterized in that: Similarity calculation methods include: Initializing a preset two-dimensional matrix; the rows of the two-dimensional matrix represent the elements of the first sequence of music information to be monitored, and the columns represent the processed information elements of the second sequence; Fill the two-dimensional matrix with the elements of the first sequence of music information to be monitored and the processed information elements of the second sequence, and calculate the similarity between the two sequences; In the process of filling the matrix, a recursive relation algorithm is used to determine the similarity value of each element based on the similarity of the sequence.
3. The music monitoring and analysis method according to claim 2, characterized in that: The recursive relationship algorithm is as follows: Assume that the size of the two-dimensional matrix is (len(a)+1)*(len(b)+1), where len(a) and len(b) are two sequences to be compared; for each element (i, j) in the matrix, it represents the similarity between the first i elements of sequence a and the first j elements of sequence b; If a[i]==b[j], then matrix[i+1][j+1]=matrix[i][j]+1; where a[i]==b[j] means that the first i elements of sequence a match the first j elements of sequence b, and the matrix similarity value increases; If a[i]!=b[j], then matrix[i+1][j+1]=max(matrix[i+1][j],matrix[i]); where a[i]!=b[j] means that the first i elements of sequence a do not match the first j elements of sequence b, then the larger value of the left and the top of the two-dimensional matrix is taken as the value of the current element; When the matrix is filled, the similarity is obtained based on the lower right corner element of the matrix, that is, matrix[len(a)][len(b)].
4. The music monitoring and analysis method according to claim 1, characterized in that: The unified processing includes removing symbols, garbled information and converting to upper and lower case.
5. The music monitoring and analysis method according to claim 1, characterized in that: The basic information includes at least three types of information among song title, performer, composer information, lyrics information, word segmentation information and keyword information.
6. The music monitoring and analysis method according to claim 1, characterized in that: The method of uniformly processing all the basic information obtained includes: uniformly processing all the basic information obtained by using a regular expression method.
7. A server for music monitoring and analysis, characterized in that: The servers are respectively connected to the user terminals for communication, and the servers are used to execute the music monitoring and analysis method as described in any one of claims 1 to 6.
8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the music monitoring and analysis method according to any one of claims 1 to 6 is implemented.