KR20170091888A - Method and system for automatically tagging themes suited for songs - Google Patents
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Abstract
Description
The following description relates to a technique for tagging a theme suitable for music.
In addition to the development of various digital compression technologies, it is possible to digitize existing large capacity media and convert them into low capacity media, so that various types of media can be stored in a portable user terminal so that users can easily select desired media have.
In addition, digitized media through such digital compression technology enables media sharing among users on a network, thereby exploiting online media services explosively.
As a music, which occupies a large part of such a massive media, it is low in capacity and low in communication load compared to other types of media, so it is easy to support a real-time streaming service and satisfaction is high for both service providers and users.
Accordingly, a variety of online music services are currently available. In the conventional online music service, a user who is connected to the online is provided with a real-time music service by providing the entire file of the sound source content to the user terminal, or providing the sound source content through the streaming service. To provide a sound source.
Korean Patent Registration No. 10-0615522 (registered on Aug. 17, 2006) discloses an example of an online music service in which music content is categorized on the basis of content, and a technique of providing sound source content to users accessing through the network .
In order to efficiently recommend, search, and manage music, it is important to classify music content. Methods for classifying sound source contents into similar sound source contents are classified into genre, singer, and music. Examples of how to distinguish music by genre include Timbre, Rhythm, Pitch, and Mel-frequency cepstral coefficient (MFCC), which is widely used in speech recognition.
However, although the music genre is mostly given when the music is created, it is not possible to clearly define the music genre, and the system is ambiguous and inconsistent, making it difficult to classify the music by genre and serve the user.
In particular, in order to recommend music based on the current situation of the user rather than merely recommend music according to a specific classification, prior to determining the current situation of the user, Do. In a general conventional art, most people directly generate tags directly for respective sound source contents. However, because tags generated by a person are very subjective, different tags can be generated for different people even for the same sound source content, and it is costly for a person to generate a tag for a very large number of sound source contents And is inefficient.
As another prior art, the genre of meta information included in the sound source contents may be utilized as the advance information. However, as described above, genres provided as meta information are information given at the time of song creation, but the system is ambiguous and inconsistent. In addition, there is a problem that the genre provided as meta information is not clearly related to the user's situation.
On the other hand, there is a prior art that utilizes log information such as what kind of music has been heard by the users before. For example, Korean Patent No. 10-1170208 (music recommendation system and method) extracts structural features by analyzing music structurally, models the analysis results of music structure and characteristics based on user information, Discloses a technique for recommending music corresponding to a modeling result.
However, this prior art has not been able to generate and provide a tag as dictionary information for the sound source content itself, but it is a technique that empirically sets the relation between the situation and the sound source contents in advance and recommends the related sound source content for the same situation There is a problem.
A theme suitable for music is used together with sound source data and text information (for example, lyrics, meta information (singer, genre, title, album name, etc.) and other text And to provide a system and method for tagging the same.
The present invention provides a method and system capable of automatically learning not only sound source data but also a plurality of tags such as a theme, a genre, a mood, a theme, etc. of sound source contents by learning text information at a time.
A method for automatically tagging music performed by a computer-implemented music auto-tagging system, the method comprising: receiving sound source data and text information of the sound source content with respect to sound source content; Processing the sound source data and the text information in the form of learning data; Learning together the learning data preprocessed for the sound source data and the learning data preprocessed for the text information through a sound source-text integrated learning model; And providing at least one tag according to the learning result to the sound source content to perform music automatic tagging.
According to one aspect of the present invention, the pre-processing step may convert the sound source data into learning data expressed in time-frequency.
According to another aspect of the present invention, the preprocessing step converts the text information into learning data represented by a sequence of individual words.
According to another aspect of the present invention, the sound source-text integrated learning model is a combination of a sound source model and a text model, and the simultaneous learning step includes a step of pre-processing the sound source data using the sound source model among the sound source- Generating a first real number vector corresponding to the learned learning data; Generating a second real number vector corresponding to the learning data preprocessed for the text information using the text model among the sound source-text integrated learning models; And calculating a tag-specific score according to a third real number vector to which the first real number vector and the second real number vector are connected with respect to the entire tag set through the sound-text integrated learning model have.
According to another aspect of the present invention, the step of performing automatic music tagging includes determining the at least one tag in the entire tag set using the score of each tag output through the sound source-text integrated learning model, And the like.
According to another aspect of the present invention, the step of generating a real vector corresponding to the training data preprocessed on the sound source data may include sampling a plurality of frames from the training data preprocessed on the sound source data, As a source of the sound source model, a real vector for the sound source data.
According to another aspect of the present invention, the sound source model may have the same number of channels as the number of the sampled frames.
According to another aspect, the step of generating a real vector corresponding to the preprocessed training data for the text information may include applying a plurality of real vector vectors to each of the plurality of individual text information of different types, And the like.
According to another aspect of the present invention, in the sound source-text integrated learning model, the source-text integrated learning model using an error based on a difference between a tag vector value obtained by digitizing tags of the entire tag set and a vector based on the calculated score of each tag, And a learning process.
According to another aspect of the present invention, an error change value calculated by partially differentiating the error in the sound source-text integrated learning model is transmitted to the individual model of the sound source model and the text model through backpropagation or BackPropagation Through Time (BPTT) And the like.
A computer-implemented music auto-tagging system, comprising: an input control unit for receiving sound source data and text information of sound source content with respect to sound source content; A preprocessor for processing the sound source data and the text information in the form of learning data; A learning unit for learning together learning data preprocessed on the sound source data and learning data preprocessed for the text information through a sound source-text integrated learning model; And a tagging unit for providing at least one tag according to the learning result to the sound source content to perform automatic tagging of music.
According to the embodiment of the present invention, it is possible to automatically tag a theme suitable for music by using the sound source data of the sound source content together with the text information, thereby reducing the cost incurred in tagging the sound source content.
According to the embodiment of the present invention, the sound source data and the text information of the sound source content can be automatically learned and tagged without separating the process of extracting a separate factor from the sound source through the minimum preprocessing.
According to the embodiment of the present invention, a plurality of tags such as a theme, a genre, a mood, and a theme suitable for sound source content can be given by learning text information including lyrics or meta information at the same time in addition to sound source data, Various tagging is possible.
1 is a block diagram for explaining an example of the internal configuration of a computer system according to an embodiment of the present invention.
2 is a diagram illustrating an example of components that a processor of a computer system according to an embodiment of the present invention may include.
3 is a flowchart illustrating an example of a method of automatically tagging music that can be performed by a computer system according to an embodiment of the present invention.
4 is a diagram illustrating an example of an automatic music tagging learning model structure according to an embodiment of the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The present invention relates to methods and systems for automatic tagging of music, and more particularly to a method and system for automatically tagging music that can be automatically and appropriately applied to music by using sound source data and text information of music source contents together. Technology.
Embodiments, including those specifically disclosed herein, achieve automatic learning and tagging for source content and thereby achieve significant advantages in terms of cost savings, efficiency and accuracy.
The tags generated for the sound source content can be utilized for recommendation, search, classification, and management of the sound source content. For example, in order to recommend sound source contents based on a user's situation, it is necessary to provide prior information on how each music fits in a certain situation, and a tag suitable for music can be given as the dictionary information.
1 is a block diagram for explaining an example of the internal configuration of a computer system according to an embodiment of the present invention. For example, a music auto-tagging system according to embodiments of the present invention may be implemented through the
The
The
The persistent storage device 130 may be a component such as a memory or other persistent storage device as used by the
The input /
The
Also, in other embodiments, the
FIG. 2 is a diagram illustrating an example of a component that a processor of a computer system according to an exemplary embodiment of the present invention may include; FIG. 3 is a block diagram of a music automatic Fig. 3 is a flowchart showing an example of a tagging method. Fig.
2, the
The music auto-tagging method may not occur in the order shown, and some of the steps may be omitted or an additional process may be further included.
In step S310, the
At this time, the sound source
In step S320, the sound source
In step S330, the sound
In step S340, the
In step S350, the
In this embodiment, a multimodal technique for simultaneously learning text data together with sound source data for music tagging is provided. In particular, sound source data and text information are input without a separate factor extraction process for sound source data Provides an E2E (end to end) technology that can automatically assign multiple tags. As described above, according to the embodiments of the present invention, text information such as lyrics or meta information is simultaneously used for tagging together with sound source data, thereby simultaneously assigning a tag considering the content of the lyrics, meta information, It becomes possible. For example, if a song is slow, but the lyrics contain bright content, or if the song contains a fast beat, but the lyrics contain depressed content, a more appropriate tag may be assigned to the song, Accuracy and reliability can be improved.
4 is a diagram illustrating an example of an automatic music tagging learning model structure according to an embodiment of the present invention. The automatic music tagging learning model structure according to the embodiment of FIG. 4 can be utilized by combining a sound source model and a text model as one embodiment of the structure of the sound source-text integration model described above. At this time, the automatic music tagging learning model structure includes a sound source data
Steps 1 to 3 below may be an example of a process of generating a real vector corresponding to sound source data in the sound source data
In step 1, the sound source data (for example, an mp3 file) included in the sound source content can be converted into data of a time-frequency-size type such as a mel-spectrogram or MFCC through preprocessing. For example, the sound
A plurality of frequency frames for one or more short time intervals (1 second to 10 seconds) may be sampled from the sound source data converted in step 2 and used as input data for the learning model of the sound source data. For example, the
In step 3, a plurality of convolution and pooling layers that the sound source data
There is a fully-connected layer on the plurality of convolution and pulling layers, and each layer function includes a sigmoid function, a hyperbolic tangent (tanh) function, a ReLU (Rectified Linear Unit) function Various functions can be used. As a result, one multi-dimensional real vector can be generated for the sound source data. For example, given the first source data
Steps 4 to 6 below may be an example of a process of generating a real vector corresponding to text information in the text information
In step 4, the lyric and meta information (genre, title, singer, album name, and query information) can be represented as a sequence of individual words through morphological analysis preprocessing. For example, the
In step 5, the individual word sequences (or individual word sequences) may be expressed as a single multidimensional real number vector using a Recurrent Neural Network (RNN) or a model capable of learning sequence information such as CNN described above. If a plurality of individual sequences are respectively input to a plurality of models, a plurality of multi-dimensional real vectors may be generated. Assuming that an individual model is used for each text information, the first data of the given m-th text information
In step 6, the real vectors calculated in the output layer of the process 3 and the process 5 can be connected as a vector in the sound-text integration
In Equation (1), u is a conjugated vector, and F h (k) can denote the k th hidden layer of the integrated model.
In the case of the theme output layer, it may be composed of the same number of nodes as the size of the theme word set, and the tags assigned to the training data may be represented by a vector having the same size as the number of nodes in the output layer. For example, if the entire set of theme tags is Y = {sadness, pleasure, break}, if the tag of the first data is 'joyful', y = {0, 1, 0}. In this case, when there are a plurality of tags such as 'enjoyment' and 'rest', the value of the elements may be divided by the number of tags such that the sum of the elements of the vector is 1, such as y = {0, 0.5, 0.5} have.
In addition, the specific output layer may be configured such that the output layer composed of two nodes of 0 and 1 has output layers as many as the number of tag words.
When the output layer is defined such that the tags assigned to the training data are represented by a vector having the same size as the number of nodes of the output layer, the score to be given to each tag can be expressed as a probability as shown in Equation 2 below.
In Equation (2), f may denote the entire model,
When the output layer is defined such that the tags assigned to the training data in step 7 are represented by a vector of the same size as the number of nodes in the output layer, the error is the difference between the actual tag vector values and the values calculated for each tag in equation Can be defined as Equation (3) below.
At this time, various functions such as a mean square error function and a cross-entropy function can be used for the function used as the difference.
In step 8, learning can use a backpropagation technique that adjusts the weight of the model based on a gradient descent so that the error is minimized. The error change value for the individual weight change is given by Equation 4 below As can be defined by partially differentiating the error into a weight vector.
The error change value follows the general back propagation from the output layer of the unified model to the layer where the vector of the sound source data and the text information are joined, and the vector corresponding to the sound source data portion and the vector corresponding to the text information portion in the joint layer, And can be propagated backward.
Equation (5) is an example of an error function using a mean square error function
In the case of using backpropagation or recurrent neural network used in each model from the hidden layer below the output layer of the sound source model and the text model, learning using an algorithm such as BPTT (BackPropagation Through Time) Can proceed.
In this way, sound source, lyric, and meta information can be integrated into the integrated model to be used for theme tagging. Errors that occur during learning can be propagated back to shared form in the integrated model and propagated to each model in the joint layer .
As described above, according to the embodiments of the present invention, it is possible to automatically tag the theme suitable for music by using the sound source data of the sound source content together with the text information, thereby reducing the cost incurred in tagging the sound source content. In addition, the sound source data and text information of the sound source contents can be automatically learned and tagged without separating the process of extracting a separate factor from the sound source through the minimum preprocessing. In addition to the sound source data, text including lyrics or meta information By learning information at a time, multiple tags such as theme, genre, mood, and theme suitable for sound source contents can be assigned, and various tagging related to music can be performed.
The apparatus described above may be implemented as a hardware component, a software component, or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented as a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit, a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.
The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.
The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.
While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.
Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.
Claims (14)
Receiving sound source data and text information of the sound source content with respect to the sound source content;
Processing the sound source data and the text information in the form of learning data;
Learning together the learning data preprocessed for the sound source data and the learning data preprocessed for the text information through a sound source-text integrated learning model; And
Performing at least one tag according to the learning result on the sound source content to perform automatic music tagging
Wherein the automatic music tagging method comprises:
Wherein the processing comprises:
And converting the sound source data into learning data expressed in time-frequency.
Wherein the processing comprises:
And converting the text information into learning data represented by a sequence of individual words.
The sound source-text integrated learning model is a combination of a sound source model and a text model,
Wherein the learning comprises:
Generating a first real-valued vector corresponding to the preprocessed training data for the sound source data using the sound source model among the sound source-text integrated learning models;
Generating a second real number vector corresponding to the learning data preprocessed for the text information using the text model among the sound source-text integrated learning models; And
A step of calculating a score for each tag according to a third real number vector to which the first real number vector and the second real number vector are connected with respect to the entire tag set through the sound-text integrated learning model
Wherein the automatic music tagging method comprises:
The step of performing the automatic music tagging includes:
Wherein the at least one tag is determined in the entire tag set by using the tag-specific score output through the sound source-text integrated learning model, and is assigned to the sound source content.
Wherein the step of generating a real vector corresponding to the preprocessed training data for the sound source data comprises:
Wherein a plurality of frames are sampled from the training data preprocessed for the sound source data and a real vector for the sound source data is generated using each of the plurality of sampled frames as an input of the sound source model, Way.
Wherein the sound source model has a number of channels equal to the number of the sampled frames.
Wherein generating the real vector corresponding to the preprocessed training data for the text information comprises:
Wherein a plurality of real vectors are generated by applying an individual text model to each of a plurality of pieces of individual text information of different kinds.
Text integrated learning model using an error based on a difference between a tag vector value obtained by digitizing the tags of the entire tag set and a vector based on the calculated score of each tag in the sound source-text integrated learning model, A method for automatically tagging music.
The error change value calculated by partially differentiating the error in the sound source-text integrated learning model is propagated to the individual sound model models and the individual models of the text model through backpropagation or backpropagation through time (BPTT) A method for automatic tagging of music.
An input control unit for receiving sound source data and text information of the sound source content with respect to the sound source content;
A preprocessor for processing the sound source data and the text information in the form of learning data;
A learning unit for learning together learning data preprocessed on the sound source data and learning data preprocessed for the text information through a sound source-text integrated learning model; And
And a tagging unit for assigning at least one tag according to the learning result to the sound source content and performing automatic music tagging,
Wherein the music auto-tagging system comprises:
The sound source-text integrated learning model is an integrated sound source model and a text model,
Wherein,
Generating a first real number vector corresponding to the preprocessed learning data for the sound source data using the sound source model among the sound source and text integrated learning models and generating a first real number vector corresponding to the preprocessed training data for the sound source data using the text model A second real number vector corresponding to the preprocessed learning data is generated, and the score for each tag according to the third real number vector to which the first real number vector and the second real number vector are connected is set as the sound- Learning model of the music.
The tagging unit,
Wherein the at least one tag is determined in the entire tag set using the tag-specific score output through the sound-text integrated learning model, and is assigned to the sound source content.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20190114221A (en) * | 2018-03-29 | 2019-10-10 | 삼성전자주식회사 | Equipment diagnosis system and method based on deep learning |
| CN111026908A (en) * | 2019-12-10 | 2020-04-17 | 腾讯科技（深圳）有限公司 | Song label determination method and device, computer equipment and storage medium |
| KR20220033304A (en) * | 2020-09-09 | 2022-03-16 | 주식회사 엘지유플러스 | Method and apparatus for recommending hehavior of user |
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2016
- 2016-02-02 KR KR1020160012717A patent/KR101801250B1/en active IP Right Grant
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20190114221A (en) * | 2018-03-29 | 2019-10-10 | 삼성전자주식회사 | Equipment diagnosis system and method based on deep learning |
| CN111026908A (en) * | 2019-12-10 | 2020-04-17 | 腾讯科技（深圳）有限公司 | Song label determination method and device, computer equipment and storage medium |
| CN111026908B (en) * | 2019-12-10 | 2023-09-08 | 腾讯科技（深圳）有限公司 | Song label determining method, device, computer equipment and storage medium |
| KR20220033304A (en) * | 2020-09-09 | 2022-03-16 | 주식회사 엘지유플러스 | Method and apparatus for recommending hehavior of user |
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