KR102272189B1 - Method for generating sound by using artificial intelligence - Google Patents

Abstract

According to an embodiment of the present disclosure, a computer program stored in a computer-readable storage medium is disclosed. When the computer program is executed in one or more processors to perform the following operations for generating sound using artificial intelligence, the operations include: acquiring time series data from a measurement device for acquiring sensor data; generating a training data set based on the acquired time series data; inputting the training data set to a sound generation model to train the sound generation model; and inputting the time series data obtained from the measurement device into the learned sound generation model to generate a sound.

Description

How to generate sound using artificial intelligence {METHOD FOR GENERATING SOUND BY USING ARTIFICIAL INTELLIGENCE}

The present disclosure relates to a method of generating a sound using artificial intelligence, and more particularly, to a method of generating a sound of an instrument using artificial intelligence.

Musical instruments can be played through professional performances, or through ordinary people playing as a hobby. Therefore, the number of ordinary people playing musical instruments is increasing.

As the number of ordinary people who play musical instruments increases, demand for electronic musical instruments (eg, digital pianos) that output the sound of expensive classical musical instruments (eg, grand pianos) is increasing.

Research on statistical modeling and artificial intelligence is being actively conducted to output the sound of expensive classical instruments, and as a result, statistical modeling and artificial intelligence are used to output the sound of expensive classical instruments through electronic instruments. Demand is rising.

Korean Laid-Open Patent (Registration No.: KR10-2013-0067856) discloses an apparatus and method for playing a virtual instrument based on finger motion.

The present disclosure has been devised in response to the above-described background technology, and is for generating sound using artificial intelligence.

According to an embodiment of the present disclosure for realizing the above-described problems, a computer program stored in a computer-readable storage medium is disclosed. When the computer program is executed in one or more processors, the following operations for generating sound using artificial intelligence are performed, wherein the operations include: acquiring time series data from a measurement device for acquiring sensor data; generating a training data set based on the acquired time series data; inputting the training data set to the sound generation model to train the sound generation model; and inputting the time series data obtained from the measurement device into the learned sound generation model to generate a sound.

In an alternative embodiment, the measuring device may include at least one of a first measuring device installed in an electronic musical instrument to acquire sensor data, or a second measuring device installed in a classical musical instrument to acquire sensor data.

In an alternative embodiment, the measuring device is a first measuring device installed in the electronic keyboard musical instrument to obtain data on the movement of the keyboard or a second measuring device installed in the classical keyboard musical instrument to acquire the data on the movement of the keyboard and sound data It may include at least one of the devices.

In an alternative embodiment, the first measuring device may include at least one of a laser sensor, a magnetic sensor, an optical sensor, or a pressure sensor for acquiring data on movement of a keyboard included in the electronic keyboard musical instrument.

In an alternative embodiment, the second measuring device is at least one of a laser sensor for acquiring data on the keyboard movement of the classical keyboard instrument or a sound sensor for acquiring a sound of the classical musical instrument corresponding to the keyboard movement of the classical keyboard instrument may include.

In an alternative embodiment, the training data set may include a training data set in which data acquired from the contact model is used as training input data, and time series data acquired from the second measurement device are labeled.

In an alternative embodiment, the contact model is a model for predicting the contact speed of the keys, and may include a model for predicting the contact speed of the keys by inputting distance data between the laser sensor and the keys obtained through the laser sensor. .

In an alternative embodiment, the learned sound generation model may include a model trained by a machine learning method to generate a classical musical instrument sound corresponding to data input to the electronic musical instrument.

In an alternative embodiment, the classical musical instrument sound corresponding to data input to the electronic musical instrument is one of a classical musical instrument sound corresponding discretely to data input to the electronic musical instrument or a classical musical instrument sound continuously corresponding to data input to the electronic musical instrument. It may include at least one.

In an alternative embodiment, the operation of learning the sound generation model by inputting the training data set to the sound generation model may include: using a laser sensor included in the second measurement device to perform a keyboard included in the classical keyboard musical instrument and the second measurement device. acquiring distance data between the included laser sensors; predicting a contact speed of a key included in a keyboard of a classical musical instrument by inputting distance data into a contact model; and a learning data set using the predicted key contact speed as the learning input data, and the sound data of a classical instrument as a label obtained through a sound sensor included in the second measuring device, by inputting the learning data set into the sound generation model to learn. may include

In an alternative embodiment, the operation of generating sound by inputting time series data obtained from the measurement device into the learned sound generation model may include inputting the contact speed of the keys obtained from the first measurement device into the learned sound generation model. ; and generating a sound of a classical musical instrument corresponding to a touch speed of a keyboard by using the learned sound generation model.

According to another embodiment of the present disclosure, a method for generating a sound using artificial intelligence is disclosed. The method includes: acquiring time series data from a measuring device for acquiring sensor data; generating a training data set based on the acquired time series data; training the sound generation model by inputting the training data set to the sound generation model; and inputting the time series data obtained from the measurement device into the learned sound generation model to generate sound.

A computing device is disclosed according to another embodiment of the present disclosure. The computing device may include one or more processors; and a memory storing instructions executable by the processor; Including, wherein the processor obtains time series data from a measurement device for obtaining sensor data, generates a training data set based on the obtained time series data, and inputs the training data set to a sound generation model to create a sound generation model , and inputting the time series data obtained from the measurement device into the learned sound generation model to generate sound.

The present disclosure may provide a method of generating a sound using artificial intelligence.

1 is a block diagram of a computing device for generating sound using artificial intelligence according to an embodiment of the present disclosure.
2 is an exemplary diagram illustrating a neural network included in a sound generation model according to an embodiment of the present disclosure.
3 is an exemplary diagram for describing a contact model according to an embodiment of the present disclosure.
4 is an exemplary diagram for explaining a sound generation model according to an embodiment of the present disclosure.
5 is an exemplary diagram for explaining a process of generating a sound using a learned sound generation model according to an embodiment of the present disclosure.
6 is a block diagram illustrating a module for generating sound using artificial intelligence.
7 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is apparent that these embodiments may be practiced without these specific descriptions.

The terms “component,” “module,” “system,” and the like, as used herein, refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or execution of software. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored therein. Components may communicate via a network such as the Internet with another system, for example via a signal having one or more data packets (eg, data and/or signals from one component interacting with another component in a local system, distributed system, etc.) may communicate via local and/or remote processes depending on the data being transmitted).

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean one of the natural implicit substitutions. That is, X employs A; X employs B; or when X employs both A and B, "X employs A or B" may apply to either of these cases. It should also be understood that the term “and/or” as used herein refers to and includes all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the feature and/or element in question is present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements and/or groups thereof. Also, unless otherwise specified or unless the context is clear as to designating a singular form, the singular in the specification and claims should generally be construed to mean "one or more."

In addition, the term “at least one of A or B” should be interpreted as meaning “includes only A”, “includes only B”, and “in the case of a combination of A and B”.

Those skilled in the art will further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or combinations of both. It should be recognized that they can be implemented with To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Descriptions of the presented embodiments are provided to enable those skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art of the present disclosure. The generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present invention is not limited to the embodiments presented herein. This invention is to be construed in its widest scope consistent with the principles and novel features presented herein.

1 is a block diagram of a computing device for generating sound using artificial intelligence according to an embodiment of the present disclosure.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In an embodiment of the present disclosure, the computing device 100 may include other components for performing the computing environment of the computing device 100 , and only some of the disclosed components may configure the computing device 100 .

The computing device 100 may include a processor 110 , a memory 130 , and a network unit 150 .

The processor 110 may include one or more cores, and a central processing unit (CPU) of a computing device, a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU). unit) and the like, and may include a processor for data analysis and deep learning. The processor 110 may read a computer program stored in the memory 130 and perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning the neural network. The processor 110 for learning of the neural network, such as processing input data for learning in deep learning (DL), extracting features from the input data, calculating an error, updating the weight of the neural network using backpropagation calculations can be performed. At least one of a CPU, a GPGPU, and a TPU of the processor 110 may process learning of a network function. For example, the CPU and the GPGPU can process learning of a network function and data classification using the network function together. In addition, in an embodiment of the present disclosure, learning of a network function and data classification using the network function may be processed by using the processors of a plurality of computing devices together. In addition, the computer program executed in the computing device according to an embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

According to an embodiment of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 .

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, a hard disk type, a multimedia card micro type, and a card type memory (eg, SD or XD memory, etc.), Random Access Memory (RAM), Static Random Access Memory (SRAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Programmable Read (PROM) -Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The server may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The description of the above-described memory is only an example, and the present disclosure is not limited thereto.

The network unit 150 according to an embodiment of the present disclosure includes a Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), VDSL ( A variety of wired communication systems such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN) can be used.

In addition, the network unit 150 presented herein is CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA ( A variety of wireless communication systems may be used, such as Single Carrier-FDMA) and other systems.

In the present disclosure, the network unit 150 may be configured regardless of its communication mode, such as wired and wireless, and may be configured with various communication networks such as a personal area network (PAN) and a wide area network (WAN). can In addition, the network may be a well-known World Wide Web (WWW), and may use a wireless transmission technology used for short-range communication, such as infrared (IrDA) or Bluetooth (Bluetooth).

The techniques described herein may be used in the networks mentioned above, as well as in other networks.

According to an embodiment of the present disclosure, the processor 110 may generate a sound using artificial intelligence. The processor 110 may store data for generating a sound in the memory 130 . The processor 110 may transmit/receive data for generating a sound through the network unit 150 . The processor 110 may generate a learning data set used for artificial intelligence learning in order to generate a sound. The processor 110 may also store the training data set in the memory 130 , and transmit/receive the training data set through the network unit 150 . The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may acquire time series data from a measurement device for acquiring sensor data. The measuring device may include a device for quantifying a quantity with a certain standard. The above-described measuring apparatus is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the measuring device may include at least one of a first measuring device installed in an electronic musical instrument to acquire sensor data, or a second measuring device installed in a classical musical instrument to acquire sensor data. Electronic musical instruments may include musical instruments that generate sound through electrical circuits. Classical musical instruments may include musical instruments that generate sound without the use of electrical circuits. Classical instruments may include string instruments that produce sound through strings, wind instruments that produce sound by blowing with the mouth, and/or percussion instruments that produce sound when struck. The time series data may include a sequence of data arranged at recognized time intervals. Time series data may also include data obtained from sensors. The above-described measuring apparatus is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the measuring device is installed in a first measuring device installed in an electronic keyboard musical instrument to acquire data on the movement of the keyboard or in a classical keyboard musical instrument to acquire data and sound data on the movement of the keyboard. and at least one of the second measuring devices. The electronic keyboard musical instrument may include an instrument that generates a sound produced by striking a keyboard using an electric circuit. A classical keyboard instrument may include an instrument in which a sound is produced by striking a key. Classical keyboard instruments may include pianos that produce sounds by striking strings, harpsichords that produce sounds by striking strings, pipe organs that produce sounds by blowing air into tubes, and the like. The above-described measuring apparatus is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the first measuring device may include a measuring device installed in an electronic musical instrument. The first measuring device may include at least one of a laser sensor, a magnetic sensor, an optical sensor, and a pressure sensor for acquiring data on movement of a keyboard included in the electronic keyboard musical instrument. The electronic keyboard musical instrument may include an instrument that generates a sound produced by striking a keyboard using an electric circuit. The processor 110 may acquire data on the movement of the keyboard by using the first measurement device. The processor 110 uses the first measuring device to determine the touch speed of the keyboard (the contact speed may include the speed at which the keyboard approaches or moves away from a sensor attached to the keyboard), the distance the key is from the sensor, and the key The direction of movement can be obtained. The processor 110 may obtain a distance between the sensor and the keyboard using, for example, a laser sensor. The processor 110 may obtain, for example, a contact speed of a key, a distance from the sensor, and/or a direction in which the key moves by using at least one of a magnetic sensor, an optical sensor, and a pressure sensor. Accordingly, the processor 110 may acquire data on the movement of the keyboard included in the electronic keyboard musical instrument by using the first measuring device. Furthermore, the processor 110 may acquire data on the movement of a string included in an electronic musical instrument (eg, an electronic violin, an electronic cello) by using the first measuring device. The above-described first measuring device is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the second measuring device may include a measuring device installed in a classical musical instrument. It may include at least one of a laser sensor for acquiring data on the keyboard movement of the classical keyboard musical instrument and a sound sensor for acquiring a sound of a classical musical instrument corresponding to the keyboard movement of the classical keyboard musical instrument. A classical keyboard instrument may include an instrument in which a sound is produced by striking a key. The processor 110 may acquire data on the movement of the keys of the classical keyboard musical instrument by using the laser sensor. The processor 110 may obtain a distance between the sensor and a key included in the classical keyboard musical instrument by using the laser sensor. The processor 110 may acquire the sound of the classical musical instrument corresponding to the keyboard movement of the classical keyboard instrument by using the sound sensor. The processor 110 may acquire sound data generated according to the movement of the vibrating string based on the movement of the keyboard of the classical keyboard musical instrument by using the sound sensor. Accordingly, the processor 110 may acquire the sound of the classical instrument corresponding to the movement of the keyboard of the classical keyboard instrument. The above-described second measuring device is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may generate a training data set based on time series data obtained from the measurement device.

According to an embodiment of the present disclosure, the training data set may include a set of training data used for training a machine learning model. The training data set may include a training data set using data acquired from the contact model as training input data and time-series data acquired from the second measurement device as a label. The contact model may include a machine learning model that receives distance data acquired through a laser sensor and outputs a predicted key contact speed. For example, the contact model may include a machine learning model trained in a supervised learning manner. The contact model may include a model trained using a training data set using distance data acquired through a laser sensor as training input data of the contact model and contact speed as a label (correct answer data) of the contact model. The contact speed may include the speed at which the keyboard approaches or moves away from a sensor attached to the keyboard. According to an embodiment of the present disclosure, the contact model is a model for predicting the contact speed of the keyboard, and includes a model for predicting the contact speed of the keyboard by inputting distance data between the laser sensor and the keyboard obtained through the laser sensor. can do. The processor 110 sets the learning input data of the sound generation model as the predicted contact speed, which is the output of the contact model, and labels the sound of the classical keyboard instrument, so that the sound of the classical keyboard instrument corresponding to the contact speed of the electronic keyboard instrument is obtained. can be printed out. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the learned sound generation model may include a model learned by a machine learning method to generate a classical musical instrument sound corresponding to data input to an electronic musical instrument. Machine learning is a branch of artificial intelligence that can include algorithms that allow computers to learn. The processor 110 may use Wavenet, seq to seq, and/or GANSynth to train the sound generation model. The above-described sound generation model is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, a classical musical instrument sound corresponding to data input to the electronic musical instrument is a classical musical instrument sound discretely corresponding to data input to the electronic musical instrument or a classical musical instrument sound continuously corresponding to data input to the electronic musical instrument. It may include at least one of musical instrument sounds. The processor 110 may generate a classical musical instrument sound discretely corresponding to data input to the electronic musical instrument by using the sound generation model. Data input to the electronic musical instrument may include, for example, data on a movement (eg, contact speed) of a keyboard included in the electronic keyboard musical instrument. The processor 110 sets the sound of a classical musical instrument to 1 when the touch speed of the keys included in the electronic keyboard musical instrument is 1-5, sets the sound of a classical instrument to 2 when the touch speed of the keyboard is 6-10, and sets the touch speed of the keyboard. In the case of 11-15, you can create a classical instrument sound with 3. The processor 110 may also generate a classical musical instrument sound continuously corresponding to data input to the electronic musical instrument. Data input to the electronic musical instrument may include, for example, data on a movement (eg, contact speed) of a keyboard included in the electronic keyboard musical instrument. The processor 110 may generate a classical musical instrument sound continuously corresponding to a contact speed of a keyboard included in the electronic keyboard musical instrument. Accordingly, the processor 110 may generate a classical musical instrument sound by inputting the performance of the electronic musical instrument to the sound generation model. That is, when a classical musical instrument sound continuously corresponding to data input to the electronic musical instrument is generated, the same sound as the classical musical instrument sound may be output through the electronic musical instrument. Through this, consumers can obtain the same effect as playing a classical instrument using an electronic musical instrument that is relatively inexpensive compared to a classical musical instrument. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may learn the sound generation model by inputting the training data set to the sound generation model. The processor 110 may acquire distance data between a key included in the classical keyboard musical instrument and the laser sensor included in the second measuring device by using the laser sensor included in the second measuring device. Through this, the processor 110 may acquire data on the movement of the keys included in the classical keyboard musical instrument. The processor 110 may input the distance data into the contact model to predict the contact speed of a key included in a keyboard of a classical musical instrument. The contact model may include a machine learning model that receives distance data acquired through a laser sensor and outputs a predicted key contact speed. For example, the contact model may include a machine learning model trained in a supervised learning manner. The contact model may include a model trained using a training data set using distance data acquired through a laser sensor as training input data and a contact speed as a label. The processor 110 uses the predicted key contact speed as learning input data, and inputs a training data set using sound data of a classical musical instrument obtained through a sound sensor included in the second measurement device as a label to the sound generation model. can be learned by doing. When the sound output by the sound generation model has a large error with the label sound data (correct answer data), the processor 110 may perform learning to reduce the error by adjusting the weight. Through this, the processor 110 may generate a classical musical instrument sound corresponding to the movement of a keyboard included in the electronic keyboard musical instrument by using the sound generation model. In addition, the processor 110 may generate a classical musical instrument sound continuously corresponding to data input to the electronic musical instrument by using a sound generation model, which is a machine learning model. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may generate sound by inputting time series data obtained from the measurement device into the learned sound generation model. The processor 110 may input the key contact speed obtained from the first measurement device into the learned sound generation model. That is, the processor 110 may input the contact speed of the keyboard included in the electronic keyboard musical instrument to the learned sound generation model. The processor 110 may generate a sound of a classical musical instrument corresponding to a touch speed of a keyboard by using the learned sound generation model. The processor 110 may generate a sound of a classical keyboard musical instrument corresponding to a contact speed of a keyboard included in the electronic keyboard musical instrument by using the learned sound generation model. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to the present disclosure, when a sound is generated using a sound generation model, a classical musical instrument sound may be generated using an electronic musical instrument. In addition, the sound generation model may generate the sound of a classical keyboard instrument continuously corresponding to the movement of a keyboard included in the electronic keyboard musical instrument by using a model learned using a machine learning method. In this way, the sound of a classical keyboard instrument that is more sophisticated and realistic can be produced through the electronic keyboard instrument.

2 is an exemplary diagram illustrating a neural network included in a sound generation model according to an embodiment of the present disclosure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. A neural network may be composed of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is configured by including at least one or more nodes. Nodes (or neurons) constituting the neural networks may be interconnected by one or more links.

In the neural network, one or more nodes connected through a link may relatively form a relationship between an input node and an output node. The concepts of an input node and an output node are relative, and any node in an output node relationship with respect to one node may be in an input node relationship in a relationship with another node, and vice versa. As described above, an input node-to-output node relationship may be created around a link. One or more output nodes may be connected to one input node through a link, and vice versa.

In the relationship between the input node and the output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be changed by the user or algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node sets values input to input nodes connected to the output node and links corresponding to the respective input nodes. An output node value may be determined based on the weight.

As described above, in a neural network, one or more nodes are interconnected through one or more links to form an input node and an output node relationship within the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the correlation between the nodes and the links, and the value of a weight assigned to each of the links. For example, when the same number of nodes and links exist and there are two neural networks having different weight values between the links, the two neural networks may be recognized as different from each other.

A neural network may include one or more nodes. Some of the nodes constituting the neural network may configure one layer based on distances from the initial input node. For example, a set of nodes having a distance n from the initial input node may constitute n layers. The distance from the initial input node may be defined by the minimum number of links that must be traversed to reach the corresponding node from the initial input node. However, the definition of such a layer is arbitrary for description, and the order of the layer in the neural network may be defined in a different way from the above. For example, a layer of nodes may be defined by a distance from the final output node.

The initial input node may refer to one or more nodes to which data is directly input without going through a link in a relationship with other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network, it may mean nodes that other input nodes connected by a link do not have. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the neural network. In addition, the hidden node may mean nodes constituting the neural network other than the first input node and the last output node. The neural network according to an embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as progresses from the input layer to the hidden layer. can In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be less than the number of nodes in the output layer, and the number of nodes decreases as the number of nodes progresses from the input layer to the hidden layer. have. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as the input layer progresses to the hidden layer. can The neural network according to another embodiment of the present disclosure may be a neural network in a combined form of the aforementioned neural networks.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can be used to identify the latent structures of data. In other words, it can identify the potential structure of photos, texts, videos, voices, and music (for example, what objects are in the photos, what the text and emotions are, what the texts and emotions are, etc.) . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), and restricted Boltzmann machines (RBMs). boltzmann machine), a deep belief network (DBN), a Q network, a U network, a Siamese network, and the like. The description of the deep neural network described above is only an example, and the present disclosure is not limited thereto.

In an embodiment of the present disclosure, the network function may include an auto-encoder. The auto-encoder may be a kind of artificial neural network for outputting output data similar to input data. The auto encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between the input/output layers. The number of nodes in each layer may be reduced from the number of nodes of the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically with reduction from the bottleneck layer to the output layer (symmetrically with the input layer). In this case, although the dimensionality reduction layer and the dimension reconstruction layer are illustrated as being symmetrical in the example of FIG. 2 , the present disclosure is not limited thereto, and the nodes of the dimension reduction layer and the dimension reconstruction layer may or may not be symmetrical. The auto-encoder can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to the number of sensors remaining after pre-processing of input data. In the auto-encoder structure, the number of nodes of the hidden layer included in the encoder may have a structure that decreases as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes between the encoder and the decoder) is too small, a sufficient amount of information may not be conveyed, so a certain number or more (e.g., more than half of the input layer, etc.) ) may be maintained.

The neural network may be trained by at least one of supervised learning, unsupervised learning, and semi-supervised learning. The training of the neural network is to minimize the error in the output. In the training of a neural network, iteratively inputs the training data to the neural network, calculates the output of the neural network and the target error for the training data, and calculates the error of the neural network from the output layer of the neural network to the input layer in the direction to reduce the error. It is a process of updating the weight of each node in the neural network by backpropagation in the direction. In the case of teacher learning, learning data in which the correct answer is labeled in each learning data is used (ie, labeled learning data), and in the case of comparative learning, the correct answer may not be labeled in each learning data. That is, for example, the learning data in the case of teacher learning regarding data classification may be data in which categories are labeled in each of the learning data. Labeled training data is input to the neural network, and an error can be calculated by comparing the output (category) of the neural network with the label of the training data. As another example, in the case of comparison learning about data classification, an error may be calculated by comparing the input training data with the neural network output. The calculated error is back propagated in the reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back propagation. A change amount of the connection weight of each node to be updated may be determined according to a learning rate. The computation of the neural network on the input data and the backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of repetitions of the learning cycle of the neural network. For example, in the early stage of training of a neural network, a high learning rate can be used to enable the neural network to quickly acquire a certain level of performance, thereby increasing efficiency, and using a low learning rate at the end of learning can increase accuracy.

In the training of neural networks, in general, the training data may be a subset of real data (that is, data to be processed using the trained neural network), and thus the error on the training data is reduced, but the error on the real data is reduced. There may be increasing learning cycles. Overfitting is a phenomenon in which errors on actual data increase by over-learning on training data as described above. For example, a phenomenon in which a neural network that has learned a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing errors in machine learning algorithms. In order to prevent such overfitting, various optimization methods can be used. In order to prevent overfitting, methods such as increasing training data, regularization, or dropout in which a part of nodes in the network are omitted in the process of learning, may be applied.

3 is an exemplary diagram for describing a contact model according to an embodiment of the present disclosure.

3, the keyboard 310, the laser sensor 320, the magnetic sensor 330, the contact speed measuring unit 340, the distance data 350, the contact model 370 and the contact speed data ( 390) is shown. In order to acquire distance data or contact speed data, not all of the above-described components should be included, and some of the above-described components may be omitted or other components may be further added.

According to an embodiment of the present disclosure, the computing device 100 may obtain distance data 350 between the laser sensor 320 and the keyboard 310 included in the electronic keyboard musical instrument by using the laser sensor 320 . . The distance data 350 may include data obtained using the magnetic sensor 330 . The distance data 350 may also include data obtained using an optical sensor or a pressure sensor. The computing device 100 may obtain the contact speed data 390 of the keyboard by using the contact speed measuring unit 340 . The computing device 100 may obtain the contact speed data 390 using the magnetic sensor 330 . The computing device 100 may also acquire the contact speed data 390 using an optical sensor or a pressure sensor. The contact speed may include the speed at which the keyboard approaches or moves away from a sensor attached to the keyboard. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the computing device 100 may generate a training data set for training a contact model. The computing device 100 may generate a training data set using the distance data 350 as training input data and the contact speed data 390 as a label. The computing device 100 may train the contact model 370 using a training data set for learning the contact model. The computing device 100 may obtain the predicted contact speed corresponding to the distance data by using the learned contact model. The foregoing is merely an example, and the present disclosure is not limited thereto.

4 is an exemplary diagram for explaining a sound generation model according to an embodiment of the present disclosure.

4, a laser sensor 410, a key 420 included in a classical keyboard instrument, a sound sensor 430, a string 440 of a classical keyboard instrument, distance data 450, a learned contact model 460, and prediction The contact velocity 470 , the sound generation model 480 , and the sound data 490 are shown. In order to acquire distance data or sound data, all the above-described components are not necessarily included, and some of the above-described components may be omitted or other components may be further added.

According to an embodiment of the present disclosure, the computing device 100 may obtain distance data 450 between the laser sensor 410 and a key 420 included in a classical keyboard musical instrument by using the laser sensor 410 . . The computing device 100 may obtain a sound generated from the strings 440 of a classical keyboard musical instrument by using the sound sensor 430 . The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the computing device 100 may generate a training data set for training the sound generation model 480 . The computing device 100 calculates the predicted contact speed 470 (ie, the predicted key contact speed 470 corresponding to the distance data 450) obtained by using the learned contact model 460 as the learning input data. And, it is possible to generate a training data set using the sound data 490 as a label. The computing device 100 may train the sound generation model 480 by using the training data set. The computing device 100 may generate a sound of a classical keyboard musical instrument corresponding to a contact speed by using the learned sound generation model. The foregoing is merely an example, and the present disclosure is not limited thereto.

5 is an exemplary diagram for explaining a process of generating a sound using a learned sound generation model according to an embodiment of the present disclosure.

5 shows the keyboard 310, the magnetic sensor 330, the contact speed measuring unit 340, the contact speed data 550, the learned sound generation model 570, and the sound 590 included in the electronic keyboard musical instrument. has been

According to an embodiment of the present disclosure, the computing device 100 may generate a sound 590 corresponding to the contact speed data 550 by using the learned sound generation model 570 . Specifically, the computing device 100 may use the learned sound generation model 570 to generate a sound of a classical keyboard musical instrument continuously corresponding to a contact speed of a keyboard included in the electronic keyboard musical instrument. Through this, the computing device ( ) may use the learned sound generation model to generate realistic and sophisticated sounds of classical musical instruments. The foregoing is merely an example, and the present disclosure is not limited thereto.

6 is a block diagram illustrating a module for generating sound using artificial intelligence.

According to an embodiment of the present disclosure, sound generation using artificial intelligence may be implemented by the following module.

According to an embodiment of the present disclosure, a module 610 for acquiring time series data from a measurement device for acquiring sensor data; a module 620 for generating a training data set based on the acquired time series data; a module 630 for training the sound generation model by inputting the training data set into the sound generation model; and a module 640 for generating sound by inputting the time series data obtained from the measurement device into the learned sound generation model.

In an alternative embodiment for generating a sound using artificial intelligence, the module 630 for learning the sound generating model by inputting the training data set into the sound generating model is configured by using a laser sensor included in the second measuring device. a module for acquiring distance data between a keyboard included in the classical keyboard musical instrument and a laser sensor included in the second measuring device; a module for predicting the contact speed of a key included in a keyboard of a classical musical instrument by inputting distance data into the contact model; and a module for learning by inputting a learning data set using the predicted key contact speed as learning input data, and labeling the sound data of a classical instrument obtained through a sound sensor included in the second measurement device, into the sound generation model may include.

In an alternative embodiment for generating a sound using artificial intelligence, the module 640 for generating a sound by inputting time series data obtained from the measuring device into the learned sound generating model is the keyboard obtained from the first measuring device. a module for inputting the contact speed of , into the learned sound generation model; and a module for generating a sound of a classical musical instrument corresponding to a touch speed of a keyboard by using the learned sound generation model.

According to an embodiment of the present disclosure, a module for generating a sound using artificial intelligence may be implemented by means, circuitry or logic for implementing a computing device. Those skilled in the art will further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be combined with electronic hardware, computer software, or combinations of both. It should be recognized that it can be implemented as To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

7 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above generally as being capable of being implemented by a computing device, those skilled in the art will appreciate that the present disclosure is a combination of hardware and software and/or in combination with computer-executable instructions and/or other program modules that may be executed on one or more computers. It will be appreciated that it can be implemented as a combination.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will appreciate that the methods of the present disclosure are suitable for single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. (each of which is It will be appreciated that other computer system configurations may be implemented, including those that may operate in connection with one or more associated devices.

The described embodiments of the present disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Any medium accessible by a computer can be a computer readable medium, and such computer readable media includes volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. including removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media includes volatile and non-volatile media, transitory and non-transitory media, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. includes media. A computer-readable storage medium may be RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device, or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store the desired information.

Computer readable transmission media typically embodies computer readable instructions, data structures, program modules or other data, etc. in a modulated data signal such as a carrier wave or other transport mechanism, and Includes any information delivery medium. The term modulated data signal means a signal in which one or more of the characteristics of the signal is set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An example environment 1100 implementing various aspects of the disclosure is shown including a computer 1102 , the computer 1102 including a processing unit 1104 , a system memory 1106 , and a system bus 1108 . do. The system bus 1108 couples system components, including but not limited to system memory 1106 , to the processing device 1104 . The processing device 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as processing unit 1104 .

The system bus 1108 may be any of several types of bus structures that may further be interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112 . A basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, EEPROM, etc., which is the basic input/output system (BIOS) that helps transfer information between components within computer 1102, such as during startup. contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

The computer 1102 may also be configured with an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) - this internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes—a magnetic floppy disk drive (FDD) 1116 (eg, for reading from or writing to removable diskette 1118), and an optical disk drive 1120 (eg, a CD-ROM) for reading from, or writing to, disk 1122, or other high capacity optical media such as DVD. The hard disk drive 1114 , the magnetic disk drive 1116 , and the optical disk drive 1120 are connected to the system bus 1108 by the hard disk drive interface 1124 , the magnetic disk drive interface 1126 , and the optical drive interface 1128 , respectively. ) can be connected to The interface 1124 for external drive implementation includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will use zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other tangible computer-readable media such as etc. may also be used in the exemplary operating environment and any such media may include computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in the drive and RAM 1112 , including an operating system 1130 , one or more application programs 1132 , other program modules 1134 , and program data 1136 . All or portions of the operating system, applications, modules, and/or data may also be cached in RAM 1112 . It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 via one or more wired/wireless input devices, for example, a pointing device such as a keyboard 1138 and a mouse 1140 . Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are connected to the processing unit 1104 through an input device interface 1142, which is often connected to the system bus 1108, parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, It may be connected by other interfaces, etc.

A monitor 1144 or other type of display device is also coupled to the system bus 1108 via an interface, such as a video adapter 1146 . In addition to the monitor 1144, the computer typically includes other peripheral output devices (not shown), such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148 via wired and/or wireless communications. Remote computer(s) 1148 may be workstations, computing device computers, routers, personal computers, portable computers, microprocessor-based entertainment devices, peer devices, or other common network nodes, and are typically connected to computer 1102 . Although it includes many or all of the components described for it, only memory storage device 1150 is shown for simplicity. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, eg, a wide area network (WAN) 1154 . Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, for example, the Internet.

When used in a LAN networking environment, the computer 1102 is connected to the local network 1152 through a wired and/or wireless communication network interface or adapter 1156 . Adapter 1156 may facilitate wired or wireless communication to LAN 1152 , which also includes a wireless access point installed therein for communicating with wireless adapter 1156 . When used in a WAN networking environment, the computer 1102 may include a modem 1158, be connected to a communication computing device on the WAN 1154, or establish communications over the WAN 1154, such as over the Internet. have other means. A modem 1158 , which may be internal or external and a wired or wireless device, is coupled to the system bus 1108 via a serial port interface 1142 . In a networked environment, program modules described for computer 1102 , or portions thereof, may be stored in remote memory/storage device 1150 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between the computers may be used.

Computer 1102 may be associated with any wireless device or object that is deployed and operates in wireless communication, for example, printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communications satellites, wireless detectable tags. It operates to communicate with any device or place, and phone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network or may simply be an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet, etc. without a wire. Wi-Fi is a wireless technology such as cell phones that allows these devices, eg, computers, to transmit and receive data indoors and outdoors, ie anywhere within range of a base station. Wi-Fi networks use a radio technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz radio bands, for example at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band). .

One of ordinary skill in the art of this disclosure will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols and chips that may be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields particles or particles, or any combination thereof.

Those of ordinary skill in the art of the present disclosure will recognize that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein include electronic hardware, (convenience For this purpose, it will be understood that it may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. A person skilled in the art of the present disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be interpreted as a departure from the scope of the present disclosure.

The various embodiments presented herein may be implemented as methods, apparatus, or articles of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash drives. memory devices (eg, EEPROMs, cards, sticks, key drives, etc.). Also, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. Based on design priorities, it is understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure. The appended method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not intended to be limited to the embodiments presented herein, but is to be construed in the widest scope consistent with the principles and novel features presented herein.

Claims (13)

A computer program stored in a computer-readable storage medium, wherein, when the computer program is executed on one or more processors, it performs the following operations for generating sound using artificial intelligence, the operations comprising:
acquiring time series data from a measurement device for acquiring sensor data;
generating a training data set based on the acquired time series data;
inputting the training data set to a sound generation model to train the sound generation model; and
generating sound by inputting the time series data obtained from the measurement device into the learned sound generation model;
including,
The measuring device is
A first measuring device installed in an electronic musical instrument to acquire sensor data and a second measuring device installed in a classical musical instrument to acquire sensor data,
The training data set is
Let the data obtained from the contact model be the learning input data, and the time series data obtained from the second measurement device as the label,
The contact model is
A model for predicting the touch speed of a keyboard, comprising a machine learning model for outputting the predicted touch speed of the keyboard by inputting distance data between the laser sensor and the keyboard obtained through a laser sensor,
A computer program stored in a computer-readable storage medium.
delete The method of claim 1,
The measuring device is
and the first measuring device installed in an electronic keyboard musical instrument to acquire data on the movement of the keyboard and the second measuring device installed in the classical keyboard musical instrument to acquire data and sound data on the movement of the keyboard.
A computer program stored in a computer-readable storage medium.
The method of claim 1,
The first measuring device,
At least one of a laser sensor, a magnetic sensor, an optical sensor, and a pressure sensor for acquiring data on the movement of a keyboard included in the electronic keyboard musical instrument,
A computer program stored in a computer-readable storage medium.
The method of claim 1,
The second measuring device,
at least one of a laser sensor for acquiring data on the keyboard movement of a classical keyboard instrument or a sound sensor for acquiring a sound of a classical musical instrument corresponding to the keyboard movement of the classical keyboard instrument,
A computer program stored in a computer-readable storage medium.
delete delete The method of claim 1,
The learned sound generation model is,
including a model trained by a machine learning method to generate a classical musical instrument sound corresponding to data input to the electronic musical instrument;
A computer program stored in a computer-readable storage medium.
9. The method of claim 8,
The classical musical instrument sound corresponding to the data input to the electronic musical instrument,
a classical musical instrument sound that discretely corresponds to data input to the electronic musical instrument; or
Comprising at least one of classical musical instrument sounds continuously corresponding to the data input to the electronic musical instrument,
A computer program stored in a computer-readable storage medium.
The method of claim 1,
The operation of learning the sound generation model by inputting the training data set to the sound generation model,
acquiring distance data between a key included in the classical keyboard musical instrument and a laser sensor included in the second measuring device by using a laser sensor included in the second measuring device;
predicting a contact speed of a key included in a keyboard of a classical musical instrument by inputting the distance data into a contact model; and
Using the predicted key contact speed as learning input data, and inputting a learning data set using the sound data of a classical instrument as a label obtained through a sound sensor included in the second measurement device into the sound generation model to learn action;
comprising,
A computer program stored in a computer-readable storage medium.
The method of claim 1,
The operation of generating sound by inputting the time series data obtained from the measurement device into the learned sound generation model,
inputting the key contact speed obtained from the first measurement device into the learned sound generation model; and
generating a sound of a classical musical instrument corresponding to the contact speed of the keyboard by using the learned sound generation model;
comprising,
A computer program stored in a computer-readable storage medium.
A method for generating sound using artificial intelligence, comprising:
acquiring time series data from a measurement device for acquiring sensor data;
generating a training data set based on the acquired time series data;
inputting the training data set into a sound generation model to train the sound generation model; and
generating sound by inputting the time series data obtained from the measurement device into the learned sound generation model;
including,
The measuring device is
A first measuring device installed in an electronic musical instrument to acquire sensor data and a second measuring device installed in a classical musical instrument to acquire sensor data,
The training data set is
Let the data obtained from the contact model be the learning input data, and the time series data obtained from the second measurement device as the label,
The contact model is
A model for predicting the touch speed of a keyboard, comprising a machine learning model for outputting the predicted touch speed of the keyboard by inputting distance data between the laser sensor and the keyboard obtained through a laser sensor,
A method for generating sound using artificial intelligence.
A computing device for generating sound using artificial intelligence, comprising:
one or more processors; and
a memory storing instructions executable by the one or more processors;
including,
The one or more processors,
acquiring time series data from a measuring device for acquiring sensor data,
Create a training data set based on the acquired time series data,
input the training data set to a sound generation model to train the sound generation model, and
To generate a sound by inputting the time series data obtained from the measurement device into the learned sound generation model,
The measuring device is
A first measuring device installed in an electronic musical instrument to acquire sensor data and a second measuring device installed in a classical musical instrument to acquire sensor data,
The training data set is
Let the data obtained from the contact model be the learning input data, and the time series data obtained from the second measurement device as the label,
The contact model is
A model for predicting the touch speed of a keyboard, comprising a machine learning model for outputting the predicted touch speed of the keyboard by inputting distance data between the laser sensor and the keyboard obtained through a laser sensor,
A computing device for generating sound using artificial intelligence.

Images