1. A method for synthesizing a song using an artificial intelligence platform, comprising: receiving input data representing musical concepts and preferences; using the input data to generate a musical composition in real-time; refining the musical composition through iterative evaluation and modification of the generated output; outputting the final synthesized song.

2. The method of claim 1, wherein the input data includes one or more of a desired tempo, key, melody, harmony, and rhythm.

3. The method of claim 1, wherein the artificial intelligence platform uses machine learning techniques to generate the musical composition.

4. The method of claim 1, wherein the iterative evaluation and modification of the generated output is performed by the artificial intelligence platform.

5. The method of claim 1, further comprising: providing a user interface for controlling the input data and refining the synthesized song.

6. A system for synthesizing a song through artificial intelligence, comprising: a data input module for receiving input data representing musical concepts and preferences; a song synthesis module for generating a musical composition in real-time based on the input data; a refinement module for iteratively evaluating and modifying the generated output; a output module for outputting the final synthesized song.

7. The system of claim 6, wherein the song synthesis module uses machine learning techniques to generate the musical composition.

8. The system of claim 6, further comprising a user interface for controlling the input data and refining the synthesized song. SYNTHESIS OF SONG BY ARTIFICIAL INTELLIGENCE Abstract One example of an embodiment of the present disclosure is a method for synthesising a song on a platform powered by artificial intelligence that comprises collecting input data representing musical ideas and preferences. This method is only one example of an embodiment. One possible implementation is making use of the input data to generate a musical composition in real time. This is another embodiment that might be incorporated. In certain implementations, there is also a process of refining the musical composition by repeatedly assessing and adjusting the output that was produced. This may be done in a number of different ways. It is probable that certain , Claims:Claims :

1. A method for synthesizing a song using an artificial intelligence platform, comprising: receiving input data representing musical concepts and preferences; using the input data to generate a musical composition in real-time; refining the musical composition through iterative evaluation and modification of the generated output; outputting the final synthesized song.

2. The method of claim 1, wherein the input data includes one or more of a desired tempo, key, melody, harmony, and rhythm.

3. The method of claim 1, wherein the artificial intelligence platform uses machine learning techniques to generate the musical composition.

4. The method of claim 1, wherein the iterative evaluation and modification of the generated output is performed by the artificial intelligence platform.

5. The method of claim 1, further comprising: providing a user interface for controlling the input data and refining the synthesized song.

6. A system for synthesizing a song through artificial intelligence, comprising: a data input module for receiving input data representing musical concepts and preferences; a song synthesis module for generating a musical composition in real-time based on the input data; a refinement module for iteratively evaluating and modifying the generated output; a output module for outputting the final synthesized song.

7. The system of claim 6, wherein the song synthesis module uses machine learning techniques to generate the musical composition.

8. The system of claim 6, further comprising a user interface for controlling the input data and refining the synthesized song.

Description:SYNTHESIS OF SONG BY ARTIFICIAL INTELLIGENCE
Field of the Invention
[0001] The invention relates to the field of music composition, particularly to the use of artificial intelligence (AI) algorithms to synthesize songs. The technology involves using AI to analyze and understand the structure and characteristics of existing music, and then using this knowledge to generate original songs that are stylistically similar to the analyzed music.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The synthesis of music by artificial intelligence (AI) has been a topic of interest for many years. The goal of AI music synthesis is to develop algorithms that can create music that is both pleasing to the ear and coherent in structure, like what a human composer might create. One approach to music synthesis is to use machine learning techniques to train algorithms on existing musical compositions, allowing them to learn the patterns and structures of music. Another approach is to use rule-based systems, where a set of rules is defined to generate music based on musical theory.
[0004] There are different types of algorithms that can be used for the synthesis of music with artificial intelligence. Generative Adversarial Networks (GANs) are a type of neural network that can be used for music generation. They work by training two neural networks: a generator that creates new music, and a discriminator that evaluates the generated music against a dataset of existing music. Recurrent Neural Networks (RNNs) - RNNs are a type of neural network that can be used for sequence-to-sequence learning, such as music generation.
[0005] RNNs use feedback loops to allow information to be passed from one time step to the next, making them useful for generating music with a long-term structure. VAEs are a type of neural network that can be used for generating music. They work by learning the underlying structure of a dataset of music and then generating new music that follows that structure. Rule-based systems use a set of predefined rules to generate music based on musical theory. These rules may include guidelines for melody, harmony, rhythm, and other aspects of music.
[0006] One of the biggest challenges in the synthesis of music with artificial intelligence is generating music that is both musically coherent and pleasing to the ear. Another challenge is the need for large datasets of musical compositions for training the algorithms. Additionally, there is a question of the originality of AI-generated music, as there is debate around whether AI-generated music can be considered truly original or whether it is simply a replication of existing musical styles and patterns.
[0007] AI-generated music can be used in music production, either as a starting point for human composers or as a final product. AI-generated music can be used in video game soundtracks, providing an interactive and adaptive soundtrack that responds to the player's actions. AI-generated music can be used in advertising and marketing campaigns, providing a unique and attention-grabbing sound.
[0008] AI-generated music can be used to create personalized music for individuals, such as personalized soundtracks for workout routines or relaxation sessions. The synthesis of music with artificial intelligence has the potential to revolutionize the way music is created and consumed. Few prior arts are listed below.
[0009] CN106373580B (By: BEIJING BAIDU NETCOM SCIENCE & TECHNOLOGY) The invention discloses a singing synthesis method based on artificial intelligence and a device. The method comprises steps that the lyric information and the music score information of a target song are acquired; the lyric information is inputted to a preset voice broadcast module to acquire broadcast voice; on the basis of the music score information, target playing duration of a meta syllable of each character of the lyric information and fundamental frequency of each note of the target song are determined; for each character of the broadcast voice, playing duration of a meta syllable of the character is adjusted to equal to the target playing duration, and a first adjustment voice is acquired; according to the fundamental frequency of each note of the target song, fundamental frequency of each character of the first adjustment voice is adjusted, and a synthesized song is acquired. Through the method, robot singing cost is reduced, voice characteristics of the synthesized song are consistent with robot voice characteristics, problems of rhythm, pitch and breath instability existing in human singing are avoided, and user hearing experience is improved.
[00010] CN112542155B (By: BEIJING BAIDU NETCOM SCIENCE & TECHNOLOGY) The invention discloses a song synthesis method and device, a model training method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence such as deep learning and intelligent voice. According to the specific implementation scheme, the song synthesis method comprises the steps of acquiring a phoneme sequence and a note sequence of a to-be-synthesized song based on a music score of the to-be-synthesized song; according to the phoneme sequence and the note sequence, generating acoustic feature information of the to-be-synthesized song by adopting a pre-trained acoustic model based on an alignment criterion; and synthesizing the song by adopting a pre-trained vocoder according to the acoustic feature information of the to-be-synthesized song. Due to the fact that the pre-trained acoustic model and the pre-trained vocoder are adopted, the accuracy of the synthesized song can be effectively guaranteed, the problems of tone shifting, dragging and the like are avoided, meanwhile, the song synthesis process is very simple and convenient, song synthesis can be achieved without professional participation, and the song synthesis efficiency is very high.
[00011] US8338687B2 (By: YAMAHA) Waveform data representative of singing voices of a singing music piece are analyzed to generate melody component data representative of variation over time in fundamental frequency component presumed to represent a melody in the singing voices. Then, through machine learning that uses score data representative of a musical score of the singing music piece and the melody component data, a melody component model, representative of a variation component presumed to represent the melody among the variation over time in fundamental frequency component, is generated for each combination of notes. Parameters defining the melody component models and note identifiers indicative of the combinations of notes whose variation over time in fundamental frequency component are represented by the melody component models are stored into a pitch curve generating database in association with each other.
[00012] While there are still challenges to be addressed, such as the need for large datasets and the question of originality, the development of AI-generated music is an exciting area of research with many potential applications. As technology continues to evolve, it will be interesting to see how AI-generated music evolves alongside it.All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
[00013] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[00014] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00015] The following paragraphs provide additional support for the claims of the subject application.
[00016] The invention relates to the field of music composition, particularly to the use of artificial intelligence (AI) algorithms to synthesize songs. The technology involves using AI to analyze and understand the structure and characteristics of existing music, and then using this knowledge to generate original songs that are stylistically similar to the analyzed music.
[00017] Embodiments of the present disclosure may include a method for synthesizing a song using an artificial intelligence platform, including receiving input data representing musical concepts and preferences. Embodiments may also include using the input data to generate a musical composition in real-time. Embodiments may also include refining the musical composition through iterative evaluation and modification of the generated output. Embodiments may also include outputting the final synthesized song.
[00018] In some embodiments, the input data includes one or more of a desired tempo, key, melody, harmony, and rhythm. In some embodiments, the artificial intelligence platform uses machine learning techniques to generate the musical composition. In some embodiments, the iterative evaluation and modification of the generated output may be performed by the artificial intelligence platform. In some embodiments, the method may include providing a user interface for controlling the input data and refining the synthesized song.
[00019] Embodiments of the present disclosure may also include a system for synthesizing a song through artificial intelligence, including a data input module for receiving input data representing musical concepts and preferences. Embodiments may also include a song synthesis module for generating a musical composition in real-time based on the input data. Embodiments may also include a refinement module for iteratively evaluating and modifying the generated output. Embodiments may also include a output module for outputting the final synthesized song. In some embodiments, the song synthesis module uses machine learning techniques to generate the musical composition. In some embodiments, the system may include a user interface for controlling the input data and refining the synthesized song.
Brief Description of the Drawings
[00020] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00021] FIG. 1 is a flowchart illustrating a method for synthesizing a song, according to some embodiments of the present disclosure.
[00022] FIG. 2 is a block diagram illustrating a system for synthesizing a song, according to some embodiments of the present disclosure.
Detailed Description
[00023] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00024] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00025] The invention relates to the field of music composition, particularly to the use of artificial intelligence (AI) algorithms to synthesize songs. The technology involves using AI to analyze and understand the structure and characteristics of existing music, and then using this knowledge to generate original songs that are stylistically similar to the analyzed music.
[00026] A method for synthesising a song is detailed in the form of a flowchart in Figure 1, which also provides an explanation of the process in accordance with some implementations of the present disclosure. Obtaining input data reflecting musical conceptions and preferences is a step that is not required in all implementations of the approach at step 110, but it is a step that may be included in some of those implementations. The process may include, at step 120, the step of producing a musical composition in real time by making use of the data that was input. The procedure may involve refining the musical composition at step 130 by repeatedly assessing and adjusting the output that was produced. Playing back the music that has been entirely synthesised is an option for the procedure to choose at step 140.
[00027] In certain cases, the input data may contain a desired tempo, key, melody, harmony, or rhythm. In other cases, the input data may include a desired rhythm. In other instances, the data could include each and every one of these five components. The artificial intelligence platform may, in particular implementations, make use of the procedures connected with machine learning while it is in the process of developing the musical composition. It's possible that, in certain implementations, the artificial intelligence platform will be the one to carry out the iterative evaluation and adjustment of the result that was developed. One of these steps that may be incorporated in some implementations of the technique is the provision of a user interface for the management of the input data and the improvement of the music that was synthesised.
[00028] As a block diagram, the system 200 for synthesizing a song is shown in FIG. 2, which also offers a description of the system in line with different features of the present disclosure. The system 200 may, in some implementations, include a data input module 210 that is responsible for receiving input data representing musical concepts and preferences, a song synthesis module 220 that is responsible for generating a musical composition in real-time based on the input data, a refinement module 230 that is responsible for iteratively evaluating and modifying the generated output, and an output module 240 that is responsible for outputting the final song track. In some implementations, the song synthesis module 220 is able to construct the musical composition by using a variety of machine learning-related strategies. In certain embodiments, the system 200 may be provided with a user interface that enables the user to manipulate the input data and make modifications to the synthesised music.
[00029] The present invention relates to a system for synthesizing a song includes a computer processor that is configured to generate a music composition based on a set of musical elements using an artificial intelligence model. The set of musical elements may include things like chord progressions, melodies, rhythms, and instrumentation. The user interface allows the user to select the set of musical elements that they want to use in their composition. Hence, the system for synthesizing a song provides a way for users to generate original music compositions using a set of musical elements and an artificial intelligence model. The system allows for customization and iteration, providing users with an efficient and effective way to create music without requiring extensive musical knowledge or training.
[00030] In yet another embodiment, once the user has selected the set of musical elements, the computer processor uses the artificial intelligence model to generate a music composition. The artificial intelligence model may use various techniques such as deep learning, machine learning, and neural networks to generate a composition that is musically coherent and aesthetically pleasing. The model may analyse various aspects of music, including melody, harmony, rhythm, and instrumentation, to create a composition that meets the user's preferences.
[00031] Referring to the preceding embodiment, in the system for synthesizing a song, the generator network of the generative adversarial network (GAN) is trained on a dataset of musical elements, such as melodies, chords, and rhythms, to learn patterns and relationships between different musical elements. Once the generator network is trained, it can generate new music compositions based on a set of musical elements provided by the user. The generative adversarial network (GAN) is a type of artificial intelligence model that consists of two neural networks, a generator and a discriminator, that are trained together in a competitive process. In the context of music synthesis, the generator network is trained to produce new musical compositions based on a set of inputs, while the discriminator network is trained to distinguish between real and generated musical compositions.
[00032] Referring to the preceding embodiment, the user may select a melody, chord progression, and drum beat as the set of musical elements, and the generator network will use this input to create a new music composition that incorporates these elements. The generator network may also introduce variations and improvisations to create a unique and original composition. The output of the generator network is then rendered into an audio file using a digital audio workstation (DAW) or other audio synthesis software. The user can then listen to the rendered audio file and make any desired adjustments or modifications using the user interface. The generated music can also be saved and used in various applications, such as video games, film scores, or background music for podcasts or videos.
[00033] In yet another embodiment, after generating the music composition, the computer processor renders it into an audio file. The rendering process may involve using various tools such as digital audio workstations and synthesizers to produce a high-quality audio file that accurately represents the music composition. After the computer processor generates the music composition using the artificial intelligence model, it needs to be rendered into an audio file. This is the process of converting the music composition data into an actual audio file that can be played back to the user.
[00034] Referring to the preceding embodiment, the system may use digital audio workstations (DAWs) that provide a suite of tools and effects for editing and producing music. The DAWs can take the music composition generated by the artificial intelligence model and add various elements such as effects, EQ, and compression to produce a polished final mix. Another tool that may be used in the rendering process is a synthesizer. Synthesizers allow for the creation of electronic sounds using a variety of different techniques such as subtractive synthesis, additive synthesis, and frequency modulation. They can be used to add a range of different sounds and textures to the music composition.
[00035] Referring to the preceding embodiment, EQ, or equalization, is a tool used in music production to adjust the balance of frequencies in an audio track. For example, if a track has too much bass or treble, an EQ can be used to cut or boost those frequencies to achieve a more balanced sound. Similarly, compression is another tool used in music production that reduces the dynamic range of an audio signal, making the loud parts quieter and the quiet parts louder. This can help to even out the overall volume of a track and bring out subtle details in the performance.
[00036] Referring to the preceding embodiment, in the context of synthesizing a song, EQ and compression can be used in the final mixing stage to create a polished audio file. For example, after the generative adversarial network has produced a music composition and it has been rendered into an audio file, the audio file may then be processed with EQ and compression to enhance its sound quality. The audio engineer may adjust the levels of different frequencies using EQ to achieve a more balanced mix and use compression to control the dynamic range of the track and ensure that it sounds consistent and professional.
[00037] Referring to the preceding embodiment, the final output of the rendering process is a high-quality audio file that accurately represents the music composition. The file can be saved in various formats such as MP3, WAV, or FLAC, depending on the desired level of quality and file size. Once the audio file has been rendered, it can be played back to the user through the system's user interface, providing them with a fully synthesized and original song.
[00038] In yet another embodiment, the user interface displays the rendered audio file, allowing the user to listen to the synthesized song. The user can also adjust the set of musical elements used in the composition and generate a new version of the song. The user may further edit the audio file by adding or removing musical elements, adjusting the tempo, or changing the instrumentation. The user interface can provide various options for the user to provide feedback on the generated music composition, such as a rating system or a comment section. For instance, the user can rate the generated music composition on a scale of 1 to 5 stars or provide specific feedback on what they liked or disliked about the composition.
[00039] Referring to the preceding embodiment, based on the feedback provided by the user, the system can use an artificial intelligence model to analyse the feedback and identify patterns in the user's preferences. The system can then adjust the parameters of the generative model to create music compositions that better align with the user's preferences. For example, if the user consistently rates compositions that have a faster tempo and a brighter tone with higher ratings, the system can adjust the generative model to prioritize those features in future compositions.
[00040]
[00041] A technique for synthesising a song on an artificial intelligence platform that includes receiving input data indicating musical ideas and preferences is one example of an embodiment of the present disclosure. Using the input data in order to construct a musical composition in real time is another embodiment that may be included. Some embodiments may further involve the process of perfecting the musical composition by iteratively evaluating and modifying the output that was created. It's possible that certain embodiments include actually producing the finished synthetic tune.
[00042] In some implementations, the data that is being entered comprises at least one of the following: a desired tempo, key, melody, harmony, and rhythm. In certain implementations, the artificial intelligence platform will develop the musical composition via the use of various methods of machine learning. In some implementations, the artificial intelligence platform may be responsible for carrying out the iterative assessment and change of the result that was created. Providing a user interface for managing the input data and improving the synthesised music is one such step that may be included in certain implementations of the approach.
[00043] A system for synthesising a song using artificial intelligence may also be included in embodiments of the current disclosure. Such a system would contain a data input module for accepting input data expressing musical ideas and preferences. A song synthesis module that can generate a musical composition in real-time based on the input data is another possible component that may be included in embodiments. A refinement module, which may be included in embodiments, is often used to iteratively evaluate and change the output that is created. It's possible that certain embodiments will incorporate a separate output module that will play back the completed synthesised music. In certain implementations, the musical composition is generated by the song synthesis module via the use of methods related to machine learning. A user interface may be included in the system in some implementations so that the user may control the data that is being entered and improve the music that is being synthesised.
[00044] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00045] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
[00046] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00047] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00048] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. A method for synthesizing a song using an artificial intelligence platform, comprising:
receiving input data representing musical concepts and preferences;
using the input data to generate a musical composition in real-time;
refining the musical composition through iterative evaluation and modification of the generated output;
outputting the final synthesized song.
2. The method of claim 1, wherein the input data includes one or more of a desired tempo, key, melody, harmony, and rhythm.
3. The method of claim 1, wherein the artificial intelligence platform uses machine learning techniques to generate the musical composition.
4. The method of claim 1, wherein the iterative evaluation and modification of the generated output is performed by the artificial intelligence platform.
5. The method of claim 1, further comprising:
providing a user interface for controlling the input data and refining the synthesized song.
6. A system for synthesizing a song through artificial intelligence, comprising:
a data input module for receiving input data representing musical concepts and preferences;
a song synthesis module for generating a musical composition in real-time based on the input data;
a refinement module for iteratively evaluating and modifying the generated output;
a output module for outputting the final synthesized song.
7. The system of claim 6, wherein the song synthesis module uses machine learning techniques to generate the musical composition.
8. The system of claim 6, further comprising a user interface for controlling the input data and refining the synthesized song.

SYNTHESIS OF SONG BY ARTIFICIAL INTELLIGENCE
Abstract
One example of an embodiment of the present disclosure is a method for synthesising a song on a platform powered by artificial intelligence that comprises collecting input data representing musical ideas and preferences. This method is only one example of an embodiment. One possible implementation is making use of the input data to generate a musical composition in real time. This is another embodiment that might be incorporated. In certain implementations, there is also a process of refining the musical composition by repeatedly assessing and adjusting the output that was produced. This may be done in a number of different ways. It is probable that certain , Claims:Claims
I/We Claim:
1. A method for synthesizing a song using an artificial intelligence platform, comprising:
receiving input data representing musical concepts and preferences;
using the input data to generate a musical composition in real-time;
refining the musical composition through iterative evaluation and modification of the generated output;
outputting the final synthesized song.
2. The method of claim 1, wherein the input data includes one or more of a desired tempo, key, melody, harmony, and rhythm.
3. The method of claim 1, wherein the artificial intelligence platform uses machine learning techniques to generate the musical composition.
4. The method of claim 1, wherein the iterative evaluation and modification of the generated output is performed by the artificial intelligence platform.
5. The method of claim 1, further comprising:
providing a user interface for controlling the input data and refining the synthesized song.
6. A system for synthesizing a song through artificial intelligence, comprising:
a data input module for receiving input data representing musical concepts and preferences;
a song synthesis module for generating a musical composition in real-time based on the input data;
a refinement module for iteratively evaluating and modifying the generated output;
a output module for outputting the final synthesized song.
7. The system of claim 6, wherein the song synthesis module uses machine learning techniques to generate the musical composition.
8. The system of claim 6, further comprising a user interface for controlling the input data and refining the synthesized song.