Indications of sample machine learning models which create synthetic content items are provided via programmatic interfaces. A representation of a synthetic content item produced by one of the sample models in response to input obtained from a client of a provider network is presented. In response to a request from the client, a machine learning model is trained to produce additional synthetic content items.

Indications of sample machine learning models which create synthetic content items are provided via programmatic interfaces. A representation of a synthetic content item produced by one of the sample models in response to input obtained from a client of a provider network is presented. In response to a request from the client, a machine learning model is trained to produce additional synthetic content items.

A method for generating a musical composition based on user input is described. A first set of musical characteristics from a first input musical piece is received as an input vector. The first set of musical characteristics is perturbed to create a perturbed input vector as input in a first set of nodes in a first visible layer of an unsupervised neural net. The unsupervised neural net comprised of a plurality of computing layers, each computing layer composed of a respective set of nodes. The unsupervised neural net is operated to calculate an output vector from a higher level hidden layer in the unsupervised neural net. The output vector is used to create an output musical piece.

Musical catalog amplification services that leverage or deploy a computer-based musical composition system are described. The computer-based musical composition system employs algorithms and, optionally, artificial intelligence to generate new music based on analyses of existing music. The new music may be wholly distinctive from, or may include musical variations of, the existing music. Rights in the new music generated by the computer-based musical composition system are granted to the rights holder(s) of the existing music. In this way, the musical catalog(s) of the rights holder(s) is/are amplified to include additional music assets. The computer-based musical composition system may be tuned so that the new music sounds more like, or less like, the existing music of the rights holder(s). Revenues generated from the new music are shared between the musical catalog amplification service provider and the rights holder(s).

Implementations of the subject matter described herein provide a solution that enables a machine to automatically generate a melody. In this solution, user emotion and/or environment information is used to select a first melody feature parameter from a plurality of melody feature parameters, wherein each of the plurality of melody feature parameters corresponds to a music style of one of a plurality of reference melodies. The first melody feature parameter is further used to generate a first melody that conforms to the music style and is different from the reference melody. Thus, a melody that matches user emotions and/or environmental information may be automatically created.

A method for analyzing one or more notes in a musical composition, comprising for each note: getting a note, a chord and a scale. computing note properties using the note's value and the chord and the scale. A method for transforming one or more input notes into one or more new notes, comprising for each input note: getting an input note and its note properties, getting a new chord and a new scale for the input note, getting a list of notes candidates, computing distances between the input note and every note in the list, using input note's value, input note's note properties, candidate note's value and candidate note's note properties, finding the candidate that has the minimal distance, and setting a new note value using a note value of the candidate with the minimal distance.

An information processing apparatus according to the present disclosure includes: a storage unit that stores a plurality of pieces of music feature information in which a plurality of types of feature amounts extracted from music information is associated with predetermined identification information, the music feature information being used as learning data in composition processing using machine learning; a reception unit that receives instruction information transmitted from a terminal apparatus; an extraction unit that extracts the music feature information from the storage unit according to the instruction information; and an output unit that outputs presentation information of the music feature information extracted by the extraction unit.

A music generation device includes: an acquisition unit that acquires first stream data and second stream data different from the first stream data; an accompaniment generation unit that generates accompaniment information, which is music data indicating an accompaniment, based on a change in the first stream data; a melody generation unit that generates melody information, which is music data indicating a melody, based on a change in the second stream data; a melody adjustment unit that adjusts the melody information in accordance with a key of the accompaniment indicated by the generated accompaniment information; a music combining unit that combines the accompaniment information and the adjusted melody information to generate musical piece information; and an output unit that outputs the generated musical piece information.

A method for training one or more AI models for generating audio scores accompanying visual datasets includes obtaining training data comprising a plurality of audiovisual datasets and analyzing each of the plurality of audiovisual datasets to extract multiple visual features, textual features, and audio features. The method also includes correlating the multiple visual features and textual features with the multiple audio features via a machine learning network. Based on the correlations between the visual features, textual features, and audio features, one or more AI models are trained for composing one or more audio scores for accompanying a given dataset.

Musical catalog amplification services that leverage or deploy a computer-based musical composition system are described. The computer-based musical composition system employs algorithms and, optionally, artificial intelligence to generate new music based on analyses of existing music. The new music may be wholly distinctive from, or may include musical variations of, the existing music. Rights in the new music generated by the computer-based musical composition system are granted to the rights holder(s) of the existing music. In this way, the musical catalog(s) of the rights holder(s) is/are amplified to include additional music assets. The computer-based musical composition system may be tuned so that the new music sounds more like, or less like, the existing music of the rights holder(s). Revenues generated from the new music are shared between the musical catalog amplification service provider and the rights holder(s).