Computing devices and methods for providing a music customization graphical user interface (GUI) to a user computing device are disclosed. The music customization GUI comprises song name selectors that each correspond to a different song and a music player region that includes a song customization selector. A customization window comprises music stem indicators that each correspond to at least one music stem of a selected song. The customization window also comprises mixing buttons that include a first mixing button configured to add a corresponding music stem to a user song mix, and a second mixing button configured to remove the music stem from the user song mix. A download button is configured to download a file comprising the user song mix to a user computing device.

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.

An information processing method according to the present invention includes providing first musical piece information representing contents of a musical piece and performance information relating to a past performance prior to one unit period within the musical piece to a learner that has undergone learning relating to a specific tendency that relates to a performance, and generating, for the one unit period, performance information that is based on the specific tendency with the learner.

Systems, devices, and methods for segmenting musical compositions are described. Discrete, musically-coherent segments (such as intro, verse, chorus, bridge, solo, and the like) of a musical composition are identified. Distance measures are used to evaluate whether each bar of a musical composition is more like the bars that directly precede it or more like the bars that directly succeed it, and each respective series of musically similar bars is assigned to the same respective segment. Large changes in the distance measure(s) between adjacent bars may be used to identify boundaries between abutting musical segments. Computer systems and computer program products for implementing segmentation are also described. The results of segmentation may advantageously be applied in computer-based composition of music and musical variations, as well as in other applications involving labelling, characterizing, or otherwise processing music.

According to an embodiment, there is provided a system and method for the generation of alternative versions of audio material in DAW projects, wherein the alternative versions feature differing energy and dynamics levels in terms of their musical content.

A facility providing technological support for the initiation, performance, and/or registration of a collaborative songwriting project is described.

According to an embodiment, there is provided a system and method for automatic AI-based song construction based on ideas of a user. It provides and benefits from a combination of expert knowledge resident in an expert engine which contains rules for a musically correct song generation and machine learning in an AI-based audio loop selection engine for the selection of fitting audio loops from a database of audio loops.

Systems, devices, and methods for segmenting musical compositions are described. Discrete, musically-coherent segments (such as intro, verse, chorus, bridge, solo, and the like) of a musical composition are identified. Distance measures are used to evaluate whether each bar of a musical composition is more like the bars that directly precede it or more like the bars that directly succeed it, and each respective series of musically similar bars is assigned to the same respective segment. Large changes in the distance measure(s) between adjacent bars may be used to identify boundaries between abutting musical segments.

Computer systems and computer program products for implementing segmentation are also described. The results of segmentation may advantageously be applied in computer-based composition of music and musical variations, as well as in other applications involving labeling, characterizing, or otherwise processing music.

Conventionally, significant time and effort are required to construct audio tracks. Disclosed embodiments enable automation of audio tracks using templates that associate sound generator(s) with template section(s). Each template enables a model to automatically generate unique audio tracks in which the sections and/or sounds are probabilistically determined. Certain embodiments introduce additional variability into the automated generation of audio tracks. In addition, the model may generate the audio tracks, note by note, to ensure that no copyrights are infringed.