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. Additionally, in some embodiments there is provided a method of energy-based song construction where the tracks of a multi-track work are balanced depending on the desired output volume level of the final project.

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. Additionally, in some embodiments there is provided a method of energy-based song construction where the tracks of a multi-track work are balanced depending on the desired output volume level of the final project.

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).

The present disclosure relates to a method for generating a new chord sequence. The method comprises obtaining known chord sequences (S), wherein each known chord sequence has a respective known transition (T) between each pair of consecutive chords (C) in the known chord sequence. The method also comprises, among the chords of the known sequences, determining one or more new allowed transitions (N). The method also comprises, among the chords of the known chord sequences, generating a new chord sequence (L) based on there being a respective known or new allowed transition between each two consecutive chords in the new chord sequence, and the new chord sequence being loopable by there also being a known or new allowed transition between a first chord and a last chord in the new chord.

A first computer implements an audio mash-up computer program with a remote computer providing back-end operations via a data communications network. A graphical user interface on the first computer runs an audio mash-up computer program that provides digital content on the graphical user interface for identifying a plurality of respective audio sources of audio segments to combine into an output audio file. The graphical user interface is configured for displaying the respective audio sources as representative blocks of source data and allows a user to engage in arranging the representative blocks into lanes of respective audio tracks displayed on the graphical user interface. By combining the respective audio tracks into the output audio file according to an arrangement of the representative blocks as displayed in the lanes of respective audio tracks, the output audio file is configured for playing the respective audio tracks according to the arrangement.

An approach is provided in which an information handling system configures a reinforcement learning model based inspiration selections received from a user. The information handling system performs training iterations using the configured reinforcement learning model, which generates multiple actions and multiple rewards corresponding to multiple actions. The information handling system determines that the multiple rewards reach an empirical threshold and, in turn, generates a musical composition based on the multiple actions.

Computer-based systems, devices, and methods for assigning mood labels to musical compositions are described. A mood classifier is trained based on mood-labeled musically-coherent segments of musical compositions and subsequently applied to automatically assign mood labels to musically-coherent segments of musical compositions. In both cases, the musically-coherent segments are generated using automated segmentation algorithms.

A mix instructions file for controlling the playback of at least one music track file, the mix instructions file comprising one or more instructions including an indication of the at least one music track file at the point in time when the at least one music track file is to be accessed, and at least one effect for manipulating the playback of the at least one music track file. The indication of the at least one music track file and the at least one function comprise the state of the music mix at the point in time. The mix instructions file comprising at least a first and a second packet, that may be transmitted independently of each other, the second packet holding information about the playback state of the mix at the corresponding end of the first packet.

A content server generates sponsored audio including procedurally generated background music. The content server obtains reference music features describing musicological characteristics of reference songs as well as sponsored audio information received from a third-party. The content server determines music generation parameters based on the sponsored audio information and based on a music model mapping the reference music features to music generation parameters. The music model may incorporate machine learning techniques to improve the mapping. The content server generates background music by using the determined music generation parameters as input to a procedural music algorithm, and generates sponsored audio by combining the generated background music concurrently with an audio voiceover obtained from the sponsored audio information. The sponsored audio is provided to a client device, which presents the sponsored audio to a user.

Audio mix information is received from a plurality of users. Mix rules are determined from the audio mix information from the plurality of users, wherein the mix rules include a first mix rule associated with a first audio item. The first mix rule relates to an overlap of the first audio item with another audio item. The first mix rule is made available to one or more clients. After making the first mix rule available, an indication, from a respective client device, that the first audio item is to be mixed with a second audio item at the respective client device in accordance with the first mix rule is received. In response to the indication, a specification of the first mix rule is transmitted to the respective client device to be applied by the respective client device to generate a transition between the first audio item and the second item.