Generating music accompaniment
AssigneeMacdougal Street Technology, Inc.
InventorsILIA BELIKOV, ALEKSANDR ALEKSEEV, VAZGEN HAKOBJANYAN, ROBERT JOSEPH PFEIFER
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Abstract
Techniques are described for generating music data for accompaniment using machine learning (ML) techniques. In an implementation, MIDI-formatted music data is tokenized as a music token sequence. Based on the input music token sequence of a portion of an original music signal, a trained ML model generates an output music token sequence for a portion of a generated music signal. Such a portion of the generated music signal is temporally ahead of the portion of the original music signal, which was used to generate the portion of the generated music signal.
Claims21
A computer-implemented method comprising:
- receiving a first input music sequence data that corresponds to a first portion of an original music signal;
- based at least in part on the first input music sequence data, generating, by a machine learning (ML) model, a first output music sequence data;
- while receiving a subsequent input music sequence data that corresponds to a subsequent portion of the original music signal that is temporally after the first portion of the original music signal, generating, by the ML model, a second output music sequence data, for a generated music signal, based at least in part on a particular portion of the first output music sequence data;
- wherein the first output music sequence data of the generated music signal corresponds to a portion of the generated music signal that is temporally aligned with the subsequent portion of the original music signal.
One or more non-transitory computer-readable media storing a set of instructions, wherein the set of instructions includes instructions, which when executed by one or more processors, cause:
- receiving a first input music sequence data that corresponds to a first portion of an original music signal;
- based at least in part on the first input music sequence data, generating, by a machine learning (ML) model, a first output music sequence data;
- while receiving a subsequent input music sequence data that corresponds to a subsequent portion of the original music signal that is temporally after the first portion of the original music signal, generating, by the ML model, a second output music sequence data, for a generated music signal, based at least in part on a particular portion of the first output music sequence data;
- wherein the first output music sequence data of the generated music signal corresponds to a portion of the generated music signal that is temporally aligned with the subsequent portion of the original music signal.
A system comprising one or more processors and one or more storage media storing one or more computer programs for execution by the one or more processors, wherein the one or more computer programs, when executed by the one or more processors, cause:
- receiving a first input music sequence data that corresponds to a first portion of an original music signal;
- based at least in part on the first input music sequence data, generating, by a machine learning (ML) model, a first output music sequence data;
- while receiving a subsequent input music sequence data that corresponds to a subsequent portion of the original music signal that is temporally after the first portion of the original music signal, generating, by the ML model, a second output music sequence data, for a generated music signal, based at least in part on a particular portion of the first output music sequence data;
- wherein the first output music sequence data of the generated music signal corresponds to a portion of the generated music signal that is temporally aligned with the subsequent portion of the original music signal.
Dependencies are inferred from the claim text.