A method for automatically generating an audio signal, the method comprising receiving a source audio signal analyzing the source audio signal to identify a musical parameter characteristic thereof obtaining a supplemental audio signal based on the identified musical parameter characteristic and combining the source audio signal and the supplemental audio signal to form an extended audio signal.

An arrangement generation method executed by a computer includes acquiring target musical piece data that include performance information that indicates a melody and a chord of at least a part of a musical piece and include meta information that indicates characteristics of at least the part of the musical piece, generating, from the acquired target musical piece data, by using a generative model trained by machine learning, arrangement data obtained by arranging the performance information in accordance with the meta information, and outputting the generated arrangement data.

An estimation model construction method realized by a computer includes preparing a plurality of training data that include first training data that include first feature amount data that represent a first feature amount of a performance sound of a musical instrument and first onset data that represent a pitch at which an onset exists, and second training data that include second feature amount data that represent a second feature amount of sound generated by a sound source of a type different than the musical instrument, and second onset data that represent that an onset does not exist, and constructing, by machine learning using the plurality of training data, an estimation model that estimates, from a feature amount data that represent a feature amount of a performance sound of the musical instrument, estimated onset data that represent a pitch at which an onset exists.

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 method and a system for synthesizing beat sound and music. The method comprises: selecting a sound material as a beat sound (S101); intelligently or manually regulating, according to a human body characteristic parameter, a beat sound frequency matched with the human body characteristic parameter (S102); selecting music (S103); and synthesizing the beat sound and the music in a volume ratio to obtain mixed music (S104). The method and system for synthesizing beat sound and music provided by the present disclosure can intelligently or manually regulate the beat sound frequency and the volume ratio of the beat sound and the music through human body characteristic parameter, and obtain the mixed music in various synthesis ways. The mixed music can be used in trainings for physical and mental relaxations and improving the concentration of the human body, and is suitable for practices of improving sleeping conditions of the human body, and improving practice effects of yoga, meditation, and zazen.

A method includes displaying a user interface of a digital audio workstation, which includes a first region for generating a composition. The first region includes a first compositional segment that has been added to the composition by a user. Based on the first compositional segment, one or more recommended predefined compositional segments are identified and displayed in a second region. The method includes receiving the selection of a second compositional segment. The method includes adding the compositional segment to the composition.

Systems and methods for facilitating transactions of musical stems between users of an online gaming platform and mixing of the musical stems are disclosed. Exemplary implementations may: store information regarding tracks; transmit a purchase request regarding a given stem; receive a notification of the assignment of the right to use the given stem, wherein the assignment is recorded on a blockchain; obtain the given stem, generate an arrangement based on one or more stored tracks and the given stem, and playing back the generated arrangement.

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.

The present disclosure provides method and apparatus for providing personalized songs in automated chatting. A message may be received in a chat flow. Personalized lyrics of a user may be generated based at least on a personal language model of the user in response to the message. A personalized song may be generated based on the personalized lyrics. The personalized song may be provided in the chat flow.