Music compilation methods disclosed herein include providing a database. Data is stored therein associating a user with access credentials for a plurality of music streaming services. A first server is communicatively coupled with the database and with multiple third party servers each of which includes a music library associated with the user. A list is stored in the database listing audio tracks of the libraries. A play selector is displayed on a user interface of a computing device communicatively coupled with the first server. User selection of the play selector initiates playback of a sample set, the sample set including portions of audio tracks in the list. The sample set is determined based on contextual information gathered by the computing device, the contextual information not including any user selection. Music compilation systems disclosed herein include systems configured to carry out the music compilation methods.

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.

The present disclosure describes techniques for identifying music attributes. The described techniques comprises receiving audio data of a piece of music; determining at least one attribute of the piece of music based on the audio data of the piece of music using a model; the model comprising a convolutional neural network and a transformer; the model being pre-trained using training data, wherein the training data comprise labelled data associated with a first plurality of music samples and unlabelled data associated with a second plurality of music samples, the labelled data comprise audio data of the first plurality of music samples and label information indicative of attributes of the first plurality of music samples, and the unlabelled data comprise audio data of the second plurality of music samples.

Systems, methods, architectures, mechanisms and apparatus for generating an audio segment playlist configured to provoke a physiological response in a listener in accordance with a desired outcome category, comprising: selecting, from a features database, a plurality of audio segments having features associated with both listener information and the desired outcome category; and ordering within the playlist at least a portion of the selected audio segments in accordance with at least one feature progression associated with the outcome category.

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.

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.

The present disclosure relates to an information processing apparatus, an information processing method, and a program enabling to present a feature serving as a reason and a basis of being searched, of a music piece searched in response to a request of a user. A music piece is searched on the basis of a search request of a user, and information is generated, as a response to the search request, in which a feature that is of the searched music piece and corresponds to the search request is expressed by a balloon size for each type of the feature. The present invention can be applied to a music piece agent device.

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.

The present disclosure provides a method and an apparatus for displaying lyric effects, an electronic device, and a computer-readable medium. The method includes: obtaining, based on a lyric effect display operation of a user, an image sequence and music data to be displayed, the music data including audio data and lyrics; determining a target time point, playing at least one target image corresponding to the target time point in the image sequence, and determining target lyrics corresponding to the target time point in the lyrics, and adding animation effects on the at least one target image, displaying the target lyrics on the at least one target image, and playing a part of the audio data corresponding to the target lyrics.