An information processing apparatus according to the present disclosure includes: an acquisition unit that acquires music information; an extraction unit that extracts a plurality of types of feature amounts from the music information acquired by the acquisition unit; and a generation unit that generates information in which the plurality of types of feature amounts extracted by the extraction unit is associated with predetermined identification information as music feature information to be used as learning data in composition processing using machine learning.

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.

Embodiments of this application provide an audio generation method, a related apparatus, and a storage medium, to provide a better audio generation solution for a user. In embodiments of this application, a text is obtained, a song clip corresponding to the text is obtained through matching, and the song clip is used as audio corresponding to the text. In this way, the text can be expressed in a manner of the song clip.

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.

An electronic musical instrument includes an operation unit that receives a user performance; and at least one processor. wherein the at least one processor performs the following: in accordance with a user operation specifying a chord on the operation unit, obtaining lyric data of a lyric and obtaining a plurality of pieces of waveform data respectively corresponding to a plurality of pitches indicated by the specified chord; inputting the obtained lyric data to a trained model that has been trained and learned singing voices of a singer so as to cause the trained model to output acoustic feature data in response thereto; synthesizing each of the plurality of pieces of waveform data with the acoustic feature data so as to generate a plurality of pieces of synthesized waveform data; and outputting a polyphonic synthesized singing voice based on the generated plurality of pieces of synthesized waveform data.

Visual effects schedules are applied to audiovisual performances with differing visual effects applied in correspondence with differing elements of musical structure. Segmentation techniques applied to one or more audio tracks (e.g., vocal or backing tracks) are used to compute some of the components of the musical structure. In some cases, applied visual effects schedules are mood-denominated and may be selected by a performer as a component of his or her visual expression or determined from an audiovisual performance using machine learning techniques.

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.

A method for rendering lyrics is provided, including: acquiring pronunciation of a polyphonic word to be rendered in target lyrics, and acquiring playback time information of the pronunciation in the process of rendering the target lyrics; determining a first number of furiganas contained in the pronunciation; and word-by-word simultaneously rendering, according to the first number and the playback time information of the pronunciation of the polyphonic word to be rendered, the polyphonic word to be rendered and each furigana in the pronunciation of the polyphonic word to be rendered, wherein the pronunciation of the polyphonic word to be rendered is adjacent to and parallel to the polyphonic word to be rendered.

A lyrics analyzer generates tags and explicitness indicators for a set of tracks. These tags may indicate the genre, mood, occasion, or other features of each track. The lyrics analyzer does so by generating an n-dimensional vector relating to a set of topics extracted from the lyrics and then using those vectors to train a classifier to determine whether each tag applies to each track. The lyrics analyzer may also generate playlists for a user based on a single seed song by comparing the lyrics vector or the lyrics and acoustics vectors of the seed song to other songs to select songs that closely match the seed song. Such a playlist generator may also take into account the tags generated for each track.

An electronic device receives audio data for a media item. The electronic device generates, from the audio data, a plurality of samples, each sample having a predefined maximum length. The electronic device, using a neural network trained to predict textal unit probabilities, generates a probability matrix of textual units for a first portion of a first sample of the plurality of samples. The probability matrix includes information about textual units, timing information, and respective probabilities of respective textual units at respective times. The electronic device identifies, for the first portion of the first sample, a first sequence of textual units based on the generated probability matrix.