A system, method and computer product for combining audio tracks. In one example embodiment herein, the method comprises determining at least one music track that is musically compatible with a base music track, aligning those tracks in time, and combining the tracks. In one example embodiment herein, the tracks may be music tracks of different songs, the base music track can be an instrumental accompaniment track, and the at least one music track can be a vocal track. Also in one example embodiment herein, the determining is based on musical characteristics associated with at least one of the tracks, such as an acoustic feature vector distance between tracks, a likelihood of at least one track including a vocal component, a tempo, or musical key. Also, determining of musical compatibility can include determining at least one of a vertical musical compatibility or a horizontal musical compatibility among tracks.

This application provides a song processing method performed by a computer device. The method includes: presenting a song recording interface in response to a singing instruction triggered in a session interface of a group chat session; recording a song in response to a song recording instruction triggered in the song recording interface, and determining a reverberation effect corresponding to the recorded song; and transmitting, in response to a song transmitting instruction, a target song obtained by processing the song based on the reverberation effect to members of the group chat session, presenting a session message corresponding to the target song in the session interface, and presenting the pick-up singing function item corresponding to the target song in the session interface, the pick-up singing function item being used for implementing pick-up singing of the target song by a member of the group chat session.

The present invention relates to an apparatus and method for identifying music in a content, The present invention includes extracting and storing a fingerprint of an original audio in an audio fingerprint DB; extracting a first fingerprint of a first audio in the content; and searching for a fingerprint corresponding to the fingerprint of the first audio in the audio fingerprint DB, wherein the first audio is audio data in a music section detected from the content.

The disclosure provides an automatic performance apparatus, an automatic performance method, and a non-transitory computer readable medium. Notes to be sounded are stored in chronological order for each beat position which is a sound generation timing in a performance pattern. A probability of generating sound at the beat position is stored for each beat position in a sound generation probability pattern. According to the probability stored in the sound generation probability pattern, it is determined whether to generate sound or not for each beat position of the performance pattern.

Scalable similarity-based generation of compatible music mixes. Music clips are projected in a pitch interval space for computing musical compatibility between the clips as distances or similarities in the pitch interval space. The distance or similarity between clips reflects the degree to which clips are harmonically compatible. The distance or similarity in the pitch interval space between a candidate music clip and a partial mix can be used to determine if the candidate music clip is harmonically compatible with the partial mix. An indexable feature space may be both beats-per-minute (BPM)-agnostic and musical key-agnostic such that harmonic compatibility can be quickly determined among potentially millions of music clips. A graphical user interface-based user application allows users to easily discover combinations of clips from a library that result in a perceptually high-quality mix that is highly consonant and pleasant-sounding and reflects the principles of musical harmony.

A matrix is generated that stores sinusoidal components evaluated for a given sample rate corresponding to the matrix. The matrix is then used to convert an audio signal to chroma vectors representing of a set of “chromae” (frequencies of interest). The conversion of an audio signal portion into its chromae enables more meaningful analysis of the audio signal than would be possible using the signal data alone. The chroma vectors of the audio signal can be used to perform analyzes such as comparisons with the chroma vectors obtained from other audio signals in order to identify audio matches.

The present disclosure relates to systems, methods, and devices for music simulation. The methods may include determining one or more simulation actions based on data associated with one or more simulation actions acquired by at least one sensor. The methods may further include determining, based on at least one of the one or more simulation actions and a mapping relationship between simulation actions and corresponding musical instruments, a simulation musical instrument that matches with the one or more simulation actions. The methods may further include determining, based on the one or more simulation actions, one or more first features associated with the simulation musical instrument. The methods may further include playing music based on the one or more first features.

A method of making audio music selection and creating a mixtape, comprising importing song files from a song repository; sorting and filtering the song files based on selection criteria; and creating the mixtape from the song files sorting and filtering results. The sorting and filtering of the song files comprise: spectral analyzing each of the song files to extract low level acoustic feature parameters of the song file; from the low level acoustic feature parameter values, determining the high level acoustic feature parameters of the analyzed song file; determining a similarity score of each of the analyzed song files by comparing the acoustic feature parameter values of the analyzed song file against desired acoustic feature parameter values determined from the selection criteria; and sorting the analyzed song files according to their similarity scores; and filtering out the analyzed song files with first similarity scores lower than a filter threshold.

Techniques are disclosed relating to automatically generate new music content. In some embodiments, a computing system receivers user input specifying a user-defined music control element. The computing system may train a machine learning model to change both composition and performance parameters based on user adjustments to the user-defined music control element. In embodiments in which composition and performance subsystems are on different devices, one device may transmit configuration information to another device, where the configuration information specifies how to adjust parameters based on user input to the user-defined music control element. Disclosed techniques may facilitate centralized learning for human-like music production while allowing individualized customization for individual users. Further, disclosed techniques may allow artists to define their own abstract music controls and make those controls available to end-users.

Audio fingerprinting includes obtaining audio samples of a piece of audio, generating frequency representations of the audio samples, identifying increasing and decreasing energy regions in frequency bands of the frequency representations, and generating hashes of features of the piece of audio. Each hash of features corresponds to portions of the identified energy regions appearing in a respective time window. Each feature is defined as a numeric value that encodes information representing: a frequency band of an energy region appearing in the respective time window, whether the energy region appearing in the respective time window is an increasing energy region or whether the energy region appearing in the respective time window is a decreasing energy region, and a placement of the energy region appearing in the respective time window.