This application discloses a method for making music recommendations. The method for making music recommendations is performed by a server device. The method includes obtaining a material for which background music is to be added; determining at least one visual semantic tag of the material, the at least one visual semantic tag describing at least one characteristic of the material; identifying a matched music matching the at least one visual semantic tag from a candidate music library; sorting the matched music according to user assessing information of a user corresponding to the material; screening the matched music based on a sorting result and according to a preset music screening condition; and recommending matched music obtained through the screening as candidate music of the material.

An apparatus is provided that accurately estimates a point at which a musical performance is started by a player. The apparatus includes the musical performance analysis unit 32, and the musical performance analysis unit 32 obtains action data that includes a time series of feature data representing actions made by a player during a musical performance for a reference period and estimating a sound-production point based on the action data at an estimated point using a learned model L.

In some implementations, a computing device can generate personalized music selections to associate with any collections of visual media (e.g., photos and videos) stored on the computing device. A user may prefer particular genres of music and listen to some genres more frequently than others. The computing device can create measures of the user's genre preferences and use these measures to select music that is preferred by the user and music that may be significant or relevant to the particular collection of visual media. The computing device may also determine music that was being played when and where the visual media were being created. The computing device may store the visual media and music items in association with each other. The computing device may generate composite media items that combine the visual media and music items. When the visual media are viewed, the selected music item is also played.

An apparatus is provided that accurately estimates a point at which a musical performance is started by a player. The apparatus includes the musical performance analysis unit 32, and the musical performance analysis unit 32 obtains action data that includes a time series of feature data representing actions made by a player during a musical performance for a reference period and estimating a sound-production point based on the action data at an estimated point using a learned model L.

Aspects of the subject disclosure may include, for example, a device that includes a processing system having a processor and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, where the operations include determining parameters for adapting audio in the content to the device, wherein the device renders the content, and wherein the parameters are based on semantic metadata embedded in the content, adapting the audio in the content based on the parameters, and rendering the content, as adapted by the parameters, to represent a semantic in the semantic metadata. Other embodiments are disclosed.

The present disclosure provides a method for generating a video of a body moving in synchronization with music by applying a first artificial neural network (ANN) to a sequence of samples of an audio waveform of the music to generate a first latent vector describing the waveform and a sequence of coordinates of points of body parts of the body, by applying a first stage of a second ANN to the sequence of coordinates to generate a second latent vector describing movement of the body, by applying a second stage of the second ANN to static images of a person in a plurality of different poses to generate a third latent vector describing an appearance of the person, and by applying a third stage of the second ANN to the first latent vector, the second latent vector, and the third latent vector to generate the video.

The present disclosure provides a method for generating a video of a body moving in synchronization with music by applying a first artificial neural network (ANN) to a sequence of samples of an audio waveform of the music to generate a first latent vector describing the waveform and a sequence of coordinates of points of body parts of the body, by applying a first stage of a second ANN to the sequence of coordinates to generate a second latent vector describing movement of the body, by applying a second stage of the second ANN to static images of a person in a plurality of different poses to generate a third latent vector describing an appearance of the person, and by applying a third stage of the second ANN to the first latent vector, the second latent vector, and the third latent vector to generate the video.

A restorative musical system having a musical composition, which has a plurality of audio elements, a start and an end, and a slow tempo. The plurality of audio elements having a phrase, a plurality of arrangements, a pulsating sound, and a field recording element. The phrase and the pulsating sound adapted to repeat within the musical composition, while the plurality of arrangements is adapted to be introduced in succession. Additionally, the plurality of audio elements is audio mixed to play at a solfeggio frequency and are adapted to cause stereo imaging.

A method implemented by a processor includes receiving performance data including pitch data; determining, based on the pitch data that is included in the received performance data, a key among a plurality of keys; selecting, based on the determined key and the pitch data, a first-type image from among a plurality of first-type images; and displaying the selected first-type image.

The present invention relates to systems and methods for visual image audio composition. In particular, the present invention provides systems and methods for audio composition from a diversity of visual images and user determined sound database sources.