This application provide an audio detection method performed by a computer device. The method includes: acquiring a target time point and a reference point of the target time point from target audio data; performing energy evaluation on the target time point according to an audio amplitude value of the target time point to obtain an energy evaluation value of the target time point; performing energy evaluation on the reference point according to an audio amplitude value of the reference point to obtain an energy evaluation value of the reference point; performing accuracy verification on the target time point according to the energy evaluation value of the target time point and the energy evaluation value of the reference point; and if the accuracy verification on the target time point succeeds, adding the target time point as a target stress point into a target stress point set.

A computer-implemented information processing method includes: determining, based on musical instrument information indicative of a musical instrument, a target part of a body of a first player, the first player playing the musical instrument indicated by the musical instrument information; and acquiring image information indicative of imagery of the determined target part.

An intelligent system for matching audio with video of the present invention provides a video analysis module targeting color tone, storyboard pace, video dialogue, length and category and director's special requirement, actors expression, movement, weather, scene, buildings, spacial and temporal, things and a music analysis module targeting recorded music form, sectional turn, style, melody and emotional tension, and then uses an AI matching module to adequately match video of the video analysis module with musical characteristics of the music analysis module, so as to quickly complete a creative composition selection function with respect to matching audio with a video.

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.

Techniques are disclosed relating to implementing audio techniques for real-time audio generation. For example, a music generator system may generate new music content from playback music content based on different parameter representations of an audio signal. In some cases, an audio signal can be represented by both a graph of the signal (e.g., an audio signal graph) relative to time and a graph of the signal relative to beats (e.g., a signal graph). The signal graph is invariant to tempo, which allows for tempo invariant modification of audio parameters of the music content in addition to tempo variant modifications based on the audio signal graph.

To provide an electronic wind instrument capable of accurately detecting the amount of rotation of a transmission member. During an electronic wind instrument performance by a performer, external light (such as light from lighting) may easily shine on the upper surface side of an instrument main body. However, the light-receiving section of an optical sensor faces toward the bottom surface side of the instrument main body therefore external light from the upper surface side of the instrument main body can be prevented from reaching the light-receiving section of the optical sensor. As a result, erroneous detection of the external light by the optical sensor can be suppressed therefore the rotation amount of a transmission member can be accurately detected by the optical sensor.

A method for generating visual enhancement of areas of interest in images includes receiving data representing a plurality of images in a sequence of images; analyzing the plurality images to identify respective three dimensional (3D) locations of one or more areas of interest in the plurality of images; visually enhancing the one or more of the identified areas of interest in the plurality of the images in the sequence of images; and communicating the visually enhanced image data to a display device to be displayed.

Techniques are disclosed relating to implementing audio techniques for real-time audio generation. For example, a music generator system may generate new music content from playback music content based on different parameter representations of an audio signal. In some cases, an audio signal can be represented by both a graph of the signal (e.g., an audio signal graph) relative to time and a graph of the signal relative to beats (e.g., a signal graph). The signal graph is invariant to tempo, which allows for tempo invariant modification of audio parameters of the music content in addition to tempo variant modifications based on the audio signal graph.

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.