A content selection system and method for identifying and organizing moods in content using objectively measured scores for rhythm, texture and pitch (RTP) and clustered into six mood classifications based on an objective analysis of the measured scores. Digitized representations of the content may also be identified and organized based on the content's frequency data, three-dimensional shapes derived from the digitized representations, and colors derived from the frequency data. Each piece of content may be identified by at least a mood shape, but may also be identified by a mood color and/or a mood based on the clustered RTP scores and/or the digitized representation. Users of the selection system may be able to view the moods identified in the different manners, or combinations of two or three mood identifying manners and select and organize content based on the identified moods.

The disclosed embodiments provide a system that generates sound primitives to facilitate sound recognition. First, the system performs a feature-detection operation on sound samples to detect a set of sound features, wherein each sound feature comprises a measurable characteristic of a window of consecutive sound samples. Next, the system creates feature vectors from coefficients generated by the feature-detection operation, wherein each feature vector comprises a set of coefficients for sound features detected in a window. The system then performs a clustering operation on the feature vectors to produce feature-vector clusters, wherein each feature-vector cluster comprises a set of feature vectors that are proximate to each other in a feature-vector space that contains the feature vectors. After the clustering operation, the system defines a set of sound primitives, wherein each sound primitive is associated with a feature-vector cluster. Finally, the system associates semantic labels with the set of sound primitives.

A method comprises: capturing images of a movable object in a scene and tracking movement of the object in the scene based on the images, to produce movement parameters that define the movement; generating for display an extended reality (XR) visualization of the physical object in the scene and changing the XR visualization responsive to changing ones of the movement parameters, such that the XR visualization visually reflects the tracked movement; displaying the XR visualization; and converting the movement parameters to control messages configured to control one or more of sound and light, and transmitting the control messages.

Embodiments of the present invention relate to automatically identifying structures of a music stream. A segment structure may be generated that visually indicates repeating segments of a music stream. To generate a segment structure, a feature that corresponds to a music attribute from a waveform corresponding to the music stream is extracted from a waveform, such as an input signal. Utilizing a signal segmentation algorithm, such as a Variable Markov Oracle (VMO) algorithm, a symbolized signal, such as a VMO structure, is generated. From the symbolized signal, a matrix is generated. The matrix may be, for instance, a VMO-SSM. A segment structure is then generated from the matrix. The segment structure illustrates a segmentation of the music stream and the segments that are repetitive.

A visualization method for music-color synesthesia, belonging to the fields of cognitive psychology, color science and computer graphics, and relating to the visualization of the music synesthesia correlation of colors and the generation of a musical color combination scheme. In the method, a musical scale system is presented in the form of a color scale system, and the musical elements are presented through the color correlation to realize the synesthetic conversion between colors and musical elements, and on this basis, a color graphical conversion of the musical structure is performed. The implementations include: a conversion from colors to musical scales, a conversion from colors to chords, a conversion from colors to rhythm and a conversion from colors to texture etc.

Embodiments of the present invention relate to automatically identifying structures of a music stream. A segment structure may be generated that visually indicates repeating segments of a music stream. To generate a segment structure, a feature that corresponds to a music attribute from a waveform corresponding to the music stream is extracted from a waveform, such as an input signal. Utilizing a signal segmentation algorithm, such as a Variable Markov Oracle (VMO) algorithm, a symbolized signal, such as a VMO structure, is generated. From the symbolized signal, a matrix is generated. The matrix may be, for instance, a VMO-SSM. A segment structure is then generated from the matrix. The segment structure illustrates a segmentation of the music stream and the segments that are repetitive.

A method for dynamic modification of audio content. An audio content theme includes audio information relating to at least one theme variation. The method includes the steps of: receiving an intensity parameter indicative of an amount of audio information being played and a variation parameter indicative of at least one theme variation of the audio information being played; modifying, based on said intensity parameter, the amount of audio information being played, and, based on said variation parameter, the at least one theme variation of the audio information being played. Also described is a computer program configured to be executed on a computer device for dynamic modification of an audio content theme.

Provided are a mobile device, a method for operating the same, and a non-transitory computer readable storage medium. The mobile device includes a touch display configured to sense a user input; a memory; and a processor configured to examine, upon receiving the user input for generating an audio file from the touch display, characteristics of the user input and characteristics of a first audio included in first audio data stored in the memory, generate melody data based on the characteristics of the user input and the characteristics of the first audio, generate a second audio based on the melody data and chord information included in the first audio data, and generate the audio file by combining the generated second audio with the first audio.

Musical attribution is performed in a two-dimensional (2D) digital representation. A piece of music representing a musical score is inputted. An abstracted representation of blanks of the score, called a digital audio canvas, is produced. Interactive, dynamic attribution is performed by a user to bring to life the musical score of abstracted blanks. Instrumentation selection, relative volume, scale selection, and score tempo are all musical attributes that are conveyed to the score of abstracted blanks. The score of the digital audio canvas is played back using the attributed blanks. The playback of the score is enabled by selecting appropriate abstracted blanks. The appropriate abstracted blanks are included among other blanks for increased educational and enjoyment value. The modified score is converted back into the format of the original inputted piece of music.

A visualization method for music-color synesthesia, belonging to the fields of cognitive psychology, color science and computer graphics, and relating to the visualization of the music synesthesia correlation of colors and the generation of a musical color combination scheme. In the method, a musical scale system is presented in the form of a color scale system, and the musical elements are presented through the color correlation to realize the synesthetic conversion between colors and musical elements, and on this basis, a color graphical conversion of the musical structure is performed. The implementations include: a conversion from colors to musical scales, a conversion from colors to chords, a conversion from colors to rhythm and a conversion from colors to texture etc.