The present invention discloses a silent digital practice horn, useable with or without a mouthpiece, characterized by a processor and communicating means enabling wireless connection with DAW application via MIDI-USB or Bluetooth modules, and thereby connection with computers or smartphones, speakers or headphones thereof. The invention also discloses a digital practice shortened either Saxophone-like or Clarinet-like horn, wherein one or more of the following is held true: octave key is configured to change range; High E key (Eb-like horn) is configured to shift tonal range up; C key is configured to shift to normal tonal range; Bb key (Bb-like horn) is configured to shift tonal range down; at least one of the following, F key, E/F♯ key and D key is configured to facilitate or change MIDI Channel; Additional F♯ key is configured to power the horn Off; Eb/D3 Key and low C key, when pressed together, enters the processor to a “Command Mode”. The present invention further discloses a practice horn that comprises a siphon which causes saliva and moisture to run down a pipe inside the instrument and to drip out of a “Moisture Outlet” in a bottom cap located at the distal most portion of the horn.

Provided are a control device, a control method and a control system. In a control system, a headphone and a pedal device are in communication with each other through first communication, and the pedal device and a portable terminal are in communication with each other through second communication. Accordingly, even when the headphone can be connected to only one control device, a control instruction from the pedal device can be transmitted to the headphone through the first communication, and a control instruction from the portable terminal can be transmitted to the headphone through the second communication and the first communication.

The present disclosure describes techniques for identifying music attributes. The described techniques comprises receiving audio data of a piece of music; determining at least one attribute of the piece of music based on the audio data of the piece of music using a model; the model comprising a convolutional neural network and a transformer; the model being pre-trained using training data, wherein the training data comprise labelled data associated with a first plurality of music samples and unlabelled data associated with a second plurality of music samples, the labelled data comprise audio data of the first plurality of music samples and label information indicative of attributes of the first plurality of music samples, and the unlabelled data comprise audio data of the second plurality of music samples.

A system for platform-independent visualization of audio content, in particular audio tracks utilizing a central computer system in communication with user devices via a computer network. The central system utilizes various algorithms to identify spoken content from audio tracks and selects visual assets associated with the identified content. Thereafter, a visualized audio track is available for users to listen and view. Audio tracks, for example Podcasts, may be segmented into topical audio segments based upon themes or topics, with segments from disparate podcasts combined into a single listening experience, based upon certain criteria, e.g., topics, themes, keywords, and the like.

Systems, methods, architectures, mechanisms and apparatus for generating an audio segment playlist configured to provoke a physiological response in a listener in accordance with a desired outcome category, comprising: selecting, from a features database, a plurality of audio segments having features associated with both listener information and the desired outcome category; and ordering within the playlist at least a portion of the selected audio segments in accordance with at least one feature progression associated with the outcome category.

A method and system for teaching oneself to learn to play a string instrument and master it by analyzing ones real-time hand/finger movement/technique/form, enabling oneself to progress and correct ones own mistakes simultaneously. In one aspect, a system for learning to play a string instrument is provided that includes a simulation instrument that includes a plurality of strings, wherein at least one of the strings includes at least one-touch sensing sensor thereon in communication with at least one processor to receive a signal from the at least one touch-sensing sensor and determine therefrom when and where on the at least one string a user applies pressure to the at least one string.

Provided are an electronic percussion instrument and a hit detection method. An overhang portion constituting the outer edge part of an edge frame is positioned further radially outward than the outer edge of a bow frame. Consequently, when the overhang portion is hit, the vibration resulting from the hit is detected by an edge sensor attached to the edge frame. In this way, it is possible to detect whether the overhang portion (the edge portion of an electronic cymbal has been hit by means of vibration detection by the edge sensor instead of by means of pressure detection by a sheet sensor.

Provided are an electronic percussion instrument and a hit detection method. An overhang portion constituting the outer edge part of an edge frame is positioned further radially outward than the outer edge of a bow frame. Consequently, when the overhang portion is hit, the vibration resulting from the hit is detected by an edge sensor attached to the edge frame. In this way, it is possible to detect whether the overhang portion (the edge portion of an electronic cymbal has been hit by means of vibration detection by the edge sensor instead of by means of pressure detection by a sheet sensor.

A method for generating a musical composition based on user input is described. A first set of musical characteristics from a first input musical piece is received as an input vector. The first set of musical characteristics is perturbed to create a perturbed input vector as input in a first set of nodes in a first visible layer of an unsupervised neural net. The unsupervised neural net comprised of a plurality of computing layers, each computing layer composed of a respective set of nodes. The unsupervised neural net is operated to calculate an output vector from a higher level hidden layer in the unsupervised neural net. The output vector is used to create an output musical piece.

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.