An apparatus may be designed to enable a user to receive, record, display, edit, arrange, re-arrange, play, loop, extend, export and import audio and video data. The audio and video data to be organized as, for example, but not limited to, a song comprised of song parts. The song parts may be comprised of tracks, and each track may be comprised of one or more layers. The various methods and systems disclosed herein incorporate such data segmentation to enable the user to intuitively and hands-free record, arrange, and perform songs comprised of both sequential and parallel tracks. In this way, the looper may enable a musician to record and loop tracks for a song, arrange the tracks into song parts, and during the same session, transition the playback from one song part to another, all the while recording a track on top of the transitioning song parts.

A system for generating a signal includes a touchpad including touch cells and a touch detection device for detecting the location and intensity of a pressure exerted on the touchpad; a first computer generating a first instruction based on the location and intensity of the pressure; an optical detection device for detecting a movement and/or a position, including optics for capturing images; a second computer for determining a motion parameter based on the captured images and for generating a second instruction based on the parameter; and a signal generator for producing a second signal based on the first instruction or on a first signal extracted from the first instruction, to which there is applied a special effect extracted from the second instruction; or on the second instruction or on a first signal extracted from the second instruction, to which there is applied a special effect extracted from the first instruction.

Provided is a signal processing apparatus having: a sound source separation unit configured to perform sound source separation on a mixed sound signal obtained by mixing a plurality of sound source signals; a sound source type determination unit configured to determine a type of a predetermined sound source signal obtained by the sound source separation; and an output destination control unit configured to output the predetermined sound source signal to a corresponding output device on the basis of a determination result of the sound source type determination unit.

A parameter inference method realized by a computer, includes obtaining target performance information indicating a performance of music using an electronic musical instrument; inferring assist information from the target performance information with use of a trained inference model generated through machine learning, the assist information being related to setting of a parameter of the electronic musical instrument that conforms to a tendency of the performance; and outputting the inferred assist information related to the setting of the parameter.

A machine learning method for training a learning model includes: transforming a first audio type of audio data into a first image type of image data, wherein a first audio component and a second audio component are mixed in the first audio type of audio data, and the first image type of image data corresponds to the first audio type of audio data; transforming a second audio type of audio data into a second image type of image data, wherein the second audio type of audio data includes the first audio component without mixture of the second audio component, and the second image type of image data corresponds to the second audio type of audio data; and performing machine learning on the learning model with training data including sets of the first image type of image data and the second image type of image data.

A system, method and computer product for training a neural network system. The method comprises inputting an audio signal to the system to generate plural outputs f(X, Θ). The audio signal includes one or more of vocal content and/or musical instrument content, and each output f(X, Θ) corresponds to a respective one of the different content types. The method also comprises comparing individual outputs f(X, Θ) of the neural network system to corresponding target signals. For each compared output f(X, Θ), at least one parameter of the system is adjusted to reduce a result of the comparing performed for the output f(X, Θ), to train the system to estimate the different content types. In one example embodiment, the system comprises a U-Net architecture. After training, the system can estimate various different types of vocal and/or instrument components of an audio signal, depending on which type of component(s) the system is trained to estimate.

A system and method for media content sequencing. Prior tracks for a listening session are segmented into groups based on attribute scores for an audial attribute. A preferred group is then selected, which can be based on user feedback regarding the prior tracks in the listening session. Candidate tracks, such as from a candidate track pool for future playback in the listening session, are also segmented into the groups of the prior tracks. The candidate tracks can then be ranked based on their associated group and the preferred group.

The electronic wind instrument (1) includes an instrument body (10) in which a through-hole leading into an internal space (11) opens on an outer surface (12), an operation piece (20) attached to the instrument body (10) at a position of a through-hole (14) and pushed down toward the internal space (11), and an electronic component disposed in the internal space (11). The electronic component includes a push-down sensor (6) that detects that the operation piece (20) is being pushed down, and the outer surface (12) of the instrument body (10) comprises an outer wall (16a) that is provided around the through-hole (14) and that faces toward the through-hole (14), an inner wall (16b) that is provided nearer to the through-hole (14) than the outer wall (16a) and that faces toward the outer wall (16a), and a groove bottom (16c) connecting the outer wall (16a) and the inner wall (16b) together.

A method and apparatus for implementing a virtual performance partner are provided. The method includes collecting audio frame data performed by a performer; and for each piece of current audio frame data collected, performing: converting the piece of current audio frame data collected into a current digital score, matching the current digital score with a range of digital scores in a repertoire, and determining a matching digital score in the range of digital scores that matches the current digital score; positioning a position of the matching digital score in the repertoire, and determining a start time of playing a cooperation part of music in a next bar of the matching digital score in the repertoire for a performance partner.

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.