An effect adding apparatus includes: at least one first operation element on which a first user operation is performed; a plurality of second operation elements on which a second user operation is performed after the first user operation; and at least one processor, in which the at least one processor determines two or more effects including at least a first effect and a second effect, from a plurality of effects in which each of the effects is associated with a plurality of parameters, based on the first user operation on the at least one first operation element, and determines a parameter associated with each of the plurality of second operation elements, based on data indicating significance of each of a plurality of first parameters associated with the first effect determined and data indicating significance of each of a plurality of second parameters associated with the second effect determined.

An accessory for modifying sound output of a musical instrument. The body of the instrument has a soundboard. The accessory includes a sound sensor, an actuator, a fastener, and a controller. The sound sensor engages the body and senses vibration of the body representing the sound output of the musical instrument. The actuator engages the soundboard and deforms the soundboard of the musical instrument so as to modify the sound output of the musical instrument. The sound sensor is preferably arranged distally to the actuator. The fastener engages the accessory to the musical instrument, to locate the actuator against the soundboard of the musical instrument. The controller is connected to the actuator and the sound sensor for receiving and analysing the sound output sensed by the sound sensor, and controlling the actuator in dependence on the sound output sensed by the sound sensor.

A wireless controller that allows a musician to wirelessly switch between audio effect presets, loops, and/or songs which are stored in a specialized audio/instrument cable or adapter. The wireless controller can be located in the same place as (or incorporated into) a digital guitar tuner on a guitar headstock allowing the musician to operate the wireless controller in a familiar location while switching between audio effect presets, loops, and/or songs by tapping on small buttons.

The sound signal processing method includes obtaining a sound signal of a sound source, convolving, according to a location of the sound source an impulse response of an early reflection sound with the sound signal to generate an early reflection sound control signal that reproduces an early reflection sound, and convolving an impulse response of a reverberant sound with the sound signal to generate a reverberant sound control signal that reproduces a reverberant sound.

Provided is a method for processing audio including: acquiring an accompaniment audio signal and a voice signal of a current to-be-processed musical composition; determining a target reverberation intensity parameter value of the acquired accompaniment audio signal, wherein the target reverberation intensity parameter value is configured to indicate a rhythm speed, an accompaniment type, and a performance score of a singer of the current to-be-processed musical composition; and reverberating the acquired vocal signal based on the target reverberation intensity parameter value.

Preparing a sum of complex exponentials in the form of a modal filter to approximate a given impulse response is considered, and a non-iterative solution is described. In one embodiment, the mode count, and the mode frequencies, dampings, and amplitudes are estimated using the generalized eigenvalues of Hankel matrices of samples of the given impulse response.

The disclosed computer-implemented method may include obtaining an audio sample from a content source, inputting the obtained audio sample into a trained machine learning model, obtaining the output of the trained machine learning model, wherein the output is a profile of an environment in which the input audio sample was recorded, obtaining an acoustic impulse response corresponding to the profile of the environment in which the input audio sample was recorded, obtaining a second audio sample, processing the obtained acoustic impulse response with the second audio sample, and inserting a result of processing the obtained acoustic impulse response and the second audio sample into an audio track. Various other methods, systems, and computer-readable media are also disclosed.

A specialized audio/instrument cable adapter with built-in digital signal processing capabilities that adds user-defined audio effects (such as reverb, delay, chorus and/or distortion) from within the cable adapter itself to affect the sound generated from an instrument or microphone such that the cable adapter (with a instrument/microphone cable) is the only connection needed between the instrument or microphone and an output device (such as an amplifier, PA, powered speaker, music mixer, or a recording device). The audio effects used by the cable adapter can be changed via (i) an app from a smartphone, tablet, computer or other electronic device; (ii) a wireless controller that attaches to the instrument; (iii) a pedal, and/or (iv) any other type of wireless controller that has the ability to communicate with a smartphone/tablet/computer or other electronic device.

A device is provided for capturing vibrations produced by an object such as a musical instrument such as a cymbal of a drum kit. The device comprises a detectable element, such as a ferromagnetic element, such as a metal shim and a sensor spaced apart from and located relative to the musical instrument. The detectable element is located between the sensor and the musical instrument. When the musical instrument vibrates, the sensor remains stationary and the detectable element is vibrated relative to the sensor by the musical instrument.

The disclosed computer-implemented method may include obtaining an audio sample from a content source, inputting the obtained audio sample into a trained machine learning model, obtaining the output of the trained machine learning model, wherein the output is a profile of an environment in which the input audio sample was recorded, obtaining an acoustic impulse response corresponding to the profile of the environment in which the input audio sample was recorded, obtaining a second audio sample, processing the obtained acoustic impulse response with the second audio sample, and inserting a result of processing the obtained acoustic impulse response and the second audio sample into an audio track. Various other methods, systems, and computer-readable media are also disclosed.