An electronic-drum module for connection to one or more electronic-drum pads is provided. The module includes an electronic display; a first memory storing audio files for playback when the playback is triggered by a signal received from a pad; and one or more processors coupled to the display and the memory. The processors are configured receive an instruction to transfer a set of samples. The set of samples is associated with a priority-instruction and includes a first subset of samples and a second subset of samples. The processors are also configures to transfer the first subset of samples from a second memory to the first memory based on the priority-instruction before transferring the second subset of samples and to transfer the second subset of samples from the second memory to the first memory.

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 driving sound library provides driving sounds classified in various themes to a user by analyzing frequency characteristics and temporal characteristics of sound sources classified into categories and determining a chord corresponding to each sound source based on the frequency characteristics and the time characteristics. Modulated sound sources in which each sound source is modulated are generated by applying a chord corresponding to each sound source. The driving sound sources are generated using the sound sources and the modulated sound sources as input data. Pitches of the driving sound sources are then changed based on the driving sound sources and engine sound orders corresponding to an engine RPM of a preset range. Scores for each theme for the driving sounds are generated.

A method includes displaying a user interface of a digital audio workstation, which includes a first region for generating a composition. The first region includes a first compositional segment that has been added to the composition by a user. Based on the first compositional segment, one or more recommended predefined compositional segments are identified and displayed in a second region. The method includes receiving the selection of a second compositional segment. The method includes adding the compositional segment to the composition.

According to an embodiment, there is provided a system and method for automatic AI-based song construction based on ideas of a user. It provides and benefits from a combination of expert knowledge resident in an expert engine which contains rules for a musically correct song generation and machine learning in an AI-based audio loop selection engine for the selection of fitting audio loops from a database of audio loops.

A small (smartphone-sized or tablet-sized), foot-enabled, flat, tiltable, rotatable, dynamic touch screen pedal and controller that uses a tilt mechanism to toggle between and select different audio (or other) functions and effects that are displayed on the attached display. The tilt of the device as well as optional tapping sequences activate different functions in predetermined function locations. Specific audio effects, audio effect presets, loops, songs, and controller functions can each be assigned to different touch screen locations, pedal buttons, and/or tilt directions.

The present invention relates to a method for processing and playing audio data comprising the steps of receiving mixed input data and playing recombined output data. Furthermore, the invention relates to a device 10 for processing and playing audio data, preferably DJ equipment, comprising an audio input unit for receiving a mixed input signal, a recombination unit 32 and a playing unit 34 for playing recombined output data. In addition, the present invention relates to a method and a device for representing audio data, i.e. on a display.

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.

The present invention provides a method for processing audio data, comprising the steps of providing input audio data containing a mixture of audio data including first audio data of a first musical timbre and second audio data of a second musical timbre different from said first musical timbre, decomposing the input audio data to provide decomposed data representative of the first audio data, transforming the decomposed data to obtain third audio data.

Devices, systems, and methods related to implementing supervised metric learning during a training of a deep neural network model are disclosed herein. In examples, audio input may be received, where the audio input includes a plurality of song fragments from a plurality of songs. For each song fragment, an aligning function may be performed to center the song fragment based on determined beat information, thereby creating a plurality of aligned song fragments. For each song fragment of the plurality of song fragments, an embedding vector may be obtained from the deep neural network. Thus, a batch of aligned song fragments from the plurality of aligned song fragments may be selected, such that a training tuple may be selected. A loss metric may be generated based on the selected training tuple and one or more weights of the deep neural network model may be updated based on the loss metric.