Techniques are disclosed relating to implementing user-created controls to modify music content. A music generator system may be configured to automatically generate output music content by selecting and combining audio tracks based on various parameters. Users may create their own control elements that the music generator system may train (e.g., using AI techniques) to generate output music content according to a user's intended functionality of a user-created control element.

Techniques are disclosed relating to tracking contributions to composed music content. In some embodiments, a computer system determines playback data for a music content mix, where the playback data indicates characteristics of playback of the music content mix and the music content mix includes a determined combination of multiple audio tracks. In some embodiments, the system records, in an electronic block-chain ledger data structure, information specifying individual playback data for one or more of the multiple audio tracks in the music content mix. The information specifying individual playback data for an individual audio track may include usage data for the individual audio track and signature information associated with the individual audio track.

Techniques are disclosed relating to automatically generate new music content based on image representations of audio files. A computer system generate image representations of audio files. The image representations may be generated, for example, based on data in the audio files and MIDI representations of the audio files. Audio files for combination may then be selected based on analysis of the image representations. For example, image-based machine learning algorithms may be implemented to assess the image representations and select music for combining.

This invention eliminates most mechanical switching in vibrational pickup circuits by using variable gains to combine signals of sensors in differential amplifiers as J−1 humbucking pairs for J>1 number of sensors, with the sensors matched to produce the same level and phase of unwanted hum from external sources. It can also combine J>1 number of matched sensors with K>1 number of dissimilar sensors which are matched only to each other in the same manner. This produces not only all the possible mechanically switched humbucking signals, but all the continuously-varying combinations of humbucking signals in between.

A method of and system for generating audio having pitch attributes of an incoming audio stream. The method comprises of receiving a digital audio input. The audio data is analyzed upon receiving an analysis trigger indication which can be synced with the audio tempo. The analysis includes a Fast Fourier Transform of a segment of digital audio data.

The integrated spectrum is process to find a number of peak frequencies in the spectrum and their gain. A number of the peak frequencies are used to program a number of oscillators and the parameters determined during analysis and controllable attributes and characteristics of the oscillators. The synthesis is performed upon receiving an analysis clock. First, a number of digital oscillators are configured with the associated frequency parameters and gain parameters from the peaks array; The number of oscillators configured according to the audio pitch analysis and are generated an oscillator output at the frequency and gain specified in the peak array. These oscillator output are summed together thereby generating synthesized audio.

An effect addition device includes at least one processor that executes a time domain convolution process of convolving a first time domain data part of impulse response of sound effects with a time domain data on an original sound, a frequency domain convolution process of convoluting a second time domain data part of the impulse response data with the time domain data on the original sound, a convolution extension process of extending a convolved state(s) of an output signal(s) resulting from the time domain convolution process and/or the frequency domain convolution process by arithmetic processing which corresponds to an all-pass filter and/or arithmetic processing which corresponds to a comb filter, and a synthesized sound effect addition process of adding a sound effect which is synthesized by execution of the time domain convolution process, the frequency domain convolution process and the convolution extension process to the original sound.

Methods and apparatus to extract a pitch-independent timbre attribute from a media signal are disclosed. An example apparatus includes an audio characteristic extractor to determine a logarithmic spectrum of an audio signal; transform the logarithmic spectrum of the audio signal into a frequency domain to generate a transform output; determine a magnitude of the transform output; and determine a timbre attribute of the audio signal based on an inverse transform of the magnitude.

A method for performing melody detection comprises interpreting the global perceptual effect of all the sounds at once, to determine what is the melody actually perceived by the human ear, and providing a music sheet or a text printout including a time sequence of single notes describing that melody.

A method and system for visualizing music using a perceptually conformal mapping system are provided. A music source file is input into a processor configured to carry out a series of steps on audio cues identified within the music and ultimately generate a simultaneous visual representation on a display device. The series of steps include application of one or more perceptually conformal mapping systems that essentially induce a synesthetic experience in which a person can experience music both acoustically and visually at the same time. The device extracts cues from the music that are designed to specifically capture fundamentals of human appreciation, maps them into visual cues, then presents those visual cues synchronized with the source music.

A system, method and computer product for estimating a component of a provided audio signal. The method comprises converting the provided audio signal to an image, processing the image with a neural network trained to estimate one of vocal content and instrumental content, and storing a spectral mask output from the neural network as a result of the image being processed by the neural network. The neural network is a U-Net. The method also comprises providing the spectral mask to a client media playback device, which applies the spectral mask to a spectrogram of the provided audio signal, to provide a masked spectrogram. The media playback device also transforms the masked spectrogram to an audio signal, and plays back that audio signal via an output user interface.