A method of generating actions following the rhythm of music includes the steps of (a) using an electronic device to classify the sound signals from plural musical instruments into a complex pitch range, (b) the electronic device performing Fourier series conversion on the sound signals of the complex pitch range to obtain plural rhythm diagrams, (c) the electronic device performing a rhythm change point capture action on each rhythm diagram, and then executing no action if the intensity in the rhythm diagram continues to increase with time, or regarding the point as a rhythm change point if the intensity changes from an increase to a decrease, and then transmitting an action signal to an action device, and (c) the action device executing the corresponding action according to the action signal.

Method of generating actions following the rhythm of music includes the steps of (a) using an electronic device to classify the sound signals from plural musical instruments into a complex pitch range, (b) the electronic device selecting at least one pitch range according to the significance of the sound, and then performing Fourier series conversion on the sound signals of the selected said at least one pitch range to obtain at least one rhythm diagram. (c) the electronic device performing a rhythm change point capture action on the rhythm diagram, and then executing no action if the intensity in the rhythm diagram continues to increase with time, or regarding the point as a rhythm change point if the intensity changes from an increase to a decrease, and then transmitting an action signal to an action device, and (c) the action device executing the corresponding action according to the action signal.

A mobile device responds in real time to media content presented on a media device, such as a television. The mobile device captures temporal fragments of audio-video content on its microphone, camera, or both and generates corresponding audio-video query fingerprints. The query fingerprints are transmitted to a search server located remotely or used with a search function on the mobile device for content search and identification. Audio features are extracted and audio signal global onset detection is used for input audio frame alignment. Additional audio feature signatures are generated from local audio frame onsets, audio frame frequency domain entropy, and maximum change in the spectral coefficients. Video frames are analyzed to find a television screen in the frames, and a detected active television quadrilateral is used to generate video fingerprints to be combined with audio fingerprints for more reliable content identification.

A software plug-in module that interfaces to a media editing host application generates and embeds information about a media composition being edited directly within portions of the user interface generated by the host application. The information may include a custom representation of media data of a time-based element of the media composition that replaces, augments, or overlays a timeline representation of the element generated by the host application. Media editing functionality provided by the plug-in may be accessed by an operator based on viewing or interacting with the custom representation. Results of analysis of the media composition by the plug-in may be displayed within the host-generated timeline and used by an operator as a basis for performing edit operations with standard host tools or with plug-in generated tools. Plug-ins may embed their interfaces within user interfaces of host digital audio workstations, non-linear video editing systems, and music notation applications.

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.

A mobile device responds in real time to media content presented on a media device, such as a television. The mobile device captures temporal fragments of audio-video content on its microphone, camera, or both and generates corresponding audio-video query fingerprints. The query fingerprints are transmitted to a search server located remotely or used with a search function on the mobile device for content search and identification. Audio features are extracted and audio signal global onset detection is used for input audio frame alignment. Additional audio feature signatures are generated from local audio frame onsets, audio frame frequency domain entropy, and maximum change in the spectral coefficients. Video frames are analyzed to find a television screen in the frames, and a detected active television quadrilateral is used to generate video fingerprints to be combined with audio fingerprints for more reliable content identification.

Music instrument and information digital mute system, for capture, processing, and conversion of sound from analog to digital signals using central processing unit (CPU) microcontroller, Bluetooth and Wi-Fi microcontrollers, sound image localization filter, global positioning and geographic information systems, universal serial bus (USB) module, and battery. Mute body is positioned in close proximity to bell or horn, body and/or voice at proximal end and/or configured to occlude sound source. Acoustically designed inner chamber within mute body captures acoustical variations of air pressure. Microphone positioned at distal end of mute, and proximal end of CB position in mute with digital signal processor (DSP) that captures, processes, converts, and transmits digital sound and data. CPU manages and controls components and modules of CB. Bluetooth and Wi-Fi microcontrollers configured to receive and send signals to and from other technological devices, components, and systems (cellphones, tablets, computers, earplugs, smart televisions, server, and cloud platforms). USB module configured to receive and send digital signals, supply power to CB, and charge and recharge battery.

A waveform analysis method utilizes a convolutional neural network (CNN) to differentiate waveform data based on applying one or more pre-trained weights to pre-processed spectrograms sampled uniformly from the waveform data. The CNN outputs, in real-time, the level of confidence that any number of potential custom tags accurately describe the sampled waveform data. These outputs can vary across a single musical track. When these outputs are packaged with the track's metadata, a comprehensive searchable database can be formed which provides quantifiable means of differentiating qualitative features of music.

A sound signal synthesis method includes generating first data representing a deterministic component of a sound signal based on second control data representing conditions of the sound signal, generating, using a first generation model, second data representing a stochastic component of the sound signal based on the first data and first control data representing conditions of the sound signal, and combining the deterministic component represented by the first data and the stochastic component represented by the second data and thereby generating the sound signal.

A computer-implemented sound signal synthesis method generates control data including pitch notation data indicative of a pitch name of a pitch of a sound signal to be synthesized and octave data indicative of an octave of the pitch of the sound signal to be synthesized; and estimates output data indicative of the sound signal to be synthesized by inputting the generated control data into a generative model that has learned a relationship between training control data including training pitch notation data indicative of a pitch name of a pitch of a reference signal and training octave data indicative of an octave of the pitch of the reference signal; and training output data indicative of the reference signal.