According to one embodiment, a speech imagery recognition device is configured to recognize speech from electroencephalogram (EEG) signals during speech imagery. The speech imagery recognition device comprises an analysis processor and an extractor. The analysis processor is configured to analyze discrete signals, which are obtained from EEG signals received from a plurality of electrodes, for each of the electrodes and output a spectral time sequence. The extractor is configured to obtain eigenvectors for each phoneme from the spectral time sequence and output a phoneme-feature vector time sequence based on the eigenvectors.

A display device includes a processor, a memory, and a monitor. The processor is configured to display a signal waveform of voice data on the monitor and then receive a designation operation of a designated section designated by a user on the voice data, determine one or more target sections in the designated section, generate a screen in which a frame line indicating each of the one or more determined target sections is superimposed on the signal waveform, and output the screen to the monitor.

A display device includes a processor, a memory, and a monitor. The processor is configured to display a signal waveform of voice data on the monitor and then receive a designation operation of a designated section designated by a user on the voice data, determine one or more target sections in the designated section, generate a screen in which a frame line indicating each of the one or more determined target sections is superimposed on the signal waveform, and output the screen to the monitor.

This application relates to a device and a method for voice-based trauma screening using deep learning. The device and method for voice-based trauma screening using deep learning screen for trauma through voices that may be obtained in a non-contact manner without limitations of space or situation. In one aspect, the device includes a memory configured to store at least one program and a processor configured to perform an operation by executing the at least one program. The processor can obtain voice data, pre-process the voice data, convert pre-processed voice data into image data, and input the image data to a deep learning model and obtain a trauma result value as an output value of the deep learning model.

This application relates to a device and a method for voice-based trauma screening using deep learning. The device and method for voice-based trauma screening using deep learning screen for trauma through voices that may be obtained in a non-contact manner without limitations of space or situation. In one aspect, the device includes a memory configured to store at least one program and a processor configured to perform an operation by executing the at least one program. The processor can obtain voice data, pre-process the voice data, convert pre-processed voice data into image data, and input the image data to a deep learning model and obtain a trauma result value as an output value of the deep learning model.

Methods, systems and apparatuses for computer-generated visualization of speech are described herein. An example method of computer-generated visualization of speech including at least one segment includes: generating a graphical representation of an object corresponding to a segment of the speech; and displaying the graphical representation of the object on a screen of a computing device. Generating the graphical representation includes: representing a duration of the respective segment by a length of the object and representing intensity of the respective segment by a width of the object; and placing, in the graphical representation, a space between adjacent objects.

Methods, systems and apparatuses for computer-generated visualization of speech are described herein. An example method of computer-generated visualization of speech including at least one segment includes: generating a graphical representation of an object corresponding to a segment of the speech; and displaying the graphical representation of the object on a screen of a computing device. Generating the graphical representation includes: representing a duration of the respective segment by a length of the object and representing intensity of the respective segment by a width of the object; and placing, in the graphical representation, a space between adjacent objects.

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.

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.