Disclosed embodiments include systems and methods to teach and analyze a student's progress in learning to play a musical instrument, sing or perform other musical endeavors. Embodiments include the production of an AI score or music AI score which may be an extraction of performance parameters such as a student's tone, speed, rhythm, pitch loudness and other metrics. A musical AI score may also track changes in measured performance while playing a piece of music. Such changes within a piece of music or over time in performing various pieces of music can be valuable is a student's self-assessment or a music teacher's approach tailored to the particular student. A music or signing AI score can help a student to select and to prioritize their music repertoire, focus performance efforts, optimize time schedule, improve appreciation for music, improve the overall quality of music performance and instructor relationship.

An electronic device having a circuitry configured to perform audio source separation on an audio input signal to obtain a vocals signal and an accompaniment signal and to perform a confidence analysis on a user's voice signal based on the vocals signal to provide guidance to the user.

A method of making audio music selection and creating a mixtape, comprising importing song files from a song repository; sorting and filtering the song files based on selection criteria; and creating the mixtape from the song files sorting and filtering results. The sorting and filtering of the song files comprise: spectral analyzing each of the song files to extract low level acoustic feature parameters of the song file; from the low level acoustic feature parameter values, determining the high level acoustic feature parameters of the analyzed song file; determining a similarity score of each of the analyzed song files by comparing the acoustic feature parameter values of the analyzed song file against desired acoustic feature parameter values determined from the selection criteria; and sorting the analyzed song files according to their similarity scores; and filtering out the analyzed song files with first similarity scores lower than a filter threshold.

A property vector representing extractable measurable properties, such as musical properties, of a file is mapped to semantic properties for the file. This is achieved by using artificial neural networks “ANNs” in which weights and biases are trained to align a distance dissimilarity measure in property space for pairwise comparative files back towards a corresponding semantic distance dissimilarity measure in semantic space for those same files. The result is that, once optimised, the ANNs can process any file, parsed with those properties, to identify other files sharing common traits reflective of emotional-perception, thereby rendering a more liable and true-to-life result of similarity/dissimilarity. This contrasts with simply training a neural network to consider extractable measurable properties that, in isolation, do not provide a reliable contextual relationship into the real-world.

Example methods, apparatus and articles of manufacture to determine semantic information for audio are disclosed. Example apparatus disclosed herein are to process an audio signal obtained by a media device to determine values of a plurality of features that are characteristic of the audio signal, compare the values of the plurality of features to a first template having corresponding first ranges of the plurality of features to determine a first score, the first template associated with first semantic information, compare the values of the plurality of features to a second template having corresponding second ranges of the plurality of features to determine a second score, the second template associated with second semantic information, and associate the audio signal with at least one of the first semantic information or the second semantic information based on the first score and the second score.

The present disclosure provides a rhythm point detection method and apparatus and an electronic device, and relates to the technical field of music analysis. The method includes that: an audio signal to be detected is acquired, and an audio feature curve is generated according to the audio signal to be detected; a music style of the audio signal to be detected is determined; a detection peak threshold and a detection frame width threshold are determined according to the music style of the audio signal to be detected; and a rhythm point of the audio feature curve is determined according to the detection peak threshold and the detection frame width threshold.

A method and system for analyzing audio (eg. music) tracks. A predictive model of the neuro-physiological functioning and response to sounds by one or more of the human lower cortical, limbic and subcortical regions in the brain is described. Sounds are analyzed so that appropriate sounds can be selected and played to a listener in order to stimulate and/or manipulate neuro-physiological arousal in that listener. The method and system are particularly applicable to applications harnessing a biofeedback resource.

In at least one embodiment, a method of performing automatic transcription of musical content included in an audio signal received by a computing device is provided. The method includes processing, using the computing device, the received audio signal to extract musical information characterizing at least a portion of the musical content and generating, using the computing device, a plurality of musical notations representing alternative musical interpretations of the extracted musical information. The method further includes applying a selected one of the plurality of musical notations for transcribing the musical content of the received audio signal.

[Object] To propose an image processing method, image processing apparatus and program which are capable of exciting the emotions of a viewer more effectively. [Solution] An information processing method including: analyzing a beat of input music; extracting a plurality of unit images from an input image; and generating, by a processor, editing information for switching the extracted unit images depending on the analyzed beat.

System, apparatus and method for determining semantic information from audio, where incoming audio is sampled and processed to extract audio features, including temporal, spectral, harmonic and rhythmic features. The extracted audio features are compared to stored audio templates that include ranges and/or values for certain features and are tagged for specific ranges and/or values. Extracted audio features that are most similar to one or more templates from the comparison are identified according to the tagged information. The tags are used to determine the semantic audio data that includes genre, instrumentation, style, acoustical dynamics, and emotive descriptor for the audio signal.