A custom artificial intelligence (AI) data categorization system and method is described for gathering and categorizing data that would overstimulate people with autism. Overstimulation is to be determined by our end-users' preferences. Our end-users will listen to a set of audio files and categorize them with: “Calm”, “Anxious”, or “Overstimulated”. The datasets presented to the end-user are randomly selected from data clusters that represent audio files with similar sounds based off a select set of attributes. Upon categorization, the selected set of attributes will be saved in a directory with its categorization saved in a database.

A custom artificial intelligence (AI) data categorization system and method is described for gathering and categorizing data that would overstimulate people with autism. Overstimulation is to be determined by our end-users' preferences. Our end-users will listen to a set of audio files and categorize them with: “Calm”, “Anxious”, or “Overstimulated”. The datasets presented to the end-user are randomly selected from data clusters that represent audio files with similar sounds based off a select set of attributes. Upon categorization, the selected set of attributes will be saved in a directory with its categorization saved in a database.

Aspects of the present disclosure are directed to methods and apparatuses involving characterization via homologizing disparate speech terminology. As may be implemented in accordance with one or more embodiments, audio processing circuitry is utilized to identify a respective language used for audio data sets. Homologizing circuitry is operable to homologize terms in the audio data sets for characterizing animals to which respective ones of the audio data sets are linked, by assessing and assigning terms in the respective audio data sets to respective homologized meanings based on the identified language for the audio data sets and an association between terms in the identified language for each audio data, set and the homologized meaning. The homologized meanings may be in association with one of the animals to which the audio data set is linked, therein facilitating common characterizations of the animals utilizing disparate languages and terms.

A method and system are provided for extracting features from digital audio signals which exhibit variations in pitch, timbre, decay, reverberation, and other psychoacoustic attributes and learning, from the extracted features, an artificial neural network model for generating contextual latent-space representations of digital audio signals. A method and system are also provided for learning an artificial neural network model for generating consistent latent-space representations of digital audio signals in which the generated latent-space representations are comparable for the purposes of determining psychoacoustic similarity between digital audio signals. A method and system are also provided for extracting features from digital audio signals and learning, from the extracted features, an artificial neural network model for generating latent-space representations of digital audio signals which take care of selecting salient attributes of the signals that represent psychoacoustic differences between the signals.

A method and system are provided for extracting features from digital audio signals which exhibit variations in pitch, timbre, decay, reverberation, and other psychoacoustic attributes and learning, from the extracted features, an artificial neural network model for generating contextual latent-space representations of digital audio signals. A method and system are also provided for learning an artificial neural network model for generating consistent latent-space representations of digital audio signals in which the generated latent-space representations are comparable for the purposes of determining psychoacoustic similarity between digital audio signals. A method and system are also provided for extracting features from digital audio signals and learning, from the extracted features, an artificial neural network model for generating latent-space representations of digital audio signals which take care of selecting salient attributes of the signals that represent psychoacoustic differences between the signals.

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.

Methods, apparatus, systems and articles of manufacture are disclosed to identify media that has been pitch shifted, time shifted, and/or resampled. An example apparatus includes: memory; instructions in the apparatus; and processor circuitry to execute the instructions to: transmit a fingerprint of an audio signal and adjusting instructions to a central facility to facilitate a query, the adjusting instructions identifying at least one of a pitch shift, a time shift, or a resample ratio; obtain a response including an identifier for the audio signal and information corresponding to how the audio signal was adjusted; and change the adjusting instructions based on the information.

Methods, apparatus, systems and articles of manufacture are disclosed to identify media that has been pitch shifted, time shifted, and/or resampled. An example apparatus includes: memory; instructions in the apparatus; and processor circuitry to execute the instructions to: transmit a fingerprint of an audio signal and adjusting instructions to a central facility to facilitate a query, the adjusting instructions identifying at least one of a pitch shift, a time shift, or a resample ratio; obtain a response including an identifier for the audio signal and information corresponding to how the audio signal was adjusted; and change the adjusting instructions based on the information.

A method of encoding audio content comprises performing a content analysis of the audio content, generating classification information indicative of a content type of the audio content based on the content analysis, encoding the audio content and the classification information in a bitstream, and outputting the bitstream. A method of decoding audio content from a bitstream including audio content and classification information for the audio content, wherein the classification information is indicative of a content classification of the audio content, comprises receiving the bitstream, decoding the audio content and the classification information, and selecting, based on the classification information, a post processing mode for performing post processing of the decoded audio content. Selecting the post processing mode can involve calculating one or more control weights for post processing of the decoded audio content based on the classification information.

A method of encoding audio content comprises performing a content analysis of the audio content, generating classification information indicative of a content type of the audio content based on the content analysis, encoding the audio content and the classification information in a bitstream, and outputting the bitstream. A method of decoding audio content from a bitstream including audio content and classification information for the audio content, wherein the classification information is indicative of a content classification of the audio content, comprises receiving the bitstream, decoding the audio content and the classification information, and selecting, based on the classification information, a post processing mode for performing post processing of the decoded audio content. Selecting the post processing mode can involve calculating one or more control weights for post processing of the decoded audio content based on the classification information.