The present disclosure relates to a system and a processing method in association with the system for customizing audio experience. Customization of audio experience can be based on derivation of at least one customized audio response characteristic which can be applied to an audio device used by a person. The customized audio response characteristic(s) can be unique to the person.

A device may be configured to determine whether an audio file is a first type of audio file that is capable of being processed to recognize the voice query based on a characteristic of the audio file itself or a second type of audio file that may require speech recognition processing in order to recognize the voice query associated with the audio file. In determining whether the audio file is a first type of audio file or a second type of audio file, a query filter associated with the device may be configured to access one or more guidance queries. Using the one or more guidance queries, the device may classify the audio file as a first type of audio file or a second type of audio file based on receiving only a portion of the audio file, thereby improving the speed at which the audio file can be processed.

Systems, methods, and apparatuses for generating and using machine learning models using genetic data. A set of input features for training the machine learning model can be identified and used to train the model based on training samples, e.g., for which one or more labels are known. As examples, the input features can include aligned variables (e.g., derived from sequences aligned to a population level or individual references) and/or non-aligned variables (e.g., sequence content). The features can be classified into different groups based on the underlying genetic data or intermediate values resulting from a processing of the underlying genetic data. Features can be selected from a feature space for creating a feature vector for training a model. The selection and creation of feature vectors can be performed iteratively to train many models as part of a search for optimal features and an optimal model.

Introduced here are approaches to authenticating the identity of speakers based on the “liveness” of the input. To prevent spoofing, an authentication platform may establish the likelihood that a voice sample represents a recording of word(s) uttered by a speaker whose identity is to be authenticated and then, based on the likelihood, determine whether to authenticate the speaker.

A method and related hardware for improved search engine results delivered to multiple devices associated with a same user is provided. The multiple devices may each have a user profile associated therewith and the search results delivered may be based on the user profile.

Methods and systems for processing audio signals containing speech data are disclosed. Biometric data associated with at least one speaker are extracted from an audio input. A correspondence is determined between the extracted biometric data and stored biometric data associated with a consenting user profile, where a consenting user profile is a user profile indicates consent to store biometric data. If no correspondence is determined, the speech data is discarded, optionally after having been processed.

Systems and methods for delivering exercise content to a user of an exercise apparatus include a database storing exercise content, data associated with a content provider and data associated with an application provider, and a platform server operable to facilitate delivery of the exercise content to the exercise apparatus. The platform server delivers for display on the exercise apparatus archived exercise classes for selection by the user, receives from the exercise apparatus a selection, transmits data representing content of the selected archived exercise class, receives performance data from the exercise apparatus based on activity by the user, and generates archived performance data representing archived user performance parameters for a plurality of other users over at least the portion of the selected archived exercise class. Data is synchronized and updated for display by the exercise apparatus to simulate the user competing with at least some of the other users.

Systems and methods for identifying the most impactful moments or segments of music, which are those most likely to elicit a chills effect in a human listener. A digital music signal is processed using two or more objective processing metrics that measure acoustic features known to be able to elicit the chills effect. Individual detection events are identified in the output of each metric based on the output being above or below thresholds relative to the overall output. A combination algorithm aggregates concurrent detection events to generate a continuous concurrence data set of the number of concurrent detection events during the music signal, which can be calculated per beat. A phrase detection algorithm can identify impactful segments of the music based on at least one of peaks, peak-proximity, and a moving average of the continuous concurrence data.

The present disclosure relates to an information processing apparatus, an information processing method, and a program enabling to present a feature serving as a reason and a basis of being searched, of a music piece searched in response to a request of a user. A music piece is searched on the basis of a search request of a user, and information is generated, as a response to the search request, in which a feature that is of the searched music piece and corresponds to the search request is expressed by a balloon size for each type of the feature. The present invention can be applied to a music piece agent device.

Systems, methods, and apparatuses for generating and using machine learning models using genetic data. A set of input features for training the machine learning model can be identified and used to train the model based on training samples, e.g., for which one or more labels are known. As examples, the input features can include aligned variables (e.g., derived from sequences aligned to a population level or individual references) and/or non-aligned variables (e.g., sequence content). The features can be classified into different groups based on the underlying genetic data or intermediate values resulting from a processing of the underlying genetic data. Features can be selected from a feature space for creating a feature vector for training a model. The selection and creation of feature vectors can be performed iteratively to train many models as part of a search for optimal features and an optimal model.