A person can use a portable electronic device to electronically purchase or otherwise request a product, service or other deliverable related to audio programming to which the person is listening at the time they initiate the request. The request is fulfilled by a service that analyzes the audio content to identify the deliverable the person desires.

A person can use a portable electronic device to electronically purchase or otherwise request a product, service or other deliverable related to audio programming to which the person is listening at the time they initiate the request. The request is fulfilled by a service that analyzes the audio content to identify the deliverable the person desires.

Example systems, apparatus, and methods receive audio information including a plurality of frames from a source device, wherein each frame of the plurality of frames includes one or more audio samples and a time stamp indicating when to play the one or more audio samples of the respective frame. In an example, the time stamp is updated for each of the plurality of frames using a time differential value determined between clock information received from the source device and clock information associated with the device. The updated time stamp is stored for each of the plurality of frames, and the audio information is output based on the plurality of frames and associated updated time stamps. A number of samples per frame to be output is adjusted based on a comparison between the updated time stamp for the frame and a predicted time value for play back of the frame.

Example systems, apparatus, and methods receive audio information including a plurality of frames from a source device, wherein each frame of the plurality of frames includes one or more audio samples and a time stamp indicating when to play the one or more audio samples of the respective frame. In an example, the time stamp is updated for each of the plurality of frames using a time differential value determined between clock information received from the source device and clock information associated with the device. The updated time stamp is stored for each of the plurality of frames, and the audio information is output based on the plurality of frames and associated updated time stamps. A number of samples per frame to be output is adjusted based on a comparison between the updated time stamp for the frame and a predicted time value for play back of the frame.

Examples described herein relate to music discovery. In one aspect, a method is provided that involves (a) receiving by a computing device an indication of a search tool from among a plurality of search tools, where each search tool of the plurality of search tools is associated with at least one respective media service, (b) receiving by the computing device an indication of a media characteristic, where the computing device receives the media characteristic via the indicated search tool, (c) selecting by the computing device one or more of the at least one respective media service that maintains media associated with the indicated media characteristic, and (d) sending by the computing device an indication of the selected one or more of the at least one respective media service.

A method for audio track data retrieval, a method for identifying an audio clip, and a mobile device are provided. In the method for audio track data retrieval, audio track data corresponding to at least a portion of an audio track is obtained, the audio track data is transformed from a first domain into a second domain based on a transform function to generate a representation of the audio track data in the second domain over the time frame, multiple peak values in multiple portions of the representation are detected, multiple identifiers are extracted from the representation based on the multiple peak values, each of the multiple identifiers is hashed with a hash function to produce a hash value for each identifier, and each hash value is associated with one of multiple buckets that share a common feature with the hash value.

A method for audio track data retrieval, a method for identifying an audio clip, and a mobile device are provided. In the method for audio track data retrieval, audio track data corresponding to at least a portion of an audio track is obtained, the audio track data is transformed from a first domain into a second domain based on a transform function to generate a representation of the audio track data in the second domain over the time frame, multiple peak values in multiple portions of the representation are detected, multiple identifiers are extracted from the representation based on the multiple peak values, each of the multiple identifiers is hashed with a hash function to produce a hash value for each identifier, and each hash value is associated with one of multiple buckets that share a common feature with the hash value.

Systems and methods are provided for determining a response to a query in a dialog. An entity extractor extracts rules and conditions associated with the query and determines a particular task. The disclosed technology generates a transformer-based dialog embedding by pre-training a transformer using dialog corpora including a plurality of tasks. A task-specific classifier generates a first set of candidate responses based on rules and conditions associated with the task. The transformer-based dialog embedding generates a second set of candidate responses to the query. The classifier accommodates changes made to a task by an interactive dialog editor as machine teaching. A response generator generates a response based on the first and second sets of candidate responses using an optimization function. The disclosed technology leverages both a data-driven, generative model (a transformer) based on dialog corpora and a user-driven, task-specific rule-based classifier that accommodating updates in rules and conditions associated with a particular task.

Systems and methods are provided for determining a response to a query in a dialog. An entity extractor extracts rules and conditions associated with the query and determines a particular task. The disclosed technology generates a transformer-based dialog embedding by pre-training a transformer using dialog corpora including a plurality of tasks. A task-specific classifier generates a first set of candidate responses based on rules and conditions associated with the task. The transformer-based dialog embedding generates a second set of candidate responses to the query. The classifier accommodates changes made to a task by an interactive dialog editor as machine teaching. A response generator generates a response based on the first and second sets of candidate responses using an optimization function. The disclosed technology leverages both a data-driven, generative model (a transformer) based on dialog corpora and a user-driven, task-specific rule-based classifier that accommodating updates in rules and conditions associated with a particular task.

A system and a method for recommending background music that corresponds to an extracted text from a book based on emotion and a topic that is relevant to the extracted text using machine learning models provided. The method includes, (i) determining, using a first trained machine learning model, the emotion from the extracted text that corresponds to the paragraph of the book, (ii) assigning, using a word similarity technique, a similarity score for emotion-words based on the emotion, (iii) determining the emotion-words that exceed a threshold to obtain a subset of emotion-words, (iv) determining a query using the subset of the emotion-words and the emotion, (v) retrieving, using the query, songs that match any of words in the query, and (vi) recommending background music based on top-ranked songs for the extracted text from the book.