Abstract: An example computer-implemented method includes selecting an audio segment from a middle section of an audio track and the computing system obtaining a frequency-component representation of a time window that spans (i) the selected audio segment and (ii) context audio before and/or after the selected audio segment. Further, the example method includes providing, to a trained machine-learning model, the frequency-component representation, the trained machine-learning model having been trained by training data that identifies cuepoints within frequency-component representations of audio segments within beginning and end sections of a plurality of training audio tracks, each cuepoint being a fade-in cuepoint or a fade-out cuepoint.

Abstract: This disclosure is directed to an enhanced audio file generator. One aspect is a method of enhancing input speech in an input audio file, the method comprising receiving the input audio file representing the input speech, wherein the input audio file is recorded at an audio recording device, and generating an enhanced audio file by applying an audio transformation model to the input audio file, wherein applying the audio transformation model to generate the enhanced audio file comprises extracting parameters defining audio features from the input audio file, the parameters including a noise parameter defining noise in the input audio file and one or more other preset parameters respectively defining other audio features, synthesizing clean speech based on the extracted parameters including the noise parameter, wherein synthesizing the clean speech comprises transforming the noise parameter to defined value(s); and generating the enhanced audio file with the synthesized clean speech.

Abstract: A system and method for data collection and aggregation using a distributed network of communications enabled sensors connected to another primary network to achieve a secondary out-of-band monitoring perspective, for example, in power grids. The data collection system includes an aggregation and processing server configured to collect data from a variety of sensors adjacent to the monitored network each sensor includes secondary power such that it can continue data transmission even during power grid outages. The data collection system includes a method for secure real-time data ingest, machine learning enabled analysis, risk assessment, and anomaly detection on a broad geographic scale irrespective of isolated network boundaries.

Abstract: A system and method for data collection and aggregation using a distributed network of communications enabled sensors connected to another primary network to achieve a secondary out-of-band monitoring perspective, for example, in power grids. The data collection system includes an aggregation and processing server configured to collect data from a variety of sensors adjacent to the monitored network each sensor includes secondary power such that it can continue data transmission even during power grid outages. The data collection system includes a method for secure real-time data ingest, machine learning enabled analysis, risk assessment, and anomaly detection on a broad geographic scale irrespective of isolated network boundaries.

Abstract: A system and method for data collection and aggregation using a distributed network of communications enabled sensors connected to another primary network to achieve a secondary out-of-band monitoring perspective, for example, in power grids. The data collection system includes an aggregation and processing server configured to collect data from a variety of sensors adjacent to the monitored network each sensor includes secondary power such that it can continue data transmission even during power grid outages. The data collection system includes a method for secure real-time data ingest, machine learning enabled analysis, risk assessment, and anomaly detection on a broad geographic scale irrespective of isolated network boundaries.

Abstract: A method includes obtaining lyrics text and audio for a media item and generating, using a first encoder, a first plurality of embeddings representing symbols that appear in the lyrics text for the media item. The method includes generating, using a second encoder, a second plurality of embeddings representing an acoustic representation of the audio for the media item. The method includes determining respective similarities between embeddings of the first plurality of embeddings and embeddings of the second plurality of embeddings and aligning the lyrics text and the audio for the media item based on the respective similarities. The method includes, while streaming the audio for the media item, providing, for display, the aligned lyrics text with the streamed audio.

Abstract: An electronic device pre-processes a target audio track, including determining, for each time interval of a plurality of time intervals of the target audio track, a multi-pitch salience. The electronic device presents the target audio track at a device associated with the user. While presenting the target audio track at the device associated with the user, the electronic device receives an audio data stream representative of a user's musical performance and scores the user's musical performance with respect to the target audio track by comparing, respectively, for each time interval of the plurality of time intervals of the target audio track, (i) a pitch of the user's musical performance represented by the audio data stream to (ii) the multi-pitch salience.

Abstract: A method includes obtaining lyrics text and audio for a media item and generating, using a first encoder, a first plurality of embeddings representing symbols that appear in the lyrics text for the media item. The method includes generating, using a second encoder, a second plurality of embeddings representing an acoustic representation of the audio for the media item. The method includes determining respective similarities between embeddings of the first plurality of embeddings and embeddings of the second plurality of embeddings and aligning the lyrics text and the audio for the media item based on the respective similarities. The method includes, while streaming the audio for the media item, providing, for display, the aligned lyrics text with the streamed audio.

Abstract: This disclosure is directed to an enhanced audio file generator. One aspect is a method of enhancing input speech in an input audio file, the method comprising receiving the input audio file representing the input speech, wherein the input audio file is recorded at an audio recording device, and generating an enhanced audio file by applying an audio transformation model to the input audio file, wherein applying the audio transformation model to generate the enhanced audio file comprises extracting parameters defining audio features from the input audio file, the parameters including a noise parameter defining noise in the input audio file and one or more other preset parameters respectively defining other audio features, synthesizing clean speech based on the extracted parameters including the noise parameter, wherein synthesizing the clean speech comprises transforming the noise parameter to defined value(s); and generating the enhanced audio file with the synthesized clean speech.

Abstract: A system and method for data collection and aggregation using a distributed network of communications enabled sensors connected to another primary network to achieve a secondary out-of-band monitoring perspective, for example, in power grids. The data collection system includes an aggregation and processing server configured to collect data from a variety of sensors adjacent to the monitored network each sensor includes secondary power such that it can continue data transmission even during power grid outages. The data collection system includes a method for secure real-time data ingest, machine learning enabled analysis, risk assessment, and anomaly detection on a broad geographic scale irrespective of isolated network boundaries.

Abstract: An electronic device receives audio data for a media item. The electronic device generates, from the audio data, a plurality of samples, each sample having a predefined maximum length. The electronic device, using a neural network trained to predict textal unit probabilities, generates a probability matrix of textual units for a first portion of a first sample of the plurality of samples. The probability matrix includes information about textual units, timing information, and respective probabilities of respective textual units at respective times. The electronic device identifies, for the first portion of the first sample, a first sequence of textual units based on the generated probability matrix.

Abstract: An electronic device receives audio data for a media item. The electronic device generates, from the audio data, a plurality of samples, each sample having a predefined maximum length. The electronic device, using a neural network trained to predict character probabilities, generates a probability matrix of characters for a first portion of a first sample of the plurality of samples. The probability matrix includes character information, timing information, and respective probabilities of respective characters at respective times. The electronic device identifies, for the first portion of the first sample, a first sequence of characters based on the generated probability matrix.

Abstract: Apparatus and methods for managing provision of content to devices in a content delivery network. In one exemplary embodiment, content with a high probability of viewership is sent to consumer premises equipment (CPE) during off-peak periods and stored prior to viewing. An application is utilized to manage decisions related to content provision. The computer program will identify content that is likely to be of interest users associated with respective CPE, and schedule provision of that content in advance of viewing. Then, the system will develop a plan for optimal scheduling of transmission of content to CPEs, often including the use of trickle downloads. The scheduling plan is based collected statistical and historical data on network resource demand to make scheduling decisions. The system allows for the shifting of bandwidth utilization from periods of high demand to those of low demand, and increased performance with regard to user experienced latency.

Abstract: Apparatus and methods for managing provision of content to devices in a content delivery network. In one exemplary embodiment, content with a high probability of viewership is sent to consumer premises equipment (CPE) during off-peak periods and stored prior to viewing. An application is utilized to manage decisions related to content provision. The computer program will identify content that is likely to be of interest users associated with respective CPE, and schedule provision of that content in advance of viewing. Then, the system will develop a plan for optimal scheduling of transmission of content to CPEs, often including the use of trickle downloads. The scheduling plan is based collected statistical and historical data on network resource demand to make scheduling decisions. The system allows for the shifting of bandwidth utilization from periods of high demand to those of low demand, and increased performance with regard to user experienced latency.

Abstract: An electronic device receives audio data for a media item. The electronic device generates, from the audio data, a plurality of samples, each sample having a predefined maximum length. The electronic device, using a neural network trained to predict textal unit probabilities, generates a probability matrix of textual units for a first portion of a first sample of the plurality of samples. The probability matrix includes information about textual units, timing information, and respective probabilities of respective textual units at respective times. The electronic device identifies, for the first portion of the first sample, a first sequence of textual units based on the generated probability matrix.

Abstract: An electronic device receives audio data for a media item. The electronic device generates, from the audio data, a plurality of samples, each sample having a predefined maximum length. The electronic device, using a neural network trained to predict character probabilities, generates a probability matrix of characters for a first portion of a first sample of the plurality of samples. The probability matrix includes character information, timing information, and respective probabilities of respective characters at respective times. The electronic device identifies, for the first portion of the first sample, a first sequence of characters based on the generated probability matrix.

Abstract: Apparatus and methods for managing provision of content to devices in a content delivery network. In one exemplary embodiment, content with a high probability of viewership is sent to consumer premises equipment (CPE) during off-peak periods and stored prior to viewing. An application is utilized to manage decisions related to content provision. The computer program will identify content that is likely to be of interest users associated with respective CPE, and schedule provision of that content in advance of viewing. Then, the system will develop a plan for optimal scheduling of transmission of content to CPEs, often including the use of trickle downloads. The scheduling plan is based collected statistical and historical data on network resource demand to make scheduling decisions. The system allows for the shifting of bandwidth utilization from periods of high demand to those of low demand, and increased performance with regard to user experienced latency.

Abstract: Apparatus and methods for managing provision of content to devices in a content delivery network. In one exemplary embodiment, content with a high probability of viewership is sent to consumer premises equipment (CPE) during off-peak periods and stored prior to viewing. An application is utilized to manage decisions related to content provision. The computer program will identify content that is likely to be of interest users associated with respective CPE, and schedule provision of that content in advance of viewing. Then, the system will develop a plan for optimal scheduling of transmission of content to CPEs, often including the use of trickle downloads. The scheduling plan is based collected statistical and historical data on network resource demand to make scheduling decisions. The system allows for the shifting of bandwidth utilization from periods of high demand to those of low demand, and increased performance with regard to user experienced latency.

Abstract: An analyzer resource monitors content access information indicating availability of different types of content available to multiple subscribers over a shared communication link. To facilitate selection of content by respective subscribers, the analyzer resource sorts the available content into different classes (genres) based on a respective subject matter of the corresponding available content. The analyzer resource produces a listing of the different classes (genres) of available content. Subscribers select and/or customize genre-based feeds to store content in a repository for later retrieval.

Abstract: An analyzer resource monitors content access information indicating availability of different types of content available to multiple subscribers over a shared communication link. To facilitate selection of content by respective subscribers, the analyzer resource sorts the available content into different classes (genres) based on a respective subject matter of the corresponding available content. The analyzer resource produces a listing of the different classes (genres) of available content. Subscribers select and/or customize genre-based feeds to store content in a repository for later retrieval.