Abstract: Introduced here are computer programs and associated computer-implemented techniques for discovering the presence of filler words through tokenization of a transcript derived from audio content. When audio content is obtained by a media production platform, the audio content can be converted into text content as part of a speech-to-text operation. The text content can then be tokenized and labeled using a Natural Language Processing (NLP) library. Tokenizing/labeling may be performed in accordance with a series of rules associated with filler words. At a high level, these rules may examine the text content (and associated tokens/labels) to determine whether patterns, relationships, verbatim, and context indicate that a term is a filler word. Any filler words that are discovered in the text content can be identified as such so that appropriate action(s) can be taken.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for manipulating noisy audio signals to produce clean audio signals that are sufficiently high quality so as to be largely, if not entirely, indistinguishable from “rich” recordings generated by recording studios. When a noisy audio signal is obtained by a media production platform, the noisy audio signal can be manipulated to sound as if recording occurred with sophisticated equipment in a soundproof environment. Manipulation can be performed by a model that, when applied to the noisy audio signal, can manipulate its characteristics so as to emulate the characteristics of clean audio signals that are learned through training.

Abstract: The invention relates to simultaneous recording and uploading systems and methods, and, more particularly to a simultaneous recording and uploading multiple files from the same conversation.

Abstract: Different types of media experiences can be developed based on characteristics of the consumer. “Linear” experiences may require execution of a pre-built script, although the script could be dynamically modified by a media production platform. Linear experiences can include guided audio tours that are modified or updated based on the location of the consumer. “Enhanced” experiences include conventional media content that is supplemented with intelligent media content. For example, turn-by-turn directions could be supplemented with audio descriptions about the surrounding area. “Freeform” experiences, meanwhile, are those that can continually morph based on information gleaned from a consumer. For example, a radio station may modify what content is being presented based on the geographical metadata uploaded by a computing device associated with the consumer.

Abstract: Introduced here are approaches to training and then employing computer-implemented models designed to upsample discrete audio signals to higher sampling rates. Assume, for example, that a media production platform obtains a first discrete signal at a relatively low sampling rate. The relatively low sampling frequency may make the first discrete audio signal unsuitable for inclusion in media compilations, so the media production platform may attempt to improve its quality through upsampling. To accomplish this, the media production platform can apply a transform to the first discrete signal to produce a first magnitude spectrogram. Then, the media production platform can apply a computer-implemented model to the first magnitude spectrogram to produce a second magnitude spectrogram. Thereafter, the media production platform can apply an inverse transform to the second magnitude spectrogram to create a second discrete signal that has a higher sampling rate than the first discrete audio signal.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for discovering the presence of filler words through tokenization of a transcript derived from audio content. When audio content is obtained by a media production platform, the audio content can be converted into text content as part of a speech-to-text operation. The text content can then be tokenized and labeled using a Natural Language Processing (NLP) library. Tokenizing/labeling may be performed in accordance with a series of rules associated with filler words. At a high level, these rules may examine the text content (and associated tokens/labels) to determine whether patterns, relationships, verbatim, and context indicate that a term is a filler word. Any filler words that are discovered in the text content can be identified as such so that appropriate action(s) can be taken.

Abstract: Introduced here are approaches to training and then employing computer-implemented models designed to upsample discrete audio signals to higher sampling rates. Assume, for example, that a media production platform obtains a first discrete signal at a relatively low sampling rate. The relatively low sampling frequency may make the first discrete audio signal unsuitable for inclusion in media compilations, so the media production platform may attempt to improve its quality through upsampling. To accomplish this, the media production platform can apply a transform to the first discrete signal to produce a first magnitude spectrogram. Then, the media production platform can apply a computer-implemented model to the first magnitude spectrogram to produce a second magnitude spectrogram. Thereafter, the media production platform can apply an inverse transform to the second magnitude spectrogram to create a second discrete signal that has a higher sampling rate than the first discrete audio signal.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for facilitating the creation of a master transcription (or simply “transcript”) that more accurately reflects underlying audio by comparing multiple independently generated transcripts. The master transcript may be used to record and/or produce various forms of media content, as further discussed below. Thus, the technology described herein may be used to facilitate editing of text content, audio content, or video content. These computer programs may be supported by a media production platform that is able to generate the interfaces through which individuals (also referred to as “users”) can create, edit, or view media content. For example, a computer program may be embodied as a word processor that allows individuals to edit voice-based audio content by editing a master transcript, and vice versa.

Abstract: Media content can be created and/or modified using a network-accessible platform. Scripts for content-based experiences could be readily created using one or more interfaces generated by the network-accessible platform. For example, a script for a content-based experience could be created using an interface that permits triggers to be inserted directly into the script. Interface(s) may also allow different media formats to be easily aligned for post-processing. For example, a transcript and an audio file may be dynamically aligned so that the network-accessible platform can globally reflect changes made to either item. User feedback may also be presented directly on the interface(s) so that modifications can be made based on actual user experiences.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for facilitating the creation of a master transcription (or simply “transcript”) that more accurately reflects underlying audio by comparing multiple independently generated transcripts. The master transcript may be used to record and/or produce various forms of media content, as further discussed below. Thus, the technology described herein may be used to facilitate editing of text content, audio content, or video content. These computer programs may be supported by a media production platform that is able to generate the interfaces through which individuals (also referred to as “users”) can create, edit, or view media content. For example, a computer program may be embodied as a word processor that allows individuals to edit voice-based audio content by editing a master transcript, and vice versa.

Abstract: Different types of media experiences can be developed based on characteristics of the consumer. “Linear” experiences may require execution of a pre-built script, although the script could be dynamically modified by a media production platform. Linear experiences can include guided audio tours that are modified or updated based on the location of the consumer. “Enhanced” experiences include conventional media content that is supplemented with intelligent media content. For example, turn-by-turn directions could be supplemented with audio descriptions about the surrounding area. “Freeform” experiences, meanwhile, are those that can continually morph based on information gleaned from a consumer. For example, a radio station may modify what content is being presented based on the geographical metadata uploaded by a computing device associated with the consumer.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for discovering the presence of filler words through tokenization of a transcript derived from audio content. When audio content is obtained by a media production platform, the audio content can be converted into text content as part of a speech-to-text operation. The text content can then be tokenized and labeled using a Natural Language Processing (NLP) library. Tokenizing/labeling may be performed in accordance with a series of rules associated with filler words. At a high level, these rules may examine the text content (and associated tokens/labels) to determine whether patterns, relationships, verbatim, and context indicate that a term is a filler word. Any filler words that are discovered in the text content can be identified as such so that appropriate action(s) can be taken.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for discovering the presence of filler words through tokenization of a transcript derived from audio content. When audio content is obtained by a media production platform, the audio content can be converted into text content as part of a speech-to-text operation. The text content can then be tokenized and labeled using a Natural Language Processing (NLP) library. Tokenizing/labeling may be performed in accordance with a series of rules associated with filler words. At a high level, these rules may examine the text content (and associated tokens/labels) to determine whether patterns, relationships, verbatim, and context indicate that a term is a filler word. Any filler words that are discovered in the text content can be identified as such so that appropriate action(s) can be taken.

Abstract: Different types of media experiences can be developed based on characteristics of the consumer. “Linear” experiences may require execution of a pre-built script, although the script could be dynamically modified by a media production platform. Linear experiences can include guided audio tours that are modified or updated based on the location of the consumer. “Enhanced” experiences include conventional media content that is supplemented with intelligent media content. For example, turn-by-turn directions could be supplemented with audio descriptions about the surrounding area. “Freeform” experiences, meanwhile, are those that can continually morph based on information gleaned from a consumer. For example, a radio station may modify what content is being presented based on the geographical metadata uploaded by a computing device associated with the consumer.

Abstract: Media content can be created and/or modified using a network-accessible platform. Scripts for content-based experiences could be readily created using one or more interfaces generated by the network-accessible platform. For example, a script for a content-based experience could be created using an interface that permits triggers to be inserted directly into the script. Interface(s) may also allow different media formats to be easily aligned for post-processing. For example, a transcript and an audio file may be dynamically aligned so that the network-accessible platform can globally reflect changes made to either item. User feedback may also be presented directly on the interface(s) so that modifications can be made based on actual user experiences.

Abstract: Different types of media experiences can be developed based on characteristics of the consumer. “Linear” experiences may require execution of a pre-built script, although the script could be dynamically modified by a media production platform. Linear experiences can include guided audio tours that are modified or updated based on the location of the consumer. “Enhanced” experiences include conventional media content that is supplemented with intelligent media content. For example, turn-by-turn directions could be supplemented with audio descriptions about the surrounding area. “Freeform” experiences, meanwhile, are those that can continually morph based on information gleaned from a consumer. For example, a radio station may modify what content is being presented based on the geographical metadata uploaded by a computing device associated with the consumer.

Abstract: Media content can be created and/or modified using a network-accessible platform. Scripts for content-based experiences could be readily created using one or more interfaces generated by the network-accessible platform. For example, a script for a content-based experience could be created using an interface that permits triggers to be inserted directly into the script. Interface(s) may also allow different media formats to be easily aligned for post-processing. For example, a transcript and an audio file may be dynamically aligned so that the network-accessible platform can globally reflect changes made to either item. User feedback may also be presented directly on the interface(s) so that modifications can be made based on actual user experiences.