Abstract: A machine is configured to identify a media file that, when played to a user, is likely to modify an emotional or physical state of the user to or towards a target emotional or physical state. The machine accesses play counts that quantify playbacks of media files for the user. The playbacks may be locally performed or detected by the machine from ambient sound. The machine accesses arousal scores of the media files and determines a distribution of the play counts over the arousal scores. The machine uses one or more relative maxima in the distribution in selecting a target arousal score for the user based on contextual data that describes an activity of the user. The machine selects one or more media files based on the target arousal score. The machine may then cause the selected media file to be played to the user.

Abstract: Example methods and systems for generating a video presentation to accompany audio are described. The video presentation to accompany the audio track is generated from one or more video sequences. In some example embodiments, the video sequences are divided into video segments that correspond to discontinuities between frames. Video segments are concatenated to form a video presentation to which the audio track is added. In some example embodiments, only video segments having a duration equal to an integral number of beats of music in the audio track are used to form the video presentation. In these example embodiments, transitions between video segments in the video presentation that accompanies the audio track are aligned with the beats of the music.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for playback using pre-processed profile information and personalization. Example apparatus disclosed herein include a synchronizer to, in response to receiving a media signal to be played on a playback device, access an equalization (EQ) profile corresponding to the media signal; an EQ personalization manager to generate a personalized EQ setting; and an EQ adjustment implementor to modify playback of the media signal on the playback device based on a blended equalization generation based on the EQ profile and the personalized EQ setting.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for dynamic volume adjustment via audio classification.

Abstract: A machine is configured to identify a media file that, when played to a user, is likely to modify an emotional or physical state of the user to or towards a target emotional or physical state. The machine accesses play counts that quantify playbacks of media files for the user. The playbacks may be locally performed or detected by the machine from ambient sound. The machine accesses arousal scores of the media files and determines a distribution of the play counts over the arousal scores. The machine uses one or more relative maxima in the distribution in selecting a target arousal score for the user based on contextual data that describes an activity of the user. The machine selects one or more media files based on the target arousal score. The machine may then cause the selected media file to be played to the user.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for playback using pre-processed profile information and personalization. Example apparatus disclosed herein include a synchronizer to, in response to receiving a media signal to be played on a playback device, access an equalization (EQ) profile corresponding to the media signal; an EQ personalization manager to generate a personalized EQ setting; and an EQ adjustment implementor to modify playback of the media signal on the playback device based on a blended equalization generated based on the EQ profile and the personalized EQ setting.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for dynamic volume adjustment via audio classification.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for audio equalization. Example instructions disclosed herein cause one or more processors to at least: detect an irregularity in a frequency representation of an audio signal in response to a change in volume between a set of frequency values exceeding a threshold; and adjust a volume at a first frequency value of the set of frequency values to reduce the irregularity..

Abstract: A machine is configured to identify a media file that, when played to a user, is likely to modify an emotional or physical state of the user to or towards a target emotional or physical state. The machine accesses play counts that quantify playbacks of media files for the user. The playbacks may be locally performed or detected by the machine from ambient sound. The machine accesses arousal scores of the media files and determines a distribution of the play counts over the arousal scores. The machine uses one or more relative maxima in the distribution in selecting a target arousal score for the user based on contextual data that describes an activity of the user. The machine selects one or more media files based on the target arousal score. The machine may then cause the selected media file to be played to the user.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for audio equalization. Example instructions disclosed herein cause one or more processors to at least: detect an irregularity in a frequency representation of an audio signal in response to a change in volume between a set of frequency values exceeding a threshold; and adjust a volume at a first frequency value of the set of frequency values to reduce the irregularity.

Abstract: A machine is configured to identify a media file that, when played to a user, is likely to modify an emotional or physical state of the user to or towards a target emotional or physical state. The machine accesses play counts that quantify playbacks of media files for the user. The playbacks may be locally performed or detected by the machine from ambient sound. The machine accesses arousal scores of the media files and determines a distribution of the play counts over the arousal scores. The machine uses one or more relative maxima in the distribution in selecting a target arousal score for the user based on contextual data that describes an activity of the user. The machine selects one or more media files based on the target arousal score. The machine may then cause the selected media file to be played to the user.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to adjust audio playback settings. Example apparatus disclosed herein include an equalization (EQ) model query generator to generate a query to a neural network, the query including a representation of a sample of an audio signal; an EQ filter settings analyzer to: access a plurality of audio playback settings determined by the neural network based on the query; and determine a filter coefficient to apply to the audio signal based on the plurality of audio playback settings; an EQ personalization manager to; generate a personalized EQ setting; and an EQ adjustment implementor to: blend the personalized EQ setting and the filter coefficient to generate a blended equalization; and apply the blended equalization to the audio signal in a first duration.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to adjust audio playback settings based on analysis of audio characteristics. Example apparatus disclosed herein include an equalization (EQ) model query generator to generate a query to a neural network, the query including a representation of a sample of an audio signal; an EQ filter settings analyzer to: access a plurality of audio playback settings determined by the neural network based on the query; and determine a filter coefficient to apply to the audio signal based on the plurality of audio playback settings; and an EQ adjustment implementer to apply the filter coefficient to the audio signal in a first duration.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for dynamic volume adjustment via audio classification.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for dynamic volume adjustment via audio classification. Examples methods include analyzing, with a neural network trained model, a parameter of an audio signal associated with a first volume level to determine a classification group associated with the audio signal, determining an input volume of the audio signal, the selection based on the classification group associated with the audio signal, applying a gain value to the audio signal, the gain value based on the classification group and the input volume, the gain value to modify the first volume level to a second volume level, and applying a compression value to the audio signal, the compression value to modify the second volume level to a third volume level that satisfies a target volume threshold.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for audio equalization. Example instructions disclosed herein cause one or more processors to at least: detect an irregularity in a frequency representation of an audio signal in response to a change in volume between a set of frequency values exceeding a threshold; and adjust a volume at a first frequency value of the set of frequency values to reduce the irregularity.

Abstract: A neural network-based classifier system can receive a query including a media signal and, in response, provide an indication that a particular received query corresponds to a known media type or media class. The neural network-based classifier system can select and apply various models to facilitate media classification. In an example embodiment, classifying a media query includes accessing digital media data and a context parameter from a first device. A model for use with the network-based classifier system can be selected based on the context parameter. In an example embodiment, the network-based classifier system provides a media type probability index for the digital media data using the selected model and spectral features corresponding to the digital media data. In an example embodiment, the digital media data includes an audio or video signal sample.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for audio equalization. Example apparatus disclosed herein include a volume adjuster to apply equalization adjustments to an audio signal to generate an equalized audio signal, the equalization adjustments output from a neural network in response to an input feature set; a thresholding controller to: detect an irregularity in a frequency representation of the audio signal after application of the equalization adjustments, the irregularity corresponding to a change in volume between adjacent frequency values exceeding a threshold; and adjust a volume at a first frequency value of the adjacent frequency values to reduce the irregularity; an equalization (EQ) curve generator to generate an EQ curve to apply to the audio signal when the irregularity has been reduced; and a frequency to time domain converter to output the equalized audio signal in a time domain based on the EQ curve.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for audio equalization. Example apparatus disclosed herein include a volume adjuster to apply equalization adjustments to an audio signal to generate an equalized audio signal, the equalization adjustments output from a neural network in response to an input feature set; a thresholding controller to: detect an irregularity in a frequency representation of the audio signal after application of the equalization adjustments, the irregularity corresponding to a change in volume between adjacent frequency values exceeding a threshold; and adjust a volume at a first frequency value of the adjacent frequency values to reduce the irregularity; an equalization (EQ) curve generator to generate an EQ curve to apply to the audio signal when the irregularity has been reduced; and a frequency to time domain converter to output the equalized audio signal in a time domain based on the EQ curve.

Abstract: A neural network-based classifier system can receive a query including a media signal and, in response, provide an indication that a particular received query corresponds to a known media type or media class. The neural network-based classifier system can select and apply various models to facilitate media classification. In an example embodiment, classifying a media query includes accessing digital media data and a context parameter from a first device. A model for use with the network-based classifier system can be selected based on the context parameter. In an example embodiment, the network-based classifier system provides a media type probability index for the digital media data using the selected model and spectral features corresponding to the digital media data. In an example embodiment, the digital media data includes an audio or video signal sample.