Abstract: The present disclosure describes systems and methods for a privacy sensitive computing system. One or more embodiments provide a protected computing environment, a code authorization unit, and a data aggregation unit. For example, some embodiments of the privacy sensitive computing system may train unsupervised or self-supervised ML models on user-generated assets subject to privacy considerations that mandate those assets are not viewed directly by human eyes.

Abstract: The present disclosure describes systems and methods for a privacy sensitive computing system. One or more embodiments provide a protected computing environment, a code authorization unit, and a data aggregation unit. For example, some embodiments of the privacy sensitive computing system may train unsupervised or self-supervised ML models on user-generated assets subject to privacy considerations that mandate those assets are not viewed directly by human eyes.

Abstract: A computer-implemented method for audio signal processing includes analyzing a foreground audio signal to determine metrics corresponding to audio slices of the foreground audio signal. Each such metric indicates a value for an audio property of a respective audio slice. The method further includes computing a total metric for an audio slice as a function of a set of the metrics corresponding to a set of the audio slices including the audio slice. The method further includes adding a key frame to a track based on the total metric. The track includes the foreground audio signal and a background audio signal, and a location of the key frame corresponds to a location of the audio slice on the track. The key frame indicates a change to the audio property of the background audio signal at the location on the track, and the key frame is utilizable for audio ducking.

Abstract: A computer-implemented method for audio signal processing includes analyzing a foreground audio signal to determine metrics corresponding to audio slices of the foreground audio signal. Each such metric indicates a value for an audio property of a respective audio slice. The method further includes computing a total metric for an audio slice as a function of a set of the metrics corresponding to a set of the audio slices including the audio slice. The method further includes adding a key frame to a track based on the total metric. The track includes the foreground audio signal and a background audio signal, and a location of the key frame corresponds to a location of the audio slice on the track. The key frame indicates a change to the audio property of the background audio signal at the location on the track, and the key frame is utilizable for audio ducking.

Abstract: Various embodiments describe audio signal processing. In an example, a computer system generates metrics, such as RMS levels, for audio slices from a foreground audio signal. A summed-area table is generated from the metrics. An observation window is used to determine whether to add a key frame or not. The observation window includes a set of audio slices. A total metrics, such as an average RMS level, is computed for the audio slices in the observation window. Based on the total metric, the computer system adds a key frame. The key frame references audio ducking parameters applicable to a background audio signal.

Abstract: Various embodiments describe audio signal processing. In an example, a computer system generates metrics, such as RMS levels, for audio slices from a foreground audio signal. A summed-area table is generated from the metrics. An observation window is used to determine whether to add a key frame or not. The observation window includes a set of audio slices. A total metrics, such as an average RMS level, is computed for the audio slices in the observation window. Based on the total metric, the computer system adds a key frame. The key frame references audio ducking parameters applicable to a background audio signal.

Abstract: Average pixel luminosity is calculated for each frame comprising a content item. For each pair of adjacent frames, an IFD is calculated. The IFD represents the difference between a baseline pixel luminosity associated with each of the two frames. An initial set of cut frames is selected based on IFD values that are less than a minimum value IFDmin, or that are greater than a maximum value IFDmax. The positions of these initial cut frames are optimized using a numerical optimization technique that favors removal of frames corresponding to IFD extrema, but that also attempts to maintain a minimum time gap between cut frames. Selecting frames for removal is approached as a constraint minimization problem. Once an optimized set of cut frames is established, audio is cut and crossfaded in a temporal window surrounding cut frame positions.

Abstract: Average pixel luminosity is calculated for each frame comprising a content item. For each pair of adjacent frames, an IFD is calculated. The IFD represents the difference between a baseline pixel luminosity associated with each of the two frames. An initial set of cut frames is selected based on IFD values that are less than a minimum value IFDmin, or that are greater than a maximum value IFDmax. The positions of these initial cut frames are optimized using a numerical optimization technique that favors removal of frames corresponding to IFD extrema, but that also attempts to maintain a minimum time gap between cut frames. Selecting frames for removal is approached as a constraint minimization problem. Once an optimized set of cut frames is established, audio is cut and crossfaded in a temporal window surrounding cut frame positions.

Abstract: Average pixel luminosity is calculated for each frame comprising a content item. For each pair of adjacent frames, an IFD is calculated. The IFD represents the difference between a baseline pixel luminosity associated with each of the two frames. An initial set of cut frames is selected based on IFD values that are less than a minimum value IFDmin, or that are greater than a maximum value IFDmax. The positions of these initial cut frames are optimized using a numerical optimization technique that favors removal of frames corresponding to IFD extrema, but that also attempts to maintain a minimum time gap between cut frames. Selecting frames for removal is approached as a constraint minimization problem. Once an optimized set of cut frames is established, audio is cut and crossfaded in a temporal window surrounding cut frame positions.

Abstract: Techniques for utilizing sets of filters to reduce a large number of searchable assets to a meaningful or reduced number of searchable assets. Feature information may be extracted from a particular asset of a set of training assets to create an artificial term. A reduction ratio may then be calculated by utilizing the created artificial term to filter the set of training assets. The reduction ratio may represent the ratio of training assets that contain a particular artificial term. A plurality of filters and their associated reduction ratios may be created this way by utilizing the set of training assets. This process can also involve receiving a requested reduction ratio associated with a set of searchable assets. A combination of filters may then be selected which, when applied to the set of searchable assets, results in a reduced number of searchable assets according to the requested reduction ratio.

Abstract: Average pixel luminosity is calculated for each frame comprising a content item. For each pair of adjacent frames, an IFD is calculated. The IFD represents the difference between a baseline pixel luminosity associated with each of the two frames. An initial set of cut frames is selected based on IFD values that are less than a minimum value IFDmin, or that are greater than a maximum value IFDmax. The positions of these initial cut frames are optimized using a numerical optimization technique that favors removal of frames corresponding to IFD extrema, but that also attempts to maintain a minimum time gap between cut frames. Selecting frames for removal is approached as a constraint minimization problem. Once an optimized set of cut frames is established, audio is cut and crossfaded in a temporal window surrounding cut frame positions.

Abstract: Techniques for utilizing sets of filters to reduce a large number of searchable assets to a meaningful or reduced number of searchable assets. Feature information may be extracted from a particular asset of a set of training assets to create an artificial term. A reduction ratio may then be calculated by utilizing the created artificial term to filter the set of training assets. The reduction ratio may represent the ratio of training assets that contain a particular artificial term. A plurality of filters and their associated reduction ratios may be created this way by utilizing the set of training assets. This process can also involve receiving a requested reduction ratio associated with a set of searchable assets. A combination of filters may then be selected which, when applied to the set of searchable assets, results in a reduced number of searchable assets according to the requested reduction ratio.