Abstract: In an embodiment, a method includes: receiving an audio frame; decomposing the received audio frame into M sub-band pulse-code modulation (PCM) audio frames, where M is a positive integer number; predicting a PCM sample of one sub-band PCM audio frame of the M sub-band PCM audio frames; comparing the predicted PCM sample with a corresponding received PCM sample to generate a prediction error sample; comparing an instantaneous absolute value of the prediction error sample with a threshold; and replacing the corresponding received PCM sample with a value based on the predicted PCM sample when the instantaneous absolute value of the prediction error sample is greater than the threshold.

Abstract: In an embodiment, a method includes: receiving an audio frame; decomposing the received audio frame into M sub-band pulse-code modulation (PCM) audio frames, where M is a positive integer number; predicting a PCM sample of one sub-band PCM audio frame of the M sub-band PCM audio frames; comparing the predicted PCM sample with a corresponding received PCM sample to generate a prediction error sample; comparing an instantaneous absolute value of the prediction error sample with a threshold; and replacing the corresponding received PCM sample with a value based on the predicted PCM sample when the instantaneous absolute value of the prediction error sample is greater than the threshold.

Abstract: In an embodiment, a method includes: receiving an audio frame; decomposing the received audio frame into M sub-band pulse-code modulation (PCM) audio frames, where M is a positive integer number; predicting a PCM sample of one sub-band PCM audio frame of the M sub-band PCM audio frames; comparing the predicted PCM sample with a corresponding received PCM sample to generate a prediction error sample; comparing an instantaneous absolute value of the prediction error sample with a threshold; and replacing the corresponding received PCM sample with a value based on the predicted PCM sample when the instantaneous absolute value of the prediction error sample is greater than the threshold.

Abstract: In an embodiment, a method includes: receiving an audio frame; decomposing the received audio frame into M sub-band pulse-code modulation (PCM) audio frames, where M is a positive integer number; predicting a PCM sample of one sub-band PCM audio frame of the M sub-band PCM audio frames; comparing the predicted PCM sample with a corresponding received PCM sample to generate a prediction error sample; comparing an instantaneous absolute value of the prediction error sample with a threshold; and replacing the corresponding received PCM sample with a value based on the predicted PCM sample when the instantaneous absolute value of the prediction error sample is greater than the threshold.

Abstract: Embodiments of a computer system that determines a performance metric are described. During operation, the computer system determines the performance metric for tasks performed by financial software during a time interval. This performance metric is based on a weighted summation of contributions from the tasks, and a given weight associated with a given task is based on a frequency of occurrence of the given task. Then, the computer system performs a remedial action to improve the determined performance metric during a subsequent time interval.

Abstract: Some embodiments of the present invention provide a system that manages process flows based on user behavior. During operation, at least one available user input selection in a first process flow is designated as being associated with an optional process flow segment. Next, at least one input that has been designated as an available user input selection is received from a user, and information associated with that input is stored in a data set. The data set is then analyzed to determine whether local criteria associated with the optional process flow segment has been satisfied. If so, the optional process flow segment is scheduled to be performed.

Abstract: Embodiments of a computer system that determines a performance metric are described. During operation, the computer system determines the performance metric for tasks performed by financial software during a time interval. This performance metric is based on a weighted summation of contributions from the tasks, and a given weight associated with a given task is based on a frequency of occurrence of the given task. Then, the computer system performs a remedial action to improve the determined performance metric during a subsequent time interval.