Abstract: An audio processing system and method for processing audio is disclosed. The audio processing system collects an input audio signal indicative of degraded measurements of a target audio waveform. The input audio signal is restored with recursive restoration that recursively restores the input audio signal until a termination condition is met. A current iteration of the recursive restoration applies a restoration operator configured to restore a degraded audio signal conditioned on a current level of severity of degradation and degrades the degraded audio signal deterministically with a level of severity less than the current level of severity. A target signal estimate indicative of enhanced measurements of the audio waveform is generated as output.

Abstract: An apparatus and method of rendering audio objects with multiple types of renderers. The weighting between the selected renderers depends upon the position information in each audio object. As each type of renderer has a different output coverage, the combination of their weighted outputs results in the audio being perceived at the position according to the position information.

Abstract: An apparatus and method of rendering audio objects with multiple types of renderers. The weighting between the selected renderers depends upon the position information in each audio object. As each type of renderer has a different output coverage, the combination of their weighted outputs results in the audio being perceived at the position according to the position information.

Abstract: Performance metric based stopping criteria for iterative algorithm techniques are described. In one or more implementations, a training dataset is processed by one or more computing devices using an iterative algorithm having a cost function. The processing includes, for a plurality of iterations of the iterative algorithm, computing a cost for the iterative algorithm using the cost function and a value for each of a plurality of performance metrics that are usable to infer accuracy of the iterative algorithm for a respective one of the iterations. Responsive to the processing, a particular one of the plurality of iterations is identified as a stopping criterion based at least in part on the computed values for the plurality of performance metrics and the stopping criterion is output to configure the iterative algorithm to use the stopping criterion for subsequent processing of data by the iterative algorithm.

Abstract: Acoustic matching and splicing of sound tracks is described. In one or more implementations, a method to acoustically match and splice first and second sound tracks by one or more computing devices is described. The method includes source separating the first and second sound tracks into first track primary and background sound data and second track primary and background sound data. Features extracted from the first and second primary sound data are matched, one to another, to generate first and second primary matching masks. Features extracted from the first and second background sound data are matched, one to another, to generate first and second background matching masks, which are applied to respective separated sound data. The applied first track primary and background sound data and the applied second track primary and background sound data are spliced to generate a spliced sound track.

Abstract: Acoustic matching and splicing of sound tracks is described. In one or more implementations, a method to acoustically match and splice first and second sound tracks by one or more computing devices is described. The method includes source separating the first and second sound tracks into first track primary and background sound data and second track primary and background sound data. Features extracted from the first and second primary sound data are matched, one to another, to generate first and second primary matching masks. Features extracted from the first and second background sound data are matched, one to another, to generate first and second background matching masks, which are applied to respective separated sound data. The applied first track primary and background sound data and the applied second track primary and background sound data are spliced to generate a spliced sound track.

Abstract: Performance metric based stopping criteria for iterative algorithm techniques are described. In one or more implementations, a training dataset is processed by one or more computing devices using an iterative algorithm having a cost function. The processing includes, for a plurality of iterations of the iterative algorithm, computing a cost for the iterative algorithm using the cost function and a value for each of a plurality of performance metrics that are usable to infer accuracy of the iterative algorithm for a respective one of the iterations. Responsive to the processing, a particular one of the plurality of iterations is identified as a stopping criterion based at least in part on the computed values for the plurality of performance metrics and the stopping criterion is output to configure the iterative algorithm to use the stopping criterion for subsequent processing of data by the iterative algorithm.