Implementations of audio algorithms on embedded devices are required to consume minimal memory and processing power. Such applications can usually tolerate numerical imprecisions (distortion) as long as the resulting perceived quality is not degraded. By taking advantage of this error-tolerant nature the algorithmic complexity can be reduced greatly. In the context of real-time audio coding, these algorithms can benefit from parametrization to adapt rate-distortion-complexity (R-D-C) trade-offs. We propose a modification to the rate-distortion loop in the quantization and coding stage of a fixed-point implementation of the Advanced Audio Coding (AAC) encoder to include complexity scaling. This parametrization could allow the control of algorithmic complexity through instantaneous workload measurements using the target processor’s task scheduler to better assign processing resources. Results show that this framework can be tuned to reduce a significant amount of the additional workload caused by the ratedistortion loop while remaining perceptually equivalent to the fullcomplexity version. Additionally, the modification allows a graceful degradation when transparency cannot be met due to limited computational capabilities.

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