SUBBAND-BASED PARAMETER OPTIMIZATION IN NOISE REDUCTION SCHEMES BY MEANS OF OBJECTIVE PERCEPTUAL QUALITY MEASURES

In general, noise reduction schemes for application in hearingaids or car environments have parameters that are determined by technical distance measures or heuristically based on informal listening by the algorithm developers. In [1] we have shown that quality measures based on psychoacoustic models are better suited to optimize single parameters in terms of the best subjective overall quality than pure technical measures like, e.g., the signal-to-noise ratio. In other words, a test-bench based on objective quality measures and several typical noise types can support the search for the best-sounding noise reduction algorithms and their internal parameter settings. However, if the algorithms become more complex, e.g., because of frequency-dependent parameters, a single broadband measure might not be feasible to assess optimal settings because of the high dimensionality of the parameter space. In this case a subband-based perceptual quality measure might be feasible. In this study, we exemplarily apply subband-based quality prediction to parameter optimization in a noise reduction algorithm based on auditory filters.