Sound propagation in urban and indoor environments often involves diffraction at corners, finite objects and openings, resulting in perceptually relevant frequency-dependent attenuation. Geometrical acoustics (GA) have become a de-facto standard for the prediction and simulation of sound propagation and real-time virtual acoustics, including effects of edge diffraction. However, methods to account for edge diffraction often assume infinite edges, such as the uniform theory of diffraction, or are computationally involved, such as the Biot-Tolstoy-Medwin-Svensson (BTMS) method. Particularly for interactive auralization, an efficient approximation of edge diffraction is desirable. Here, an extension of GA to account for diffraction with simple-to-derive parameters and a time-domain recursive filter implementation is suggested. This universal diffraction filter approximation (UDFA) describes diffraction from infinite and finite wedges using a combination of first-order and (fractional) half-order low-pass filters to account for the frequency-dependent attenuation of the diffracted incident and reflected sound field. The physically-based UDFA exactly matches asymptotic solutions for infinite wedges and provides approximations for finite wedges. It is demonstrated that first-order diffraction from flat finite objects like a plate or an aperture can be described by combining the filters for each edge. To account for effects of higher-order diffraction, an additional heuristic filter extension is suggested.

Recently, the binaural auditory-model-based quality prediction (BAM-Q) was successfully applied to predict binaural audio quality degradations, while the generalized power-spectrum model for quality (GPSM$^\text{q}$) has been demonstrated to account for a large variety of monaural signal distortions. For many applications, a combined monaural and binaural model would be advantageous, however, the contribution of monaural and binaural quality aspects to overall (spatial) quality is not conclusively clarified. Thus, the current study systematically investigated overall audio quality in a listening experiment for monaural and binaural distortions on music, speech, and noise, applied either in isolation or in combination. The resulting database was used for assessing different methods for combining BAM-Q and GPSM$^\text{q}$to joint overall audio predictions for monaural and binaural signal distortions. It was investigated, if monaural or binaural quality aspects contribute stronger to overall audio quality. The results indicate that overall audio quality depends on the lower quality aspect, either monaural or binaural.

Sensorineural hearing loss typically results in elevated thresholds and steepened loudness growth significantly conditioned by a damage of outer hair cells (OHC). In hearing aids, amplification and dynamic compression aim at widening the limited available dynamic range. However, speech perception particularly in complex acoustic scenes often remains difficult. Here, a physiologically motivated, fast acting, model-based dynamic compression algorithm (MDC) is introduced which aims at restoring the behaviorally estimated basilar membrane input-output (BM I/O) function in normal-hearing listeners. A system-specific gain prescription rule is suggested, based on the same model BM I/O function and a behavioral estimate of the individual OHC loss. Cochlear off-frequency component suppression is mimicked using an instantaneous frequency (IF) estimate. Increased loudness as a consequence of widened filters in the impaired system is considered in a further compensation stage. In an extended version, a subband estimate of the speech presence probability (MDC+SPP) additionally provides speech-selective amplification in stationary noise. Instrumental evaluation revealed that the IF control enhances the spectral contrast of vowels and benefits in quality predictions at higher signal-to-noise ratios (SNRs) were observed. Compared with a conventional multiband dynamic compressor, MDC achieved objective quality and intelligibility benefits for a competing talker at lower SNRs. MDC+SPP outperformed the conventional compressor in the quality predictions and reached comparable instrumental speech intelligibility as achieved with linear amplification. The proposed algorithm provides a first promising basis for auditory model-based compression with signal-type- and bandwidth-dependent gains.

A perceptual, binaural audio-quality model is introduced. The model was developed for predicting any kinds of perceived spatial quality differences between two audio signals in multi-channel reproduction and audio processing schemes. It employs a recent binaural auditory model as front-end to provide perceptually relevant binaural features for the reference and test audio signal. Correlations between the binaural features of both signals are combined to an overall spatial quality measure by the use of multivariate adaptive regression splines (MARS). Furthermore, a database was generated to train and evaluate the model. The database contains various multi-channel audio signals, which were subjectively assessed in formal listening tests with 15 trained listeners. The results show different model prediction performances depending on the type of quality degradation. Combination of the proposed spatial quality measure with established monaural quality measures improved the predictive power.