Martin Dewhirst

Modeling listener distraction resulting from audio-on-audio interference

As devices that produce audio become more commonplace and increasingly portable, situations in which two competing audio programs are present occur more regularly. In order to support the design of systems intended to mitigate the effects of interfering audio (including sound field control, noise cancelation or source separation systems), it is desirable to model the perceived distraction in such situations. Distraction ratings were collected for a range of audio-on-audio interference situations including various target and interferer programs at three interferer levels, with and without road noise. Time-frequency target-to-interferer ratio (TIR) maps of the stimuli were created using a simple auditory model. A number of feature sets were extracted from the TIR maps, including combinations of mean, standard deviation, minimum and maximum TIR taken across the duration of the program item. In order to predict distraction ratings from the features, linear regression models were produced. The models were evaluated for goodness-of-fit (RMSE) and generalizability (using a K-fold cross-validation procedure). The best model performed well, with almost all predictions falling within the 95% confidence intervals of the perceptual data. A validation data set was used to test the model, suggesting areas for future improvement.

Measuring perceived spatial quality changes in surround sound reproduction

The spatial quality of audio content delivery systems is becoming increasingly important as service providers attempt to deliver enhanced experiences of spatial immersion and naturalness in audio-visual applications. Examples are virtual reality, telepresence, home cinema, games and communications products. The QESTRAL project is developing an artificial listener that will compare the perceived quality of a spatial audio reproduction to a reference reproduction. The model is calibrated using data from listening tests, and utilises a range of metrics to predict the resulting spatial sound quality ratings. Potential application areas for the model are outlined, together with exemplary results obtained from some of its component parts.

Prediction of spatial perceptual attributes of reproduced sound across the listening area

Audio systems and recordings are optimized for listening at the “sweet spot,” but how well do they work elsewhere? An acoustic‐perceptual model has been developed that simulates sound reproduction in a variety of formats, including mono, two‐channel stereo, five‐channel surround and wavefield synthesis. A virtual listener placed anywhere in the listening area is used to extract binaural signals, and hence interaural cues to the spatial attributes of the soundfield. Using subjectively‐validated models of spatial sound perception, we can predict the way that human listeners would perceive these attributes, such as the direction (azimuth) and width of a phantom source. Results will be presented across the listening area for different source signals, sound scenes and reproduction systems, illustrating their spatial fidelity in perceptual terms. Future work investigates the effects of typical reproduction degradations.

Eliciting the most prominent perceived differences between microphones

The attributes contributing to the differences perceived between microphones (when auditioning recordings made with those microphones) are not clear from previous research. Consideration of technical specifications and expert opinions indicated that recording five programme items with eight studio and two microelectromechanical system microphones could allow determination of the attributes related to the most prominent inter-microphone differences. Pairwise listening comparisons between the resulting 50 recordings, followed by multi-dimensional scaling analysis, revealed up to 5 salient dimensions per programme item; 17 corresponding pairs of recordings were selected exemplifying the differences across those dimensions. Direct elicitation and panel discussions on the 17 pairs identified a hierarchy of 40 perceptual attributes. An attribute contribution experiment on the 31 lowest-level attributes in the hierarchy allowed them to be ordered by degree of contribution and showed brightness, harshness, and clarity to always contribute highly to perceived inter-microphone differences. This work enables the future development of objective models to predict these important attributes.