Phase vocoder-based time-stretching is a widely used technique for the time-scale modification of audio signals. However, conventional implementations suffer from "percussion smearing," a well-known artifact that significantly degrades the quality of percussive components. We attribute this artifact to a fundamental time-scale mismatch between the temporally smeared magnitude spectrogram and the localized, newly generated phase. To address this, we propose SELEBI, a signal-adaptive phase vocoder algorithm that significantly reduces percussion smearing while preserving stability and the perfect reconstruction property. Unlike conventional methods that rely on heuristic processing or component separation, our approach leverages the nonstationary Gabor transform. By dynamically adapting analysis window lengths to assign short windows to intervals containing significant energy associated with percussive components, we directly compute a temporally localized magnitude spectrogram from the time-domain signal. This approach ensures greater consistency between the temporal structures of the magnitude and phase. Furthermore, the perfect reconstruction property of the nonstationary Gabor transform guarantees stable, high-fidelity signal synthesis, in contrast to previous heuristic approaches. Experimental results demonstrate that the proposed method effectively mitigates percussion smearing and yields natural sound quality.

The acquisition of the human pinna geometry requires elaborate equipment for accurate results. Even then, the results are often corrupted by measurement artifacts. We introduce Mesh2PPM, a framework facilitating the generation of a personalized and artifact-free pinna mesh. Mesh2PPM predicts the parameters of a parametric pinna model based on cubic B & eacute;zier curves (BezierPPM) from a pinna mesh via a vision transformer. We evaluated Mesh2PPM with multi-view renderings of synthetic pinna geometries, providing additional depth information, varying the grids of camera views, and jittering the camera views. While added depth information had no practically relevant effect, a grid with 3 & times;3 camera views facilitated the lowest overall prediction errors and best counteracted the detrimental effects of jitter. For this grid, with and without jitter, the median Pompeiu-Hausdorff distances were 1.98 mm and 1.34 mm, respectively, and the root mean square distances were 0.92 mm and 0.52 mm. A refined analysis targeting the perceptually most important pinna regions for sound localization showed that multi-view information particularly improved the prediction of BezierPPM parameters describing the cavum-conchae region. The accuracy achieved indicates the suitability of Mesh2PPM to retrieve BezierPPM parameters from pinna meshes.