Ambisonics rendering has become an integral part of 3D audio for headphones. It works well with existing recording hardware, the processing cost is mostly independent of the number of sound sources, and it elegantly allows for rotating the scene and listener. One challenge in Ambisonics headphone rendering is to find a perceptually well behaved low-order representation of the Head-Related Transfer Functions (HRTFs) that are contained in the rendering pipe-line. Low-order rendering is of interest, when working with microphone arrays containing only a few sensors, or for reducing the bandwidth for signal transmission. Magnitude Least Squares rendering became the de facto standard for this, which discards high-frequency interaural phase information in favor of reducing magnitude errors. Building upon this idea, we suggest Masked Magnitude Least Squares, which optimized the Ambisonics coefficients with a neural network and employs a spatio-spectral weighting mask to control the accuracy of the magnitude reconstruction. In the tested case, the weighting mask helped to maintain high-frequency notches in the low-order HRTFs and improved the modeled median plane localization performance in comparison to MagLS, while only marginally affecting the overall accuracy of the magnitude reconstruction.

Differentiable rendering has become a key ingredient in solving challenging inverse problems in computer graphics and vision. Existing systems can simulate and differentiate the spatial propagation of light. We exploit the duality of light transport simulations and geometric acoustics to apply differential rendering techniques to established acoustic simulation methods. The resulting system is capable of simulating sound according to the geometrical acoustics model and computing derivatives of the output energy spectrograms with respect to arbitrary parameters of the scene, including materials, emitters, microphones, and scene geometry. Contrary to current differentiable transient rendering, we can handle arbitrary simulation depths and achieve constant memory and linear execution times by presenting a temporal extension of Path Replay Backpropagation [Vicini et al. 2021]. We verify our model against established simulation software, and demonstrate the capabilities of optimization with gradients at examples of inverse acoustics and optimizing room parameters. This opens up a new field of research for acoustic optimization that could be as impactful for the acoustic community as differentiable rendering was for the graphics community.

Head-related transfer functions (HRTFs) are essential for virtual acoustic realities because they contain all cues for localizing sound sources in three-dimensional space. Acoustic measurements are one way to obtain high-quality HRTFs. To reduce measurement time, cost, and complexity of measurement systems, a promising approach is to capture only a few HRTFs on a sparse sampling grid and then upsample them to a dense HRTF set by interpolation. However, HRTF interpolation is challenging because small changes in source position can result in significant changes in the HRTF phase and magnitude response. Previous studies have greatly improved the interpolation by time-aligning the HRTFs in pre-processing, but magnitude interpolation errors remain a problem, especially in contralateral regions. Building on time-aligned interpolation, we propose a post-interpolation magnitude correction derived from a frequency-smoothed HRTF representation. Our technical evaluation based on 96 individual simulated HRTF sets shows that the magnitude correction reduces subject-averaged magnitude errors in the higher frequency range by up to 1.5 dB when averaged over all directions and by up to 4 dB in the contralateral region. As a result, interaural level differences in the upsampled HRTFs are also improved. The accompanying perceptual evaluation shows that the magnitude correction significantly reduces perceived coloration and results in a more stable and accurate perceived source position. Additional technical evaluations show that the proposed method outperforms current machine learning based algorithms, can be used with measured HRTFs, and is superior to using a dummy head HRTF set even when only six source positions are used for upsampling.

Numerical simulations offer a feasible alternative to the direct acoustic measurement of individual head-related transfer functions (HRTFs). For the acquisition of high quality 3D surface scans, as required for these simulations, several approaches exist. In this paper, we systematically analyze the variations between different approaches and evaluate the influence of the accuracy of 3D scans on the resulting simulated HRTFs. To assess this effect, HRTFs were numerically simulated based on 3D scans of the head and pinna of the FABIAN dummy head generated with 6 different methods. These HRTFs were analyzed in terms of interaural time difference, interaural level difference, energetic error in auditory filters and by their modeled localization performance. From the results, it is found that a geometric precision of about 1 mm is needed to maintain accurate localization cues, while a precision of about 4 mm is sufficient to maintain the overall spectral shape.

In this paper we present a novel approach for the description of speech rhythm and the extraction of rhythm-related features for automatic language identification (LID).Previous methods have extracted speech rhythm through the calculation of features based on salient elements of speech such as consonants, vowels and syllables.We present how an automatic rhythm extraction method borrowed from music information retrieval, the beat histogram, can be adapted for the analysis of speech rhythm by defining the most relevant novelty functions in the speech signal and extracting features describing their periodicities.We have evaluated those features in a rhythm-based LID task for two multilingual speech corpora using support vector machines, including feature selection methods to identify the most informative descriptors.Results suggest that the method is successful in describing speech rhythm and provides LID classification accuracy comparable to or better than that of other approaches, without the need for a preceding segmentation or annotation of the speech signal.Concerning rhythm typology, the rhythm class hypothesis in its original form seems to be only partly confirmed by our results.