Current methods for immersive playback of spatial sound content aim at flexibility in terms of encoding and decoding, abstracting the two from the recording or playback setup. Ambisonics constitutes such a method, that is however signal-independent, and at low spatial resolutions fails to provide appropriate spatialization cues to the listener, with potential severe colouration effects and localization ambiguity. We present a new signal-dependent method for parametric analysis and synthesis of ambisonic sound scenes that takes advantage of the flexibility of Ambisonics as a spatial audio format, while improving reproduction. The proposed approach considers a more general acoustic model than previous proposals, with multiple source signals and a non isotropic ambient component. According to a listening test using headphones, the method is perceived closer to binaural reference sound scenes than ambisonic playback.

We study the detection and estimation of the parameters related to the deterministic model of the reflections of room impulse responses measured with a spherical microphone array. More specifically, we investigate the problem of detecting and estimating several reflections of a single snapshot of data in the spherical harmonic (SH) domain with four detection and four estimation methods, presented previously in the array processing research. Three of the estimation methods are based on Bayesian Maximum a Posteriori (MAP) estimation, and they employ a prior normal distribution on the signals. The estimation methods are compared with the deterministic maximum likelihood (DML) method [Tervo & Politis 2015]. In the detection task, two information criteria-based methods, minimum description length (MDL) and Akaike information criteria (AIC), a normalized likelihood (NL) based method, and a Bayesian detection scheme (BDS) are explored. The experiments study the performance of the methods with simulated and real data experiments. The simulation results show that the MAP estimation methods have a lower root mean squared error (RMSE) than DML in the reflection signal amplitude estimation, but all three have similar performance in the direction of arrival (DOA) and noise variance estimation, although in general RMSE of DOA estimation of MAP methods is slightly lower that that of DML. In addition, in the detection task, the BDS and AIC are the most robust against additive noise, and NL and AIC have the best detection rate when more than two reflections are simulated. The results of the real data experiments show that all the estimation methods have similar performance for DOA and noise variance estimation, while the Bayesian MAP methods have a clearly lower RMSE for reflection signal amplitude estimation than DML. In total, NL has the highest detection rate in the real data experiments.

This paper studies the direction of arrival estimation of reflections in short time windows of room impulse responses measured with a spherical microphone array. Spectral-based methods, such as multiple signal classification (MUSIC) and beamforming, are commonly used in the analysis of spatial room impulse responses. However, the room acoustic reflections are highly correlated or even coherent in a single analysis window and this imposes limitations on the use of spectral-based methods. Here, we apply maximum likelihood (ML) methods, which are suitable for direction of arrival estimation of coherent reflections. These methods have been earlier developed in the linear space domain and here we present the ML methods in the context of spherical microphone array processing and room impulse responses. Experiments are conducted with simulated and real data using the em32 Eigenmike. The results show that direction estimation with ML methods is more robust against noise and less biased than MUSIC or beamforming.

Estimation of the room geometry from spatial room impulse responses is studied. An algorithm for estimating the geometry is presented. The algorithm does not require any a priori information on the room shape, number of walls, or order of the reflections, but deduces the set of planes that explain the measured source and image-source locations and covariances iteratively. The algorithm is demonstrated with real data experiments.

The geometry of an enclosure is of interest for example when synthesizing impulse responses or when studying concert halls. Recently, several approaches for estimation of the room geometry, or the reflective surfaces, have been proposed. These approaches use a priori information of the source signal for the room geometry estimation, typically, impulse response measurements. Here, a method for estimating the reflective surfaces from continuous signals, such as speech or music, is proposed. The method is based on inverse mapping of the acoustic multi-path propagation problem. The validity of the method is demonstrated in a real auditorium. With reasonable signal-to-noise ratio the proposed algorithm has less than 0.05 m error in the position of the point of reflection and less than 1 degree of error in the direction of the normal of the surface.