Sound reproduction systems may highly benefit from detailed knowledge of the acoustic space to enhance the spatial sound experience. This article presents a room geometry inference method based on identification of reflective boundaries using a high-resolution direction-of-arrival map produced via room impulse responses (RIRs) measured with a linear loudspeaker array and a single microphone. Exploiting the sparse nature of the early part of the RIRs, Elastic Net regularization is applied to obtain a 2D polar-coordinate map, on which the direct path and early reflections appear as distinct peaks, described by their propagation distance and direction of arrival. Assuming a separable room geometry with four side-walls perpendicular to the floor and ceiling, and imposing pre-defined geometrical constraints on the walls, the 2D-map is segmented into six regions, each corresponding to a particular wall. The salient peaks within each region are selected as candidates for the first-order wall reflections, and a set of potential room geometries is formed by considering all possible combinations of the associated peaks. The room geometry is then inferred using a cost function evaluated on the higher-order reflections computed via beam tracing. The proposed method is tested with both simulated and measured data.

Human perception of room acoustics depends among others on the time of transition from early reflections to late reverberation in room impulse responses, which is known as mixing time. In this letter, a multi-channel mixing time prediction method is proposed, which in contrast to state-of-the-art channel-based predictors accounts for spatiotemporal properties of the sound field. The proposed diffuseness-based method is compared with existing model-and channel-based prediction methods through measurements and acoustic simulations, and is shown to correlate well with the perceptual mixing time. Furthermore, insights into relations between prediction methods and mixing time definitions based on reflection density are presented

Previously proposed methods for estimating acoustic parameters from reverberant, noisy speech signals exhibit insufficient performance under changing acoustic conditions. A data-centric approach is proposed to overcome the limiting assumption of fixed source–receiver transmission paths. The obtained solution significantly enlarges the scope of potential applications for such estimators. The joint estimation of reverberation time RT60 and clarity index C50 in multiple frequency bands is studied with a focus on dynamic acoustic environments. Three different convolutional recurrent neural network architectures are considered to solve the tasks of single-band, multi-band, and multi-task parameter estimation. A comprehensive performance evaluation is provided that highlights the benefits of the proposed approach.

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Multi-point room equalization (EQ) aims to achieve a desired sound quality within a wider listening area than single-point EQ. However, multi-point EQ necessitates the measurement of multiple room impulse responses at a listener position, which may be a laborious task for an end-user. This article presents a data-driven method that estimates a spatially averaged room transfer function (RTF) from a single-point RTF in the low-frequency region. A deep neural network (DNN) is trained using only simulated RTFs and tested with both simulated and measured RTFs. It is demonstrated that the DNN learns a spatial smoothing operation: notches across the spectrum are smoothed out while the peaks of the single-point RTF are preserved. An EQ framework based on a finite impulse response filter is used to evaluate the room EQ performance. The results show that while not fully reaching the level of multi-point EQ performance, the proposed data-driven local average RTF estimation method generally brings improvement over single-point EQ.