This paper presents Motus, a new dataset of higherorder Ambisonic room impulse responses. The measurements took place in a single room while varying the amount and placement of furniture. 830 different room configurations were measured with four source-to-receiver configurations, resulting in 3320 room impulse responses in total. The dataset features various furniture object placements, including non-uniform distributions of absorptive material and cases with occluded direct paths between source and receiver. All acoustic measurements are accompanied by matching 3D models and 360 -photographs of the room. After describing the dataset, we demonstrate its usage with a reverberation time analysis. The analysis reveals that most of our measurements follow the expected relationship between absorption area and reverberation time. Some exceptional cases feature particular room acoustic phenomena, such as non-uniform absorption area distributions or multi-slope decays. Additionally, we show with a large number of measurements that furniture placement can significantly affect the reverberation time of a room. The dataset can be used to investigate room acoustic topics such as the acoustic effects of absorber placements or the decay behavior of rooms.
An established model for sound energy decay functions (EDFs) is the superposition of multiple exponentials and a noise term. This work proposes a neural-network-based approach for estimating the model parameters from EDFs. The network is trained on synthetic EDFs and evaluated on two large datasets of over 20 000 EDF measurements conducted in various acoustic environments. The evaluation shows that the proposed neural network architecture robustly estimates the model parameters from large datasets of measured EDFs while being lightweight and computationally efficient. An implementation of the proposed neural network is publicly available.
Spatial room impulse responses (SRIRs) capture room acoustics with directional information. SRIRs measured in coupled rooms and spaces with non-uniform absorption distribution may exhibit anisotropic reverberation decays and multiple decay slopes. However, noisy measurements with low signal-to-noise ratios pose issues in analysis and reproduction in practice. This paper presents a method for resynthesis of the late decay of anisotropic SRIRs, effectively removing noise from SRIR measurements. The method accounts for both multi-slope decays and directional reverberation. A spherical filter bank extracts directionally constrained signals from Ambisonic input, which are then analyzed and parameterized in terms of multiple exponential decays and a noise floor. The noisy late reverberation is then resynthesized from the estimated parameters using modal synthesis, and the restored SRIR is reconstructed as Ambisonic signals. The method is evaluated both numerically and perceptually, which shows that SRIRs can be denoised with minimal error as long as parts of the decay slope are above the noise level, with signal-to-noise ratios as low as 40 dB in the presented experiment. The method can be used to increase the perceived spatial audio quality of noise-impaired SRIRs.
Whilst room acoustic measurements can accurately capture the sound field of real rooms, they are usually time consuming and tedious if many positions need to be measured. Therefore, this contribution presents the Autonomous Robot Twin System for Room Acoustic Measurements (ARTSRAM) to autonomously capture large sets of room impulse responses with variable sound source and receiver positions. The proposed implementation of the system consists of two robots, one of which is equipped with a loudspeaker, while the other one is equipped with a microphone array. Each robot contains collision sensors, thus enabling it to move autonomously within the room. The robots move according to a random walk procedure to ensure a big variability between measured positions. A tracking system provides position data matching the respective measurements. After outlining the robot system, this paper presents a validation, in which anechoic responses of the robots are presented and the movement paths resulting from the random walk procedure are investigated. Additionally, the quality of the obtained room impulse responses is demonstrated with a sound field visualization. In summary, the evaluation of the robot system indicates that large sets of diverse and high-quality room impulse responses can be captured with the system in an automated way. Such large sets of measurements will benefit research in the fields of room acoustics and acoustic virtual reality.
This work suggests a method of presenting information about the acoustical and geometric properties of a room as spherical images to a machine-learning algorithm to estimate acoustical parameters of the room. The approach has the advantage that the spatial distribution of the properties can be presented in a generic and potentially compact way to machine learning methods. The estimation of reverberation time T 60 is used as a proof-of-concept study here. The distribution of absorptive material is presented as a spherical map of feature values, in which each value is formed by calculating the equivalent absorption area visible through the corresponding facet of a polyhedron as seen from the polyhedron's center point. The pixel values are then used as feature vectors, and the real measured T 60 values of corresponding rooms are used as target data. This work presents the method and trains a set of neural networks with di erent spherical map resolutions using a dataset composed of real-world acoustical measurements of a single room with 831 di erent configurations of furniture and absorptive materials. The estimation of reverberation time using the proposed approach exhibits a much higher accuracy compared to simple analytic methods, which proves the validity of the approach.
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