Download Higher-Order Scattering Delay Networksfor Artificial Reverberation Computer simulations of room acoustics suffer from an efficiency vs accuracy trade-off, with highly accurate wave-based models being highly computationally expensive, and delay-network-based models lacking in physical accuracy. The Scattering Delay Network (SDN) is a highly efficient recursive structure that renders first order reflections exactly while approximating higher order ones. With the purpose of improving the accuracy of SDNs, in this paper, several variations on SDNs are investigated, including appropriate node placement for exact modeling of higher order reflections, redesigned scattering matrices for physically-motivated scattering, and pruned network connections for reduced computational complexity. The results of these variations are compared to state-of-the-art geometric acoustic models for different shoebox room simulations. Objective measures (Normalized Echo Densities (NEDs) and Energy Decay Curves (EDCs)) showed a close match between the proposed methods and the references. A formal listening test was carried out to evaluate differences in perceived naturalness of the synthesized Room Impulse Responses. Results show that increasing SDNs’ order and adding directional scattering in a fully-connected network improves perceived naturalness, and higher-order pruned networks give similar performance at a much lower computational cost.
Download Perceptual Evaluation and Genre-specific Training of Deep Neural Network Models of a High-gain Guitar Amplifier Modelling of analogue devices via deep neural networks (DNNs) has gained popularity recently, but their performance is usually measured using accuracy measures alone. This paper aims to assess the performance of DNN models of a high-gain vacuum-tube guitar amplifier using additional subjective measures, including preference and realism. Furthermore, the paper explores how the performance changes when genre-specific training data is used. In five listening tests, subjects rated models of a popular high-gain guitar amplifier, the Peavey 6505, in terms of preference, realism and perceptual accuracy. Two DNN models were used: a long short-term memory recurrent neural network (LSTM-RNN) and a WaveNet-based convolutional neural network (CNN). The LSTMRNN model was shown to be more accurate when trained with genre-specific data, to the extent that it could not be distinguished from the real amplifier in ABX tests. Despite minor perceptual inaccuracies, subjects found all models to be as realistic as the target in MUSHRA-like experiments, and there was no evidence to suggest that the real amplifier was preferred to any of the models in a mix. Finally, it was observed that a low-gain excerpt was more difficult to emulate, and was therefore useful to reveal differences between the models.
Download Naturalness of Double-Slope Decay in Generalised Active Acoustic Enhancement Systems Active acoustic enhancement systems (AAESs) alter the perceived acoustics of a space by using microphones and loudspeakers to introduce sound energy into the room. Double-sloped energy decay may be observed in these systems. However, it is unclear as to which conditions lead to this effect, and to what extent double sloping reduces the perceived naturalness of the reverberation compared to Sabine decay. This paper uses simulated combinations of AAES parameters to identify which cases affect the objective curvature of the energy decay. A subjective test with trained listeners assessed the naturalness of these conditions. Using an AAES model, room impulse responses were generated for varying room dimensions, absorption coefficients, channel counts, system loop gains and reverberation times (RTs) of the artificial reverberator. The objective double sloping was strongly correlated to the ratio between the reverberator and passive room RTs, but parameters such as absorption and room size did not have a profound effect on curvature. It was found that double sloping significantly reduced the perceived naturalness of the reverberation, especially when the reverberator RT was greater than two times that of the passive room. Double sloping had more effect on the naturalness ratings when subjects listened to a more absorptive passive room, and also when using speech rather than transient stimuli. Lowering the loop gain by 9 dB increased the naturalness of the doublesloped stimuli, where some were rated as significantly more natural than the Sabine decay stimuli from the passive room.
Download A Common-Slopes Late Reverberation Model Based on Acoustic Radiance Transfer In rooms with complex geometry and uneven distribution of energy losses, late reverberation depends on the positions of sound sources and listeners. More precisely, the decay of energy is characterised by a sum of exponential curves with position-dependent amplitudes and position-independent decay rates (hence the name common slopes). The amplitude of different energy decay components is a particularly important perceptual aspect that requires efficient modeling in applications such as virtual reality and video games. Acoustic Radiance Transfer (ART) is a room acoustics model focused on late reverberation, which uses a pre-computed acoustic transfer matrix based on the room geometry and materials, and allows interactive changes to source and listener positions. In this work, we present an efficient common-slopes approximation of the ART model. Our technique extracts common slopes from ART using modal decomposition, retaining only the non-oscillating energy modes. Leveraging the structure of ART, changes to the positions of sound sources and listeners only require minimal processing. Experimental results show that even very few slopes are sufficient to capture the positional dependency of late reverberation, reducing model complexity substantially.