Download Differentiable Active Acoustics - Optimizing Stability via Gradient Descent Active acoustics (AA) refers to an electroacoustic system that actively modifies the acoustics of a room. For common use cases, the number of transducers—loudspeakers and microphones—involved in the system is large, resulting in a large number of system parameters. To optimally blend the response of the system into the natural acoustics of the room, the parameters require careful tuning, which is a time-consuming process performed by an expert. In this paper, we present a differentiable AA framework, which allows multi-objective optimization without impairing architecture flexibility. The system is implemented in PyTorch to be easily translated into a machine-learning pipeline, thus automating the tuning process. The objective of the pipeline is to optimize the digital signal processor (DSP) component to evenly distribute the energy in the feedback loop across frequencies. We investigate the effectiveness of DSPs composed of finite impulse response filters, which are unconstrained during the optimization. We study the effect of multiple filter orders, number of transducers, and loss functions on the performance. Different loss functions behave similarly for systems with few transducers and low-order filters. Increasing the number of transducers and the order of the filters improves results and accentuates the difference in the performance of the loss functions.
Download Neural Net Tube Models for Wave Digital Filters Herein, we demonstrate the use of neural nets towards simulating multiport nonlinearities inside a wave digital filter. We introduce a resolved wave definition which allows us to extract features from a Kirchhoff domain dataset and train our neural networks directly in the wave domain. A hyperparameter search is performed to minimize error and runtime complexity. To illustrate the method, we model a tube amplifier circuit inspired by the preamplifier stage of the Fender Pro-Junior guitar amplifier. We analyze the performance of our neural nets models by comparing their distortion characteristics and transconductances. Our results suggest that activation function selection has a significant effect on the distortion characteristic created by the neural net.
Download Neural Sample-Based Piano Synthesis Piano sound emulation has been an active topic of research and development for several decades. Although comprehensive physicsbased piano models have been proposed, sample-based piano emulation is still widely utilized for its computational efficiency and
relative accuracy despite presenting significant memory storage
requirements. This paper proposes a novel hybrid approach to
sample-based piano synthesis aimed at improving the fidelity of
sound emulation while reducing memory requirements for storing samples. A neural network-based model processes the sound
recorded from a single example of piano key at a given velocity.
The network is trained to learn the nonlinear relationship between
the various velocities at which a piano key is pressed and the corresponding sound alterations. Results show that the method achieves
high accuracy using a specific neural architecture that is computationally efficient, presenting few trainable parameters, and it requires memory only for one sample for each piano key.
Download A parallel 3D digital wave guide mesh model with tetrahedral topology for room acoustic simulation Following a summary of the basic principles of 3D waveguide mesh modelling and the context of its application to room acoustic simulation, this paper presents a detailed analysis of the tetrahedral mesh topology and describes its implementation on a parallel computer model. Its structural characteristics are analysed, with particular emphasis on how they influence execution speed. Performance deterioration due to communication overhead in the parallelised model is discussed. Theoretical predictions are compared with data from performance tests carried out on different computer platforms and both are contrasted with the corresponding results from the rectilinear model, in order to assess the practical efficiency of the model. Objective validation tests are reported and discussed.
Download Improving the robustness of the iterative solver in state-space modelling of guitar distortion circuitry Iterative solvers are required for the discrete-time simulation of nonlinear behaviour in analogue distortion circuits. Unfortunately, these methods are often computationally too expensive for realtime simulation. Two methods are presented which attempt to reduce the expense of iterative solvers. This is achieved by applying information that is derived from the specific form of the nonlinearity. The approach is first explained through the modelling of an asymmetrical diode clipper, and further exemplified by application to the Dallas Rangemaster Treble Booster guitar pedal, which provides an initial perspective of the performance on systems with multiple nonlinearities.
Download Guitar Preamp Simulation Using Connection Currents This paper deals with a method of decomposition of a nonlinear audio circuit based on so called connection currents. These currents are used to connect inner blocks of the audio circuit with regards to preserve mutual interaction between adjoined blocks. Although this approach requires usage of numerical algorithm to solve the nonlinear equations, it reduces number of nonlinear equations to be solved if the solution of inner blocks is approximated while the accuracy of simulation is comparable to numerical solution of the whole nonlinear audio circuit.
Download On the Impact of Ground Sound Rigid-body impact sound synthesis methods often omit the ground sound. In this paper we analyze an idealized ground-sound model based on an elastodynamic halfspace, and use it to identify scenarios wherein ground sound is perceptually relevant versus when it is masked by the impacting object’s modal sound or transient acceleration noise. Our analytical model gives a smooth, closed-form expression for ground surface acceleration, which we can then use in the Rayleigh integral or in an “acoustic shader” for a finite-difference time-domain wave simulation. We find that when modal sound is inaudible, ground sound is audible in scenarios where a dense object impacts a soft ground and scenarios where the impact point has a low elevation angle to the listening point.
Download Detecting arrivals within room impulse responses using matching pursuit This paper proposes to use Matching Pursuit, in order to investigate some statistical foundations of Room Acoustics, such as the temporal distribution of arrivals, and the estimation of mixing time. As this has never been experimentally explored, this study is a first step towards a validation of the ergodic theory of reverberation. The use of Matching Pursuit is implicit, since correlation between the impulse response and the direct sound is assumed. The compensation for the energy decay is necessary to obtain stationnary signals. Methods for determining the best the temporal boundaries of the direct sound, for choosing an appropriate stopping criteria based on the similarity between acoustical indices of the original RIR and those of the synthesized signal, and for experimentally defining the mixing time constitute the scope of this study.
Download Differentiable Scattering Delay Networks for Artificial Reverberation Scattering delay networks (SDNs) provide a flexible and efficient
framework for artificial reverberation and room acoustic modeling. In this work, we introduce a differentiable SDN, enabling
gradient-based optimization of its parameters to better approximate the acoustics of real-world environments. By formulating
key parameters such as scattering matrices and absorption filters
as differentiable functions, we employ gradient descent to optimize an SDN based on a target room impulse response. Our approach minimizes discrepancies in perceptually relevant acoustic
features, such as energy decay and frequency-dependent reverberation times. Experimental results demonstrate that the learned SDN
configurations significantly improve the accuracy of synthetic reverberation, highlighting the potential of data-driven room acoustic modeling.
Download Recognition of Distance Cues from a Virtual Spatialization Model Emerging issues in the auditory display aim at increasing the usability of interfaces. In this paper we present a virtual resonating environment, which synthesizes distance cues by means of reverberation. We realize a model that recreates the acoustics inside a tube, applying a numerical scheme called Waveguide Mesh, and we present the psychophysical experiments we have conducted for validating the information about distance conveyed by the virtual environment.