Download Dark Velvet Noise This paper proposes dark velvet noise (DVN) as an extension of the original velvet noise with a lowpass spectrum. The lowpass spectrum is achieved by allowing each pulse in the sparse sequence to have a randomized pulse width. The cutoff frequency is controlled by the density of the sequence. The modulated pulse-width can be implemented efficiently utilizing a discrete set of recursive running-sum filters, one for each unique pulse width. DVN may be used in reverberation algorithms. Typical room reverberation has a frequency-dependent decay, where the high frequencies decay faster than the low ones. A similar effect is achieved by lowering the density and increasing the pulse-width of DVN in time, thereby making the DVN suitable for artificial reverberation.
Download Multichannel Interleaved Velvet Noise The cross-correlation of multichannel reverberation generated using interleaved velvet noise is studied. The interleaved velvetnoise reverberator was proposed recently for synthesizing the late reverb of an acoustic space. In addition to providing a computationally efficient structure and a perceptually smooth response, the interleaving method allows combining its independent branch outputs in different permutations, which are all equally smooth and flutter-free. For instance, a four-branch output can be combined in 4! or 24 ways. Additionally, each branch output set is mixed orthogonally, which increases the number of permutations from M ! to M 2 !, since sign inversions are taken along. Using specific matrices for this operation, which change the sign of velvet-noise sequences, decreases the correlation of some of the combinations. This paper shows that many selections of permutations offer a set of well decorrelated output channels, which produce a diffuse and colorless sound field, which is validated with spatial variation. The results of this work can be applied in the design of computationally efficient multichannel reverberators.
Download Differentiable Feedback Delay Network for Colorless Reverberation Artificial reverberation algorithms often suffer from spectral coloration, usually in the form of metallic ringing, which impairs the perceived quality of sound. This paper proposes a method to reduce the coloration in the feedback delay network (FDN), a popular artificial reverberation algorithm. An optimization framework is employed entailing a differentiable FDN to learn a set of parameters decreasing coloration. The optimization objective is to minimize the spectral loss to obtain a flat magnitude response, with an additional temporal loss term to control the sparseness of the impulse response. The objective evaluation of the method shows a favorable narrower distribution of modal excitation while retaining the impulse response density. The subjective evaluation demonstrates that the proposed method lowers perceptual coloration of late reverberation, and also shows that the suggested optimization improves sound quality for small FDN sizes. The method proposed in this work constitutes an improvement in the design of accurate and high-quality artificial reverberation, simultaneously offering computational savings.
Download How Smooth Do You Think I Am: An Analysis on the Frequency-Dependent Temporal Roughness of Velvet Noise Velvet noise is a sparse pseudo-random signal, with applications in late reverberation modeling, decorrelation, speech generation, and extending signals. The temporal roughness of broadband velvet noise has been studied earlier. However, the frequency-dependency of the temporal roughness has little previous research. This paper explores which combinative qualities such as pulse density, filter type, and filter shape contribute to frequency-dependent temporal roughness. An adaptive perceptual test was conducted to find minimal densities of smooth noise at octave bands as well as corresponding lowpass bands. The results showed that the cutoff frequency of a lowpass filter as well as the center frequency of an octave filter is correlated with the perceived minimal density of smooth noise. When the lowpass filter with the lowest cutoff frequency, 125 Hz, was applied, the filtered velvet noise sounded smooth at an average of 725 pulses/s and an average of 401 pulses/s for octave filtered noise at a center frequency of 125 Hz. For the broadband velvet noise, the minimal density of smoothness was found to be at an average of 1554 pulses/s. The results of this paper are applicable in designing velvet-noise-based artificial reverberation with minimal pulse density.
Download Binaural Dark-Velvet-Noise Reverberator Binaural late-reverberation modeling necessitates the synthesis of frequency-dependent inter-aural coherence, a crucial aspect of spatial auditory perception. Prior studies have explored methodologies such as filtering and cross-mixing two incoherent late reverberation impulse responses to emulate the coherence observed in measured binaural late reverberation. In this study, we introduce two variants of the binaural dark-velvet-noise reverberator. The first one uses cross-mixing of two incoherent dark-velvet-noise sequences that can be generated efficiently. The second variant is a novel time-domain jitter-based approach. The methods’ accuracies are assessed through objective and subjective evaluations, revealing that both methods yield comparable performance and clear improvements over using incoherent sequences. Moreover, the advantages of the jitter-based approach over cross-mixing are highlighted by introducing a parametric width control, based on the jitter-distribution width, into the binaural dark velvet noise reverberator. The jitter-based approach can also introduce timedependent coherence modifications without additional computational cost.
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 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.