Evaluating Neural Networks Architectures for Spring Reverb Modelling

• URL: http://arxiv.org/abs/2409.04953v1
• Date: Sun, 8 Sep 2024 02:37:42 GMT
• Title: Evaluating Neural Networks Architectures for Spring Reverb Modelling
• Authors: Francesco Papaleo, Xavier Lizarraga-Seijas, Frederic Font,
• Abstract summary: The electromechanical functioning of the spring reverb makes it a nonlinear system that is difficult to fully emulate in the digital domain. We compare five different neural network architectures, including convolutional and recurrent models, to assess their effectiveness in replicating the characteristics of this audio effect. This paper specifically focuses on neural audio architectures that offer parametric control, aiming to advance the boundaries of current black-box modelling techniques in the domain of spring reverberation.
• Score: 0.21847754147782888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reverberation is a key element in spatial audio perception, historically achieved with the use of analogue devices, such as plate and spring reverb, and in the last decades with digital signal processing techniques that have allowed different approaches for Virtual Analogue Modelling (VAM). The electromechanical functioning of the spring reverb makes it a nonlinear system that is difficult to fully emulate in the digital domain with white-box modelling techniques. In this study, we compare five different neural network architectures, including convolutional and recurrent models, to assess their effectiveness in replicating the characteristics of this audio effect. The evaluation is conducted on two datasets at sampling rates of 16 kHz and 48 kHz. This paper specifically focuses on neural audio architectures that offer parametric control, aiming to advance the boundaries of current black-box modelling techniques in the domain of spring reverberation.

Related papers

• Model and Deep learning based Dynamic Range Compression Inversion [12.002024727237837]
  Inverting DRC can help to restore the original dynamics to produce new mixes and/or to improve the overall quality of the audio signal. We propose a model-based approach with neural networks for DRC inversion. Our results show the effectiveness and robustness of the proposed method in comparison to several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-07T00:33:07Z)
• A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures [73.65190161312555]
  ARCANA is a spiking neural network simulator designed to account for the properties of mixed-signal neuromorphic circuits. We show how the results obtained provide a reliable estimate of the behavior of the spiking neural network trained in software.
  arXiv  Detail & Related papers  (2024-09-23T11:16:46Z)
• Modeling Time-Variant Responses of Optical Compressors with Selective State Space Models [0.0]
  This paper presents a method for modeling optical dynamic range compressors using deep neural networks with Selective State Space models. It features a refined technique integrating Feature-wise Linear Modulation and Gated Linear Units to adjust the network dynamically. The proposed architecture is well-suited for low-latency and real-time applications, crucial in live audio processing.
  arXiv  Detail & Related papers  (2024-08-22T17:03:08Z)
• Model-Based Qubit Noise Spectroscopy [0.0]
  We derive model-based QNS approaches using inspiration from classical signal processing. We show, through both simulation and experimental data, how these model-based QNS approaches maintain the statistical and computational benefits of their classical counterparts.
  arXiv  Detail & Related papers  (2024-05-20T09:30:38Z)
• Comparative Study of State-based Neural Networks for Virtual Analog Audio Effects Modeling [0.0]
  This article explores the application of machine learning advancements for virtual analog modeling. We compare State-Space models and Linear Recurrent Units against the more common Long Short-Term Memory networks.
  arXiv  Detail & Related papers  (2024-05-07T08:47:40Z)
• Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark [65.79402756995084]
  Real Acoustic Fields (RAF) is a new dataset that captures real acoustic room data from multiple modalities. RAF is the first dataset to provide densely captured room acoustic data.
  arXiv  Detail & Related papers  (2024-03-27T17:59:56Z)
• From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion [84.138804145918]
  Deep generative models can generate high-fidelity audio conditioned on various types of representations. These models are prone to generate audible artifacts when the conditioning is flawed or imperfect. We propose a high-fidelity multi-band diffusion-based framework that generates any type of audio modality from low-bitrate discrete representations.
  arXiv  Detail & Related papers  (2023-08-02T22:14:29Z)
• On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals [104.11663769306566]
  We study the single-channel source separation problem involving frequency-division multiplexing (OFDM) signals. We propose critical domain-informed modifications to the network parameterization, based on insights from OFDM structures.
  arXiv  Detail & Related papers  (2023-03-11T16:29:13Z)
• Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain [131.74762114632404]
  The model is trained end-to-end and performs spatial processing implicitly. We evaluate the proposed model on a real-world dataset and show that the model matches the performance of an oracle beamformer.
  arXiv  Detail & Related papers  (2022-06-30T17:13:01Z)
• Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search [63.39818029362661]
  We present a model based on Neural Architecture Search (NAS) and self-learning for received signal strength ( RSS) map reconstruction. The approach first finds an optimal NN architecture and simultaneously train the deduced model over some ground-truth measurements of a given ( RSS) map. Experimental results show that signal predictions of this second model outperforms non-learning based state-of-the-art techniques and NN models with no architecture search.
  arXiv  Detail & Related papers  (2021-05-17T12:19:22Z)
• Exploring Quality and Generalizability in Parameterized Neural Audio Effects [0.0]
  Deep neural networks have shown promise for music audio signal processing applications. Results to date have tended to be constrained by low sample rates, noise, narrow domains of signal types, and/or lack of parameterized controls. This work expands on prior research published on modeling nonlinear time-dependent signal processing effects.
  arXiv  Detail & Related papers  (2020-06-10T00:52:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.