Model and Deep learning based Dynamic Range Compression Inversion

• URL: http://arxiv.org/abs/2411.04337v1
• Date: Thu, 07 Nov 2024 00:33:07 GMT
• Title: Model and Deep learning based Dynamic Range Compression Inversion
• Authors: Haoran Sun, Dominique Fourer, Hichem Maaref,
• Abstract summary: 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.
• Score: 12.002024727237837
• License:
• Abstract: Dynamic Range Compression (DRC) is a popular audio effect used to control the dynamic range of a signal. Inverting DRC can also help to restore the original dynamics to produce new mixes and/or to improve the overall quality of the audio signal. Since, state-of-the-art DRC inversion techniques either ignore parameters or require precise parameters that are difficult to estimate, we fill the gap by combining a model-based approach with neural networks for DRC inversion. To this end, depending on the scenario, we use different neural networks to estimate DRC parameters. Then, a model-based inversion is completed to restore the original audio signal. Our experimental results show the effectiveness and robustness of the proposed method in comparison to several state-of-the-art methods, when applied on two music datasets.

Related papers

• 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)
• 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)
• Modality-Agnostic Variational Compression of Implicit Neural Representations [96.35492043867104]
  We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR) Bridging the gap between latent coding and sparsity, we obtain compact latent representations non-linearly mapped to a soft gating mechanism. After obtaining a dataset of such latent representations, we directly optimise the rate/distortion trade-off in a modality-agnostic space using neural compression.
  arXiv  Detail & Related papers  (2023-01-23T15:22:42Z)
• Synthetic Wave-Geometric Impulse Responses for Improved Speech Dereverberation [69.1351513309953]
  We show that accurately simulating the low-frequency components of Room Impulse Responses (RIRs) is important to achieving good dereverberation. We demonstrate that speech dereverberation models trained on hybrid synthetic RIRs outperform models trained on RIRs generated by prior geometric ray tracing methods.
  arXiv  Detail & Related papers  (2022-12-10T20:15:23Z)
• Conditional variational autoencoder to improve neural audio synthesis for polyphonic music sound [4.002298833349517]
  realtime audio variational autoencoder (RAVE) method was developed for high-quality audio waveform synthesis. We propose an enhanced RAVE model with a conditional variational autoencoder structure and an additional fully-connected layer. The proposed model exhibits a more significant performance and stability improvement than the conventional RAVE model.
  arXiv  Detail & Related papers  (2022-11-16T07:11:56Z)
• Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs [9.593925140084846]
  We propose a high-quality audio super-resolution method that can utilize unpaired data based on two connected cycle consistent generative adversarial networks (CycleGAN) Our method decomposes the super-resolution method into domain adaptation and resampling processes to handle acoustic mismatch in the unpaired low- and high-resolution signals. Experimental results verify that the proposed method significantly outperforms conventional methods when paired data are not available.
  arXiv  Detail & Related papers  (2022-10-28T04:32:59Z)
• Multi-stage image denoising with the wavelet transform [125.2251438120701]
  Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. We propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and residual block (RB)
  arXiv  Detail & Related papers  (2022-09-26T03:28:23Z)
• Active Restoration of Lost Audio Signals Using Machine Learning and Latent Information [0.7252027234425334]
  This paper proposes the combination of steganography, halftoning (dithering), and state-of-the-art shallow and deep learning methods. We show improvement in the inpainting performance in terms of signal-to-noise ratio (SNR), the objective difference grade (ODG) and Hansen's audio quality metric.
  arXiv  Detail & Related papers  (2021-11-21T20:11:33Z)
• Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover [87.28082715343896]
  We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
  arXiv  Detail & Related papers  (2021-10-20T06:15:45Z)
• Compute and memory efficient universal sound source separation [23.152611264259225]
  We provide a family of efficient neural network architectures for general purpose audio source separation. The backbone structure of this convolutional network is the SUccessive DOwnsampling and Resampling of Multi-Resolution Features (SuDoRM-RF) Our experiments show that SuDoRM-RF models perform comparably and even surpass several state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2021-03-03T19:16:53Z)
• Deep Networks for Direction-of-Arrival Estimation in Low SNR [89.45026632977456]
  We introduce a Convolutional Neural Network (CNN) that is trained from mutli-channel data of the true array manifold matrix. We train a CNN in the low-SNR regime to predict DoAs across all SNRs. Our robust solution can be applied in several fields, ranging from wireless array sensors to acoustic microphones or sonars.
  arXiv  Detail & Related papers  (2020-11-17T12:52:18Z)

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.