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Found 13 papers, 8 papers with code
Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models
We describe a novel approach for developing realistic digital models of dynamic range compressors for digital audio production by analyzing their analog prototypes.
ATGNN: Audio Tagging Graph Neural Network
Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging.
Modulation Extraction for LFO-driven Audio Effects
Low frequency oscillator (LFO) driven audio effects such as phaser, flanger, and chorus, modify an input signal using time-varying filters and delays, resulting in characteristic sweeping or widening effects.
Leveraging Neural Representations for Audio Manipulation
We investigate applying audio manipulations using pretrained neural network-based autoencoders as an alternative to traditional signal processing methods, since the former may provide greater semantic or perceptual organization.
Modelling black-box audio effects with time-varying feature modulation
Deep learning approaches for black-box modelling of audio effects have shown promise, however, the majority of existing work focuses on nonlinear effects with behaviour on relatively short time-scales, such as guitar amplifiers and distortion.
HEAR: Holistic Evaluation of Audio Representations
The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios.
Steerable discovery of neural audio effects
Applications of deep learning for audio effects often focus on modeling analog effects or learning to control effects to emulate a trained audio engineer.
Direct design of biquad filter cascades with deep learning by sampling random polynomials
In this work, we address some of these limitations by learning a direct mapping from the target magnitude response to the filter coefficient space with a neural network trained on millions of random filters.
WaveBeat: End-to-end beat and downbeat tracking in the time domain
In this work, we propose WaveBeat, an end-to-end approach for joint beat and downbeat tracking operating directly on waveforms.
Filtered Noise Shaping for Time Domain Room Impulse Response Estimation From Reverberant Speech
Deep learning approaches have emerged that aim to transform an audio signal so that it sounds as if it was recorded in the same room as a reference recording, with applications both in audio post-production and augmented reality.
Efficient neural networks for real-time modeling of analog dynamic range compression
Deep learning approaches have demonstrated success in modeling analog audio effects.
Automatic multitrack mixing with a differentiable mixing console of neural audio effects
Applications of deep learning to automatic multitrack mixing are largely unexplored.
Audio and Speech Processing Sound
Randomized Overdrive Neural Networks
By processing audio signals in the time-domain with randomly weighted temporal convolutional networks (TCNs), we uncover a wide range of novel, yet controllable overdrive effects.
