135 citations · 190 across the 16 of their papers we have counts for
16 papers
SCAPES: Semantically Conditioned Autoregressive Prior for Environmental Sounds
Esteban Gutiérrez, Lonce Wyse, Frederic Font +1
As generative audio models grow in complexity, the computational and ecological costs of synthesizing everyday sounds have become increasingly prohibitive, often requiring industri…
Physics-Informed Neural Engine Sound Modeling with Differentiable Pulse-Train Synthesis
Robin Doerfler, Lonce Wyse
Engine sounds originate from sequential exhaust pressure pulses rather than sustained harmonic oscillations. While neural synthesis methods typically aim to approximate the resulti…
Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations
Robin Doerfler, Lonce Wyse
Computational engine sound modeling is central to the automotive audio industry, particularly for active sound design applications and virtual prototyping. Emerging data-driven eng…
A Statistics-Driven Differentiable Approach for Sound Texture Synthesis and Analysis
Esteban Gutiérrez, Frederic Font, Xavier Serra +1
In this work, we introduce TexStat, a novel loss function specifically designed for the analysis and synthesis of texture sounds characterized by stochastic structure and perceptua…
Example-Based Framework for Perceptually Guided Audio Texture Generation
Purnima Kamath, Chitralekha Gupta, Lonce Wyse +1
Controllable generation using StyleGANs is usually achieved by training the model using labeled data. For audio textures, however, there is currently a lack of large semantically l…
Towards Controllable Audio Texture Morphing
Chitralekha Gupta, Purnima Kamath, Yize Wei +3
In this paper, we propose a data-driven approach to train a Generative Adversarial Network (GAN) conditioned on "soft-labels" distilled from the penultimate layer of an audio class…
Parameter Sensitivity of Deep-Feature based Evaluation Metrics for Audio Textures
Chitralekha Gupta, Yize Wei, Zequn Gong +3
Standard evaluation metrics such as the Inception score and Fréchet Audio Distance provide a general audio quality distance metric between the synthesized audio and reference clean…
Sound Model Factory: An Integrated System Architecture for Generative Audio Modelling
Lonce Wyse, Purnima Kamath, Chitralekha Gupta
We introduce a new system for data-driven audio sound model design built around two different neural network architectures, a Generative Adversarial Network(GAN) and a Recurrent Ne…
Signal Representations for Synthesizing Audio Textures with Generative Adversarial Networks
Chitralekha Gupta, Purnima Kamath, Lonce Wyse
Generative Adversarial Networks (GANs) currently achieve the state-of-the-art sound synthesis quality for pitched musical instruments using a 2-channel spectrogram representation c…
MTCRNN: A multi-scale RNN for directed audio texture synthesis
M. Huzaifah, L. Wyse
Audio textures are a subset of environmental sounds, often defined as having stable statistical characteristics within an adequately large window of time but may be unstructured lo…
Deep generative models for musical audio synthesis
M. Huzaifah, L. Wyse
Sound modelling is the process of developing algorithms that generate sound under parametric control. There are a few distinct approaches that have been developed historically incl…
Mechanisms of Artistic Creativity in Deep Learning Neural Networks
Lonce Wyse
The generative capabilities of deep learning neural networks (DNNs) have been attracting increasing attention for both the remarkable artifacts they produce, but also because of th…
Conditioning a Recurrent Neural Network to synthesize musical instrument transients
Lonce Wyse, Muhammad Huzaifah
A recurrent Neural Network (RNN) is trained to predict sound samples based on audio input augmented by control parameter information for pitch, volume, and instrument identificatio…
Applying Visual Domain Style Transfer and Texture Synthesis Techniques to Audio - Insights and Challenges
M. Huzaifah, L. Wyse
Style transfer is a technique for combining two images based on the activations and feature statistics in a deep learning neural network architecture. This paper studies the analog…
Real-valued parametric conditioning of an RNN for interactive sound synthesis
Lonce Wyse
A Recurrent Neural Network (RNN) for audio synthesis is trained by augmenting the audio input with information about signal characteristics such as pitch, amplitude, and instrument…
Audio Spectrogram Representations for Processing with Convolutional Neural Networks
L. Wyse
One of the decisions that arise when designing a neural network for any application is how the data should be represented in order to be presented to, and possibly generated by, a…