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Found 9 papers, 6 papers with code
Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations
Non-negative Matrix Factorization (NMF) is a powerful technique for analyzing regularly-sampled data, i. e., data that can be stored in a matrix.
Noise-Robust DSP-Assisted Neural Pitch Estimation with Very Low Complexity
Pitch estimation is an essential step of many speech processing algorithms, including speech coding, synthesis, and enhancement.
Unsupervised Improvement of Audio-Text Cross-Modal Representations
In this paper, we study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio.
End-to-end LPCNet: A Neural Vocoder With Fully-Differentiable LPC Estimation
Neural vocoders have recently demonstrated high quality speech synthesis, but typically require a high computational complexity.
Point Cloud Audio Processing
As a consequence, most audio machine learning models are designed to process fixed-size vector inputs which often prohibits the repurposing of learned models on audio with different sampling rates or alternative representations.
Optimizing Short-Time Fourier Transform Parameters via Gradient Descent
The Short-Time Fourier Transform (STFT) has been a staple of signal processing, often being the first step for many audio tasks.
HpRNet : Incorporating Residual Noise Modeling for Violin in a Variational Parametric Synthesizer
Generative Models for Audio Synthesis have been gaining momentum in the last few years.
VaPar Synth -- A Variational Parametric Model for Audio Synthesis
With the advent of data-driven statistical modeling and abundant computing power, researchers are turning increasingly to deep learning for audio synthesis.
Generative Audio Synthesis with a Parametric Model
Use a parametric representation of audio to train a generative model in the interest of obtaining more flexible control over the generated sound.
