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Content Adaptive Front End For Audio Signal Processing
In this work, we propose a way of computing a content-adaptive learnable time-frequency representation.
One-Shot Acoustic Matching Of Audio Signals -- Learning to Hear Music In Any Room/ Concert Hall
Typically, researchers use an excitation such as a pistol shot or balloon pop as an impulse signal with which an auralization can be created.
A Generative Model for Raw Audio Using Transformer Architectures
We show how causal transformer generative models can be used for raw waveform synthesis.
A Deep Learning Approach for Low-Latency Packet Loss Concealment of Audio Signals in Networked Music Performance Applications
Networked Music Performance (NMP) is envisioned as a potential game changer among Internet applications: it aims at revolutionizing the traditional concept of musical interaction by enabling remote musicians to interact and perform together through a telecommunication network.
Neuralogram: A Deep Neural Network Based Representation for Audio Signals
We propose the Neuralogram -- a deep neural network based representation for understanding audio signals which, as the name suggests, transforms an audio signal to a dense, compact representation based upon embeddings learned via a neural architecture.
