Networked microcontrollers for accessible, distributed spatial audio
State-of-the-art systems for spatial and immersive audio are typically very costly, being reliant on specialist audio hardware capable of performing computationally intensive signal processing and delivering output to many tens, if not hundreds, of ...
Thomas Albert Rushton +4 more
doaj +1 more source
Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead
Advancements in audio synthesis and manipulation technologies have reshaped applications such as personalised virtual assistants, voice cloning for creative content, and language learning tools.
Bowen Zhang +3 more
doaj +1 more source
Affordances and Constraints in Interactive Audio / Visual Systems [PDF]
Nuno Correia, Raul Masu
doaj +1 more source
Spoof detection with dynamic learnable sparse attention and tri-modal fusion in resource-constrained audio systems. [PDF]
Wang X, Tan Z, Li G.
europepmc +1 more source
A modern audio reproduction system comprises of one input block, the input signal, and three hardware blocks, the power supply, the power amplifier and the loudspeaker. The system is meant to handle an infinite combination of sine waves within the audible range, 20 Hz to 20 kHz, with a dynamic range of up to 120 dB. Being able to do this with low Total
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A grounded study in user preference for immersive sound in extended reality environments. [PDF]
Anderson CSW, Thorogood M.
europepmc +1 more source
Talking Head Generation Through Generative Models and Cross-Modal Synthesis Techniques. [PDF]
Nisar H, Masood S, Malik Z, Abid A.
europepmc +1 more source
Depression detection using deep learning and large language models from multimodalities. [PDF]
Hussain Y, Zaheer MA, Khan AM, Malik AS.
europepmc +1 more source
The First Cadenza Challenge: Perceptual Evaluation of Machine Learning Systems to Improve Audio Quality of Popular Music for Those with Hearing Loss. [PDF]
Bannister S +10 more
europepmc +1 more source
eXCube2: Explainable Brain-Inspired Spiking Neural Network Framework for Emotion Recognition from Audio, Visual and Multimodal Audio-Visual Data. [PDF]
Kasabov NK +5 more
europepmc +1 more source

