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Found 10 papers, 3 papers with code
Should Audio Front-ends be Adaptive? Comparing Learnable and Adaptive Front-ends
Hand-crafted features, such as Mel-filterbanks, have traditionally been the choice for many audio processing applications.
Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features
Additionally, we introduce FOA-Conv3D, a novel back-end network for effectively utilising the SSCV feature with a 3D convolutional encoder.
Dual-Constrained Dynamical Neural ODEs for Ambiguity-aware Continuous Emotion Prediction
There has been a significant focus on modelling emotion ambiguity in recent years, with advancements made in representing emotions as distributions to capture ambiguity.
Binaural Selective Attention Model for Target Speaker Extraction
The remarkable ability of humans to selectively focus on a target speaker in cocktail party scenarios is facilitated by binaural audio processing.
An Exploration of Length Generalization in Transformer-Based Speech Enhancement
In this paper, we conduct comprehensive experiments to explore the length generalization problem in speech enhancement with Transformer.
Mamba in Speech: Towards an Alternative to Self-Attention
Moreover, experiments demonstrate the effectiveness of BiMamba as an alternative to the self-attention module in Transformer and its derivates, particularly for the semantic-aware task.
What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy Conditions
There is increasing interest in the use of the LEArnable Front-end (LEAF) in a variety of speech processing systems.
A Novel Markovian Framework for Integrating Absolute and Relative Ordinal Emotion Information
We propose a Markovian framework referred to as Dynamic Ordinal Markov Model (DOMM) that makes use of both absolute and relative ordinal information, to improve speech based ordinal emotion prediction.
An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks
The neural encoding scheme, that we call Biologically plausible Auditory Encoding (BAE), emulates the functions of the perceptual components of the human auditory system, that include the cochlear filter bank, the inner hair cells, auditory masking effects from psychoacoustic models, and the spike neural encoding by the auditory nerve.
