most citedAn Empirical Study on the Impact of Positional Encoding in Transformer-based Monaural Speech Enhancement

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

eess.AS2024

Binaural Selective Attention Model for Target Speaker Extraction

Hanyu Meng, Qiquan Zhang, Xiangyu Zhang +2

The remarkable ability of humans to selectively focus on a target speaker in cocktail party scenarios is facilitated by binaural audio processing. In this paper, we present a binau…

eess.AS2024

An Exploration of Length Generalization in Transformer-Based Speech Enhancement

Qiquan Zhang, Hongxu Zhu, Xinyuan Qian +2

The use of Transformer architectures has facilitated remarkable progress in speech enhancement. Training Transformers using substantially long speech utterances is often infeasible…

eess.AS20241 cited

An Empirical Study on the Impact of Positional Encoding in Transformer-based Monaural Speech Enhancement

Qiquan Zhang, Meng Ge, Hongxu Zhu +4

Transformer architecture has enabled recent progress in speech enhancement. Since Transformers are position-agostic, positional encoding is the de facto standard component used to…

eess.AS2023

EEG-Derived Voice Signature for Attended Speaker Detection

Hongxu Zhu, Siqi Cai, Yidi Jiang +2

\textit{Objective:} Conventional EEG-based auditory attention detection (AAD) is achieved by comparing the time-varying speech stimuli and the elicited EEG signals. However, in ord…

cs.SD2023

Ripple sparse self-attention for monaural speech enhancement

Qiquan Zhang, Hongxu Zhu, Qi Song +3

The use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally…