arXiv subjects

Jangyeon Kim

Publications and source records attributed to Jangyeon Kim.

2 recordsLinked to original sources

arXiv2026

Deep Filter Estimation from Inter-Frame Correlations for Monaural Speech Dereverberation

Speech dereverberation with a distant microphone is challenging because reverberation is correlated with the target speech, and models trained on simulated data often generalize poorly to real recordings. We propose IF-CorrNet, a correlation-to-filter architecture for monaural dereverberation. Instead of feeding raw complex STFT coefficients to the network, IF-CorrNet computes inter-frame correlations among neighboring frames at each time-frequency bin and estimates multi-frame deep filters from these features with a dual-path Transformer backbone. This design makes inter-frame dependencies explicit at the network input while retaining a multi-frame filtering output, a pairing motivated by the normal equation of linear multi-frame filtering. On the REVERB Challenge corpus, IF-CorrNet achieves the best CD, LLR, SNRfw, and PESQ among the compared dereverberation baselines on SimData, and the highest SRMR among the compared systems on RealData. The ablation shows higher RealData SRMR with correlation inputs for both filtering and masking, with filtering adding a further gain.

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arXiv2025

Stack Less, Repeat More: A Block Reusing Approach for Progressive Speech Enhancement

This paper presents an efficient speech enhancement (SE) approach that reuses a processing block repeatedly instead of conventional stacking. Rather than increasing the number of blocks for learning deep latent representations, repeating a single block leads to progressive refinement while reducing parameter redundancy. We also minimize domain transformation by keeping an encoder and decoder shallow and reusing a single sequence modeling block. Experimental results show that the number of processing stages is more critical to performance than the number of blocks with different weights. Also, we observed that the proposed method gradually refines a noisy input within a single block. Furthermore, with the block reuse method, we demonstrate that deepening the encoder and decoder can be redundant for learning deep complex representation. Therefore, the experimental results confirm that the proposed block reusing enables progressive learning and provides an efficient alternative for SE.

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