Decoding Speech Envelopes from Electroencephalogram with a Contrastive Pearson Correlation Coefficient Loss
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914593133035520 |
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| author | Liang, Yayun Zhang, Yuanming Chen, Fei Lu, Jing Lin, Zhibin |
| author_facet | Liang, Yayun Zhang, Yuanming Chen, Fei Lu, Jing Lin, Zhibin |
| contents | Recent advances in reconstructing speech envelopes from Electroencephalogram (EEG) signals have enabled continuous auditory attention decoding (AAD) in multi-speaker environments. Most Deep Neural Network (DNN)-based envelope reconstruction models are trained to maximize the Pearson correlation coefficients (PCC) between the attended envelope and the reconstructed envelope (attended PCC). While the difference between the attended PCC and the unattended PCC plays an essential role in auditory attention decoding, existing methods often focus on maximizing the attended PCC. We therefore propose a contrastive PCC loss which represents the difference between the attended PCC and the unattended PCC. The proposed approach is evaluated on three public EEG AAD datasets using four DNN architectures. Across many settings, the proposed objective improves envelope separability and AAD accuracy, while also revealing dataset- and architecture-dependent failure cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20542 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Decoding Speech Envelopes from Electroencephalogram with a Contrastive Pearson Correlation Coefficient Loss Liang, Yayun Zhang, Yuanming Chen, Fei Lu, Jing Lin, Zhibin Audio and Speech Processing Recent advances in reconstructing speech envelopes from Electroencephalogram (EEG) signals have enabled continuous auditory attention decoding (AAD) in multi-speaker environments. Most Deep Neural Network (DNN)-based envelope reconstruction models are trained to maximize the Pearson correlation coefficients (PCC) between the attended envelope and the reconstructed envelope (attended PCC). While the difference between the attended PCC and the unattended PCC plays an essential role in auditory attention decoding, existing methods often focus on maximizing the attended PCC. We therefore propose a contrastive PCC loss which represents the difference between the attended PCC and the unattended PCC. The proposed approach is evaluated on three public EEG AAD datasets using four DNN architectures. Across many settings, the proposed objective improves envelope separability and AAD accuracy, while also revealing dataset- and architecture-dependent failure cases. |
| title | Decoding Speech Envelopes from Electroencephalogram with a Contrastive Pearson Correlation Coefficient Loss |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2601.20542 |