Noise Suppression for Time Difference of Arrival: Performance Evaluation of a Generalized Cross-Correlation Method Using Mean Signal and Inverse Filter
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909945707888640 |
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| author | Obo, Hirotaka Fujita, Yuki Ishii, Masahisa Moriyama, Hideki Tsuchiya, Ryota Ohashi, Yuta Seki, Kotaro |
| author_facet | Obo, Hirotaka Fujita, Yuki Ishii, Masahisa Moriyama, Hideki Tsuchiya, Ryota Ohashi, Yuta Seki, Kotaro |
| contents | This paper proposes a novel generalized cross-correlation (GCC) method, termed GCC-MSIF, to improve time difference of arrival (TDOA) estimation accuracy in noisy environments. Conventional GCC methods often suffer from performance degradation under low signal-to-noise ratio (SNR) conditions, particularly when the signal bandwidth is unknown. GCC-MSIF introduces a "mean signal" estimated from multi-channel inputs and an "inverse filter" to virtually reconstruct the source signal, enabling adaptive suppression of out-of-band noise. Numerical simulations simulating a small-scale array demonstrate that GCC-MSIF significantly outperforms conventional methods, such as GCC-PHAT and GCC-SCOT, in low SNR regions and achieves robustness comparable to or exceeding the maximum likelihood (GCC-ML) method. Furthermore, the estimation accuracy improves scalably with the number of array elements. These results suggest that GCC-MSIF is a promising solution for robust passive localization in practical blind environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05355 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Noise Suppression for Time Difference of Arrival: Performance Evaluation of a Generalized Cross-Correlation Method Using Mean Signal and Inverse Filter Obo, Hirotaka Fujita, Yuki Ishii, Masahisa Moriyama, Hideki Tsuchiya, Ryota Ohashi, Yuta Seki, Kotaro Signal Processing Audio and Speech Processing This paper proposes a novel generalized cross-correlation (GCC) method, termed GCC-MSIF, to improve time difference of arrival (TDOA) estimation accuracy in noisy environments. Conventional GCC methods often suffer from performance degradation under low signal-to-noise ratio (SNR) conditions, particularly when the signal bandwidth is unknown. GCC-MSIF introduces a "mean signal" estimated from multi-channel inputs and an "inverse filter" to virtually reconstruct the source signal, enabling adaptive suppression of out-of-band noise. Numerical simulations simulating a small-scale array demonstrate that GCC-MSIF significantly outperforms conventional methods, such as GCC-PHAT and GCC-SCOT, in low SNR regions and achieves robustness comparable to or exceeding the maximum likelihood (GCC-ML) method. Furthermore, the estimation accuracy improves scalably with the number of array elements. These results suggest that GCC-MSIF is a promising solution for robust passive localization in practical blind environments. |
| title | Noise Suppression for Time Difference of Arrival: Performance Evaluation of a Generalized Cross-Correlation Method Using Mean Signal and Inverse Filter |
| topic | Signal Processing Audio and Speech Processing |
| url | https://arxiv.org/abs/2512.05355 |