一種穩(wěn)健的室內(nèi)無(wú)模糊多聲源TDOA估計(jì)算法
doi: 10.11999/JEIT150824
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61171167, 61401203),江蘇省自然科學(xué)基金(BK20130776)
A Robust Algorithm for Unambiguous TDOA Estimation of Multiple Sound Sources under Indoor Environment
Funds:
The National Natural Science Foundation of China (61171167, 61401203), Natural Science Foundation of Jiangsu Province (BK20130776)
-
摘要: 該文針對(duì)室內(nèi)環(huán)境下的寬間距多聲源到達(dá)時(shí)間差(TDOA)估計(jì)問(wèn)題,研究了一種基于近似核密度估計(jì)(KDE)的無(wú)模糊算法。根據(jù)聲頻信號(hào)的短時(shí)頻譜稀疏性,利用相關(guān)性檢測(cè)(CT)提取單個(gè)聲源能量占優(yōu)的時(shí)頻支撐域,進(jìn)而將觀測(cè)信號(hào)的歸一化互功率譜(NCS)所構(gòu)建的近似核函數(shù)通過(guò)累加平均削弱室內(nèi)混響的干擾,同時(shí)引入多階段(MS)分頻帶處理有效解決寬間距時(shí)的空域模糊。理論推導(dǎo)及仿真研究驗(yàn)證了該算法是一種穩(wěn)健的室內(nèi)無(wú)模糊多聲源TDOA估計(jì)算法。
-
關(guān)鍵詞:
- 語(yǔ)音信號(hào)處理 /
- 麥克風(fēng)陣列 /
- 歸一化互功率譜 /
- 相關(guān)性檢測(cè) /
- 近似核密度函數(shù) /
- 無(wú)模糊到達(dá)時(shí)間差估計(jì)
Abstract: For Time Difference Of Arrival (TDOA) estimation of multiple sound sources with wide spacing under indoor environment, an unambiguous algorithm based on approximated Kernel Destiny Estimator (KDE) is studied. According to the short-time spectral sparseness of audio signals, the time-frequency bin with energy dominance of a single source is extracted from Coherence Test (CT), then an approximated kernel function constructed of Normalized Cross-Spectrum (NCS) of obtained signals is used to weaken the interference of indoor reverberation with cumulative average, while adding Multi-Stage (MS) to divide the frequency band, the spatial ambiguity with wide spacing can be solved effectively. This algorithm is verified as an unambiguous TDOA estimation algorithm of multi-source under indoor environment by both theoretical derivation and simulation results. -
KNAPP C H and CARTER G C. The generalized correlation method for estimation of time delay[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1976, 24(4): 320-327. TSIAMI A, KATSAMANIS A, MARAGOS P, et al. Experiments in acoustic source localization using sparse arrays in adverse indoors environments[C]. Proceedings of 2014 European Signal Processing Conference (EUSIPCO), Lisbon, Portugal, 2014: 2390-2394. 張超, 吳小培, 呂釗. 基于獨(dú)立分量分析的運(yùn)動(dòng)目標(biāo)檢測(cè)算法中對(duì)通道數(shù)選擇和觀測(cè)向量生成方式的實(shí)驗(yàn)和分析[J]. 電子與信息學(xué)報(bào), 2015, 37(1): 137-142. doi: 10.11999/ JEIT140197. ZHANG Chao, WU Xiaopei, and L Zhao. Experiments and analysis on observation vector generation and channel number selection in motion detection algorithm based on independent component analysis[J]. Journal of Electronics Information Technology, 2015, 37(1): 137-142. doi: 10.11999/ JEIT140197. LOMBARD A, ZHENG Y, BUCHNER H, et al. TDOA estimation for multiple sound sources in noisy and reverberant environments using broadband independent component analysis[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2011, 19(6): 1490-1503. NESTA F, SVAIZER P, and OMOLOGO M. Cumulative state coherence transform for a robust two-channel multiple source localization[C]. Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation (ICA), Berlin, Germany, 2009: 290-297. NESTA F and OMOLOGO M. Generalized state coherence transform for multidimensional TDOA estimation of multiple sources[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(1): 246-260. REDDY V V, KHONG W H, and NG B P. Unambiguous speech DOA estimation under spatial aliasing conditions[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2014, 22(12): 2133-2145. YILMAZ O and RICKARD S. Blind separation of speech mixtures via time-frequency masking[J]. IEEE Transactions on Signal Processing, 2004, 52(7): 1830-1847. ARAKI S, SAWADA H, MUKAI R, et al. DOA estimation for multiple sparse sources with normalized observation vector clustering[C]. Proceedings of 2006 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2006), Toulouse, France, 2006: 33-36. BRUTTI A and NESTA F. Tracking of multidimensional TDOA for multiple sources with distributed microphone pairs[J]. Computer Speech Language, 2013, 27(3): 660-682. THO N T N, ZHAO Shengkui, and JONES D L. Robust DOA estimation of multiple speech sources[C]. Proceedings of 2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Florence, Italy, 2014: 2287-2291. 許志勇, 趙兆, 劉明. 寬間距麥克風(fēng)陣列實(shí)時(shí)無(wú)模糊多聲源被動(dòng)測(cè)向[J]. 電子與信息學(xué)報(bào), 2011, 33(9): 2056-2061. doi: 10.3724/SP.J.1146.2010.01273. XU Zhiyong, ZHAO Zhao, and LIU Ming. Real-time unambiguious passive direction finding for multiple sound sources with widely spaced microphone array[J]. Journal of Electronics Information Technology, 2011, 33(9): 2056-2061. doi: 10.3724/SP.J.1146.2010.01273. GUSTAFFSON T, RAO B D, and TRIVEDI M. Source localization in reverberant environments: Modeling and statistical analysis[J]. IEEE Transactions on Speech and Audio Processing, 2003, 11(6): 791-803. LEHMANN E and JOHANSSON A. Prediction of energy decay in room impulse responses simulated with an image-source model[J]. Acoustical Society of America, 2008, 124(1): 269-277. BLANDIN C, OZEROV A, and VINCENT E. Multi-source TDOA estimation in reverberant audio using angular spectra and clustering[J]. Signal Processing, 2012, 92(8): 1950-1960. -
計(jì)量
- 文章訪問(wèn)數(shù): 1500
- HTML全文瀏覽量: 201
- PDF下載量: 491
- 被引次數(shù): 0