![\"\"](\"https:\/\/audio.create.aau.dk\/wp-content\/uploads\/2017\/10\/IMG_20171017_090407-300x225.jpg\")
The Audio Analysis Lab presented two papers at the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, which was held October 15-18 at Mohonk Mountain House, New Paltz, New York. The lab was also part of the organizing team for this edition of the workshop, as Mads Gr\u00e6sb\u00f8ll Christensen was Technical Program Co-Chair. The papers presented were:<\/p>\n
Experimental Study of Robust Beamforming Techniques for Acoustic Applications\u00a0<\/span><\/strong>Yingke Zhao (Northwestern Polytechnical University, P.R. China); Jesper Rindom Jensen and Mads Gr\u00e6sb\u00f8ll Christensen (Aalborg University, Denmark); Simon Doclo (University of Oldenburg, Germany); Jingdong Chen (Northwestern Polytechnical University, P.R: China)<\/p>\n In this paper, we investigate robust beamforming methods for wideband signal processing in noisy and reverberant environments. In such environments, the appearance of steering vector estimation errors is inevitable, which degrades the performance of beamformers. Here, we study two types of robust beamformers against this estimation inaccuracy. The first type includes the norm constrained Capon, the robust Capon, and the doubly constrained robust Capon beamformers. The underlying principle is to add steering vector uncertainty constraint and norm constraint to the optimization problem to improve the beamformer\u2019s robustness. The second one is the amplitude and phase estimation method, which utilizes both time and spatial smoothing to obtain robust beamforming. Experiments are presented to demonstrate the performance of the robust beamformers in acoustic environments. The results show that the robust beamformers outperform the non-robust methods in many respects: 1) robust performance in reverberation and different noise levels; 2) resilience against steering vector and covariance matrix estimation errors; and 3) better speech quality and intelligibility.<\/p>\n A Kalman-Based Fundamental Frequency Estimation Algorithm\u00a0<\/span><\/strong>Liming Shi, Jesper Kj\u00e6r Nielsen and Jesper Rindom Jensen (Aalborg University, Denmark); Max Little (MIT, USA); Mads Gr\u00e6sb\u00f8ll Christensen (Aalborg University, Denmark)<\/p>\n The Audio Analysis Lab presented two papers at the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, which was held October 15-18 at Mohonk Mountain House, New Paltz, New York. The lab was also part of the organizing team for this edition of the workshop, as Mads Gr\u00e6sb\u00f8ll Christensen was Technical Program […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-793","post","type-post","status-publish","format-standard","hentry","category-general"],"_links":{"self":[{"href":"https:\/\/audio.create.aau.dk\/index.php?rest_route=\/wp\/v2\/posts\/793","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/audio.create.aau.dk\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/audio.create.aau.dk\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/audio.create.aau.dk\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/audio.create.aau.dk\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=793"}],"version-history":[{"count":3,"href":"https:\/\/audio.create.aau.dk\/index.php?rest_route=\/wp\/v2\/posts\/793\/revisions"}],"predecessor-version":[{"id":797,"href":"https:\/\/audio.create.aau.dk\/index.php?rest_route=\/wp\/v2\/posts\/793\/revisions\/797"}],"wp:attachment":[{"href":"https:\/\/audio.create.aau.dk\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=793"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/audio.create.aau.dk\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=793"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/audio.create.aau.dk\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=793"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Fundamental frequency estimation is an important task in speech and audio analysis. Harmonic model-based methods typically have superior estimation accuracy. However, such methods usually assume that the fundamental frequency and amplitudes are stationary over a short time frame. In this paper, we propose a Kalman filter-based fundamental frequency estimation algorithm using the harmonic model, where the fundamental frequency and amplitudes can be truly nonstationary by modeling their time variations as first-order Markov chains. The Kalman observation equation is derived from the harmonic model and formulated as a compact nonlinear matrix form, which is further used to derive an extended Kalman filter. Detailed and continuous fundamental frequency and amplitude estimates for speech, the sustained vowel \/a\/ and solo musical tones with vibrato are demonstrated.<\/div>\n","protected":false},"excerpt":{"rendered":"