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Non-Gaussianity-based Independent Vector Extraction leads to the famous one-unit FastICA/FastIVA algorithm when the likelihood function is optimized using an approximate Newton-Raphson algorithm under the orthogonality constraint. In this paper, we replace the constraint with the analytic form of the minimum variance distortionless beamformer (MVDR), by which a semi-blind variant of FastICA/FastIVA is obtained. The side information here is provided by a weighted covariance matrix replacing the noise covariance matrix, the estimation of which is a frequent goal of neural beamformers. The algorithm thus provides an intuitive connection between model-based blind extraction and learning-based extraction. The algorithm is tested in simulations and speaker ID-guided speaker extraction, showing fast convergence and promising performance., Comment: accepted for IWAENC 2024

This manuscript proposes a novel robust procedure for the extraction of a speaker of interest (SOI) from a mixture of audio sources. The estimation of the SOI is performed via independent vector extraction (IVE). Since the blind IVE cannot distinguish the target source by itself, it is guided towards the SOI via frame-wise speaker identification based on deep learning. Still, an incorrect speaker can be extracted due to guidance failings, especially when processing challenging data. To identify such cases, we propose a criterion for non-intrusively assessing the estimated speaker. It utilizes the same model as the speaker identification, so no additional training is required. When incorrect extraction is detected, we propose a ``deflation'' step in which the incorrect source is subtracted from the mixture and, subsequently, another attempt to extract the SOI is performed. The process is repeated until successful extraction is achieved. The proposed procedure is experimentally tested on artificial and real-world datasets containing challenging phenomena: source movements, reverberation, transient noise, or microphone failures. The method is compared with state-of-the-art blind algorithms as well as with current fully supervised deep learning-based methods., Comment: Modified version of the article accepted for publication in IEEE/ACM Transactions on Audio Speech and Language Processing journal. Original results unchanged, additional experiments presented, refined discussion and conclusions

Recently, Constant Separating Vector (CSV) mixing model has been proposed for the Blind Source Extraction (BSE) of moving sources. In this paper, we experimentally verify the applicability of CSV in the blind extraction of a moving speaker and propose a new BSE method derived by modifying the auxiliary function-based algorithm for Independent Vector Analysis. Also, a piloted variant is proposed for the method with partially controllable global convergence. The methods are verified under reverberant and noisy conditions using {\color{red} simulated as well as real-world acoustic conditions}. They are also verified within the CHiME-4 speech separation and recognition challenge. The experiments corroborate the applicability of CSV as well as the improved convergence of the proposed algorithms.

We propose a novel algorithm for adaptive blind audio source extraction. The proposed method is based on independent vector analysis and utilizes the auxiliary function optimization to achieve high convergence speed. The algorithm is partially supervised by a pilot signal related to the source of interest (SOI), which ensures that the method correctly extracts the utterance of the desired speaker. The pilot is based on the identification of a dominant speaker in the mixture using x-vectors. The properties of the x-vectors computed in the presence of cross-talk are experimentally analyzed. The proposed approach is verified in a scenario with a moving SOI, static interfering speaker, and environmental noise.

This work addresses the problem of block-online processing for multi-channel speech enhancement. Such processing is vital in scenarios with moving speakers and/or when very short utterances are processed, e.g., in voice assistant scenarios. We consider several variants of a system that performs beamforming supported by DNN-based voice activity detection (VAD) followed by post-filtering. The speaker is targeted through estimating relative transfer functions between microphones. Each block of the input signals is processed independently in order to make the method applicable in highly dynamic environments. Owing to the short length of the processed block, the statistics required by the beamformer are estimated less precisely. The influence of this inaccuracy is studied and compared to the processing regime when recordings are treated as one block (batch processing). The experimental evaluation of the proposed method is performed on large datasets of CHiME-4 and on another dataset featuring moving target speaker. The experiments are evaluated in terms of objective and perceptual criteria (such as signal-to-interference ratio (SIR) or perceptual evaluation of speech quality (PESQ), respectively). Moreover, word error rate (WER) achieved by a baseline automatic speech recognition system is evaluated, for which the enhancement method serves as a front-end solution. The results indicate that the proposed method is robust with respect to short length of the processed block. Significant improvements in terms of the criteria and WER are observed even for the block length of 250 ms., Comment: 10 pages, 8 figures, 4 tables. Modified version of the article accepted for publication in IET Signal Processing journal. Original results unchanged, additional experiments presented, refined discussion and conclusions

Relative impulse responses between microphones are usually long and dense due to the reverberant acoustic environment. Estimating them from short and noisy recordings poses a long-standing challenge of audio signal processing. In this paper we apply a novel strategy based on ideas of Compressed Sensing. Relative transfer function (RTF) corresponding to the relative impulse response can often be estimated accurately from noisy data but only for certain frequencies. This means that often only an incomplete measurement of the RTF is available. A complete RTF estimate can be obtained through finding its sparsest representation in the time-domain: that is, through computing the sparsest among the corresponding relative impulse responses. Based on this approach, we propose to estimate the RTF from noisy data in three steps. First, the RTF is estimated using any conventional method such as the non-stationarity-based estimator by Gannot et al. or through Blind Source Separation. Second, frequencies are determined for which the RTF estimate appears to be accurate. Third, the RTF is reconstructed through solving a weighted $\ell_1$ convex program, which we propose to solve via a computationally efficient variant of the SpaRSA (Sparse Reconstruction by Separable Approximation) algorithm. An extensive experimental study with real-world recordings has been conducted. It has been shown that the proposed method is capable of improving many conventional estimators used as the first step in most situations., Comment: IEEE Trans. on Speech, Audio and Language Processing, 2015

We report a high-resolution resonant inelastic soft x-ray scattering study of the quantum magnetic spin-chain materials Li2CuO2 and CuGeO3. By tuning the incoming photon energy to the oxygen K-edge, a strong excitation around 3.5 eV energy loss is clearly resolved for both materials. Comparing the experimental data to many-body calculations, we identify this excitation as a Zhang-Rice singlet exciton on neighboring CuO4-plaquettes. We demonstrate that the strong temperature dependence of the inelastic scattering related to this high-energy exciton enables to probe short-range spin correlations on the 1 meV scale with outstanding sensitivity., Comment: 5 pages, 4 figures

As-vacancies (V_As) in La-1111-systems, which are nominally non-magnetic defects, are shown to create in their vicinity by symmetry ferromagnetically oriented local magnetic moments due to the strong, covalent bonds with neighboring Fe atoms that they break. From microscopic theory in terms of an appropriately modified Anderson-Wolff model, we find that the moment formation results in a substantially enhanced paramagnetic susceptibility in both the normal and superconducting (SC) state. Despite the V_As act as magnetic scatterers, they do not deteriorate SC properties which can even be improved by V_As by suppressing a competing or coexisting commensurate spin density wave or its remnant fluctuations. Due to the induced local magnetic moments an s_++-scenario in related systems is unlikely., Comment: 7 pages, 4 Figures

For the 1D-frustrated ferromagnetic J_1-J_2 model with interchain coupling added, we analyze the dynamical and static structure factor S(k,omega), the pitch angle phi of the magnetic structure, the magnetization curve of edge-shared chain cuprates, and focus on LiCuVO4 for which neither a perturbed spinon nor a spin wave approach can be applied. phi is found to be most sensitive to the interplay of frustration and quantum fluctuations. For LiVCuO4 the obtained exchange parameters J are in accord with the results for a realistic 5-band extended Hubbard model and LSDA + U predictions yielding alpha=J_2/|J_1| about 0.75 in contrast to 5.5 > alpha > 1.42 suggested in the literature. The alpha-regime of the empirical phi-values in NaCu2O2 and linarite are considered, too., Comment: 7 pages, 7 figures, (1 figure added), improved text including also the abstract (the present second version has been submitted to EPL 26.10.2011, so far with one missing first referee report)

We report a theoretical analysis of intrinsic localized modes in a quasi-one-dimensional charge-transfer-solid $[Pt(en)_2][Pt(en)_2 Cl_2](ClO_4)_4$(PtCl). We discuss strongly nonlinear features of resonant Raman overtone scattering measurements on PtCl, arising from quantum intrinsic localized (multiphonon) modes (ILMs) and ILM-plus-phonon states. We show, that Raman scattering data displays clear signs of a non-thermalization of lattice degrees-of-freedom, manifested in a nonequilibrium density of intrinsic localized modes., Comment: 4 pages, 4 figures, REVTEX

The ground state energies of infinite half-filled Hubbard-Peierls chains are investigated combining incremental expansion with exact diagonalization of finite chain segments. The ground state energy of equidistant infinite Hubbard (Heisenberg) chains is calculated with a relative error of less than $3 \cdot 10^{-3}$ for all values of $U$ using diagonalizations of 12-site (20-site) chain segm ents. For dimerized chains the dimerization order parameter $d$ as a function of the onsite repulsion interaction $U$ has a maximum at nonzero values of $U$, if the electron-phonon coupling $g$ is lower than a critical value $g_c$. The critical value $g_c$ is found with high accuracy to be $g_c=0.69$. For smaller values of $g$ the position of the maximum of $d(U)$ is approximately $3t$, and rapidly tends to zero as $g$ approaches $g_c$ from below. We show how our method can be applied to calculate breathers for the problem of phonon dynamics in Hubbard-Peierls systems., Comment: 4 Pages, 3 Figures, REVTEX