Related Transfer Functions Research Articles

Impact of non-individualised head related transfer functions on speech-in-noise performances within a synthesised virtual environment.

When performing binaural spatialisation, it is widely accepted that the choice of the head related transfer functions (HRTFs), and in particular the use of individually measured ones, can have an impact on localisation accuracy, externalization, and overall realism. Yet the impact of HRTF choices on speech-in-noise performances in cocktail party-like scenarios has not been investigated in depth. This paper introduces a study where 22 participants were presented with a frontal speech target and two lateral maskers, spatialised using a set of non-individual HRTFs. Speech reception threshold (SRT) was measured for each HRTF. Furthermore, using the SRT predicted by an existing speech perception model, the measured values were compensated in the attempt to remove overall HRTF-specific benefits. Results show significant overall differences among the SRTs measured using different HRTFs, consistently with the results predicted by the model. Individual differences between participants related to their SRT performances using different HRTFs could also be found, but their significance was reduced after the compensation. The implications of these findings are relevant to several research areas related to spatial hearing and speech perception, suggesting that when testing speech-in-noise performances within binaurally rendered virtual environments, the choice of the HRTF for each individual should be carefully considered.

An integrated MVDR beamformer for speech enhancement using a local microphone array and external microphones

An integrated version of the minimum variance distortionless response (MVDR) beamformer for speech enhancement using a microphone array has been recently developed, which merges the benefits of imposing constraints defined from both a relative transfer function (RTF) vector based on a priori knowledge and an RTF vector based on a data-dependent estimate. In this paper, the integrated MVDR beamformer is extended for use with a microphone configuration where a microphone array, local to a speech processing device, has access to the signals from multiple external microphones (XMs) randomly located in the acoustic environment. The integrated MVDR beamformer is reformulated as a quadratically constrained quadratic program (QCQP) with two constraints, one of which is related to the maximum tolerable speech distortion for the imposition of the a priori RTF vector and the other related to the maximum tolerable speech distortion for the imposition of the data-dependent RTF vector. An analysis of how these maximum tolerable speech distortions affect the behaviour of the QCQP is presented, followed by the discussion of a general tuning framework. The integrated MVDR beamformer is then evaluated with audio recordings from behind-the-ear hearing aid microphones and three XMs for a single desired speech source in a noisy environment. In comparison to relying solely on an a priori RTF vector or a data-dependent RTF vector, the results demonstrate that the integrated MVDR beamformer can be tuned to yield different enhanced speech signals, which may be more suitable for improving speech intelligibility despite changes in the desired speech source position and imperfectly estimated spatial correlation matrices.

Robust MFC anti-windup scheme for LTI systems with norm-bounded uncertainty

On the basic of the fact that all signals in the practical system are always bounded, this paper proposes a 4-degree-of-freedom (DoF) anti-windup scheme for saturated systems with parametric uncertainty. A fairly straightforward tuning rule is introduced to the robust stability analysis for the proposed anti-windup structure under the framework of IQC (Integral Quadratic Constraint). And the sufficient stability conditions are derived to check the reasonable definiteness of the related transfer function. Moreover, the control design for disturbance response and set-point tracking response are two separate part in this proposed scheme. Numerical example demonstrates the effectiveness and the considerable performance improvement of the anti-windup compensator that is designed by the proposed technique.

Sensor Selection for Relative Acoustic Transfer Function Steered Linearly-Constrained Beamformers

For multi-microphone speech enhancement, different microphones might have different contributions, assome are even marginal. This is more likely to happen in wireless acoustic sensor networks (WASNs), where somesensors might be distant. In this work, we therefore consider sensor selection for linearly-constrained beamformers. Theproposed sensor selection approach is formulated by minimizing the total output noise power and constraining thenumber of selected sensors. As the considered sensor selection problem requires the relative acoustic transfer function(RTF), the covariance whitening based RTF estimation or a direct-path RTF approximation is exploited. For a singletarget source, we can thus substitute the estimated RTF or the assumed RTF to the original problem formulation in orderto design a minimum variance distortionless response (MVDR) beamformer. Alternatively, we can integrate the two RTFsto design a linearly constrained minimum variance (LCMV) beamformer in order to alleviate the effects of RTFestimation/approximation errors. By leveraging the superiority of LCMV beamformers, the proposed approach can beapplied to the multi-source case. An evaluation using a simulated large-scale WASN demonstrates that the integration ofRTFs for the sensor selection based LCMV beamformer can be beneficial as opposed to relying on either of theindividual RTF steered sensor selection based MVDR beamformers. We conclude that the sensors that are close to thetarget source(s) and also some around the coherent interferers are more informative.

Stability of micro-milling thin-walled part process

Micro-scale thin-walled parts have the characteristics of small size and low rigidity, so chatter is very easy to occur in high speed micro-milling, which influences machining precision and surface quality of the parts. To solve this problem, the authors first establish micro-milling force models in micro-milling thin-walled parts that consider the elastic deflection of both thin-walled part and micro-milling tool. According to the Lagrange’s equation, the dynamic characteristics of thin-walled part that vary with the cutting position of the tool are obtained combined with the Rayleigh-Ritz method. The relative transfer function between the micro-milling tool and the thin-walled part is achieved through the relationship between cutting force and vibration vector and then the dynamic characteristics of the system are obtained. Finally, the stability lobe diagram is drawn through time-domain simulation and verified by micro-milling experiments. The comparison results show that the prediction results of the stability lobe diagram are consistent with the experimental results. The research is a meaningful exploration of the mechanism of micro-milling thin-walled parts and provides a basis for the selection of cutting parameters for stable cutting.

Semi-Supervised Source Localization in Reverberant Environments With Deep Generative Modeling

Localization in reverberant environments remains an open challenge. Recently, supervised learning approaches have demonstrated very promising results in addressing reverberation. However, even with large data volumes, the number of labels available for supervised learning in such environments is usually small. We propose to address this issue with a semi-supervised learning (SSL) approach, based on deep generative modeling. Our chosen deep generative model, the variational autoencoder (VAE), is trained to generate the phase of relative transfer functions (RTFs) between microphones. In parallel, a direction of arrival (DOA) classifier network based on RTF-phase is also trained. The joint generative and discriminative model, deemed VAE-SSL, is trained using labeled and unlabeled RTF-phase sequences. In learning to generate and classify the sequences, the VAE-SSL extracts the physical causes of the RTF-phase (i.e., source location) from distracting signal characteristics such as noise and speech activity. This facilitates effective end-to-end operation of the VAE-SSL, which requires minimal preprocessing of RTF-phase. VAE-SSL is compared with two signal processing-based approaches, steered response power with phase transform (SRP-PHAT) and MUltiple SIgnal Classification (MUSIC), as well as fully supervised CNNs. The approaches are compared using data from two real acoustic environments - one of which was recently obtained at Technical University of Denmark specifically for our study. We find that VAE-SSL can outperform the conventional approaches and the CNN in label-limited scenarios. Further, the trained VAE-SSL system can generate new RTF-phase samples which capture the physics of the acoustic environment. Thus, the generative modeling in VAE-SSL provides a means of interpreting the learned representations. To the best of our knowledge, this paper presents the first approach to modeling the physics of acoustic propagation using deep generative modeling.

Sequential Estimation of Relative Transfer Function in Application of Acoustic Beamforming

In this paper, a sequential approach is proposed to estimate the relative transfer functions (RTF) used in developing a generalized sidelobe canceller (GSC). The latency in calibrating microphone arrays for GSC, often suffered by conventional approaches involving batch operations, is significantly reduced in the proposed sequential method. This is accomplished by an immediate generation of the RTF from initial input segments and subsequent updates of the RTF as the input stream continues. From the experimental results via the mean square error (MSE) criterion, it has been shown that the proposed method exhibits improved performance over the conventional batch approach as well as over recently introduced least mean squares approaches.

Semi-supervised source localization in reverberant environments using deep generative modeling

We present a method for acoustic source localization in reverberant environments based on semi-supervised machine learning (ML) with deep generative models. Source localization in the presence of reverberation remains a major challenge, which recent ML techniques have shown promise in addressing. Despite often large data volumes, the number of labels available for supervised learning in reverberant environments is usually small. In semi-supervised learning, ML systems are trained using many examples with only few labels, with the goal of exploiting the natural structure of the data. We use variational autoencoders (VAEs), which are generative neural networks (NNs) that rely on explicit probabilistic representations, to model the latent distribution of reverberant acoustic data. VAEs consist of an encoder NN, which maps complex input distributions to simpler parametric distributions (e.g., Gaussian), and a decoder NN which approximates the training examples. The VAE is trained to generate the phase of relative transfer functions (RTFs) between two microphones in reverberant environments, in parallel with a DOA classifier, on both labeled and unlabeled RTF samples. The performance this VAE-based approach is compared with conventional and ML-based localization in simulated and real-world scenarios.

Geomorphological and Geochronological Analysis Applied to the Quaternary Landscape Evolution of the Yeltes River (Salamanca, Spain)

This paper aims to study the Quaternary geomorphological evolution of the Yeltes river-valley (Duero Basin, Central Spain) primarily based on the study of the Late Neogene piedmont dissected by the river and its Quaternary terrace sequence, since fluvial terraces are excellent archives to study the landscape and climate evolution during this period. Detailed geomorphological mapping implemented in GIS-based digital elevation models was used to the further applications of existing fluvial chronofunctions (relative terrace height-age transfer functions) to establish a numerical geochronology to the sequence of fluvial terraces in the zone. The obtained theoretical ages points to an onset of fluvial incision in the zone after 2.0–2.5 Myr ago, with the dissection of the “Raña surface” (a Gelasian alluvial piedmont widely developed in Central Spain). The obtained terrace ages coincide, in most cases, with warm isotopic stages (MIS) or mainly with the transit of cold to warm MIS. Additionally, this study suggests that the full connectivity of the Yeltes drainage (Ciudad Rodrigo Basin) with the Atlantic drainage was not completely effective until MIS 9 (c. 0.29 Myr). The new reported data allows for the exploration of the timing and processes involved in the capture of inland sedimentary basins (Ciudad Rodrigo, Duero basins) by the Atlantic drainage during the early Quaternary.

Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse head related transfer function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions into high frequency representations of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of convolutional auto-encoder (CAE) and denoising auto-encoder (DAE) models is proposed to restore the high frequency distortion in SH-interpolated HRTFs. Results were evaluated using both perceptual spectral difference (PSD) and localisation prediction models, both of which demonstrated significant improvement after the restoration process.

An integrated multi-channel approach for joint noise reduction and dereverberation

The performance of speech communication and automatic speech recognition systems greatly degrades in real conditions where reverberation and noise exist. In this paper, we propose an integrated multi-channel speech enhancement approach for joint noise reduction and dereverberation based on the multi-channel Wiener filter (MWF) and the weighted prediction error (WPE). Specifically, the input signal of MWF is derived from WPE that is considered as a preprocessor. MWF is implemented as a minimum variance distortionless response (MVDR) beamformer followed by a single-channel Wiener filter. The covariance matrix of the undesired signal in MWF is approximated using the prediction in WPE. In the implementation of MVDR beamformer, the relative early transfer functions (RETFs) are used rather than either the entire relative transfer functions (RTFs) or only the direct-path of desired speech signal, which are estimated by covariance whitening method. The target signal variance in WPE is further updated with the output signal. Experimental results show that this proposed approach outperforms the state-of-art approaches in reducing late reverberation and noise, with the reduction of word error rate of 38.50% in 3 m recorded data conditions.

A 36-Class Bimodal ERP Brain-Computer Interface Using Location-Congruent Auditory-Tactile Stimuli.

To date, traditional visual-based event-related potential brain-computer interface (ERP-BCI) systems continue to dominate the mainstream BCI research. However, these conventional BCIs are unsuitable for the individuals who have partly or completely lost their vision. Considering the poor performance of gaze independent ERP-BCIs, it is necessary to study techniques to improve the performance of these BCI systems. In this paper, we developed a novel 36-class bimodal ERP-BCI system based on tactile and auditory stimuli, in which six-virtual-direction audio files produced via head related transfer functions (HRTF) were delivered through headphones and location-congruent electro-tactile stimuli were simultaneously delivered to the corresponding position using electrodes placed on the abdomen and waist. We selected the eight best channels, trained a Bayesian linear discriminant analysis (BLDA) classifier and acquired the optimal trial number for target selection in online process. The average online information transfer rate (ITR) of the bimodal ERP-BCI reached 11.66 bit/min, improvements of 35.11% and 36.69% compared to the auditory (8.63 bit/min) and tactile approaches (8.53 bit/min), respectively. The results demonstrate the performance of the bimodal system is superior to each unimodal system. These facts indicate that the proposed bimodal system has potential utility as a gaze-independent BCI in future real-world applications.

Research on interconnection power system Construction of traditional energy Control Structure

With the increasing proportion of new energy in the power system energy. It has become an inevitable trend for new energy grid connected to participate in power system frequency modulation. As the representative energy of new energy, wind power has become the research focus of frequency modulation with traditional energy in recent years. In order to realize the frequency stability of power system, the load frequency control technology is used for power system frequency modulation at this stage. That is to say, in the case of any load disturbance, the power system can realize the load disturbance following the random change of power generation, so as to maintain the frequency deviation within a allowable range (±0.2Hz). Based on this purpose, this design starts from the load frequency control model of conventional units, and first does not consider the role of wind power to build the control structure of traditional energy. In order to facilitate the study of the design of traditional energy units are selected thermal power units. Starting from the establishment of power system frequency response model, we can understand the composition of traditional energy power system frequency response model and the structural characteristics of multi regional interconnected power system. The characteristics of load frequency control module of governor, turbine, generator, load and other basic conventional units are analyzed, and the related transfer function is listed. After the completion of the basic module, the mathematical model of traditional energy single area and traditional energy two areas is built. At the same time, when considering the construction of two regional interconnected thermal power units, we should also pay attention to the characteristic analysis of tie line power and regional control deviation.

Block‐online multi‐channel speech enhancement using deep neural network‐supported relative transfer function estimates

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.

Parasitic Capacitances, Inductive Coupling, and High-Frequency Behavior of AMR Sensors

Anisotropic Magneto-Resistive (AMR) sensors are popular in sensitive applications, especially in planar on-chip integrated form which implies the presence of several on-chip capacitive and inductive parasitic elements. However, there is a lack of equivalent circuit models capturing these parasitics and allowing the modeling of sensors' behavior at higher frequencies or in stability-sensitive closed-loop operation. This work fills this gap in the literature by introducing a high-frequency equivalent circuit model, for planar on-chip AMR sensors with integrated offset and Set/Reset coils, capturing the main capacitive and inductive parasitic elements. A general methodology for estimating the values of the parasitic elements via a set of measurements is presented and it is applied to a particular commercial AMR sensor. Using the introduced circuit model, the transfer function from the offset coil voltage to the output voltage of the sensor, via the parasitic capacitive coupling path, is derived. The transfer function from the S/R coil voltage to the offset coil voltage is also derived. Both are used to create a system-level model of the AMR sensor which can be used in system-level analysis to help the designer predict and optimize the open or the closed-loop sensor circuit system behavior. The introduced high-frequency equivalent circuit model and the two related transfer functions have been verified experimentally using the component values derived from the examined commercial AMR sensor.

Successive Relative Transfer Function Identification Using Blind Oblique Projection

Distortionless speech extraction in a reverberant environment can be achieved by applying a beamforming algorithm, provided that the relative transfer functions (RTFs) of the sources and the covariance matrix of the noise are known. In this paper, the challenge of RTF identification in a multi-speaker scenario is addressed. We propose a successive RTF identification (SRI) technique, based on the sole assumption that sources do not become simultaneously active. That is, we address the challenge of estimating the RTF of a specific speech source while assuming that the RTFs of all other active sources in the environment were previously estimated in an earlier stage. The RTF of interest is identified by applying the blind oblique projection (BOP)-SRI technique. When a new speech source is identified, the BOP algorithm is applied. BOP results in a null steering toward the RTF of interest, by means of applying an oblique projection to the microphone measurements. We prove that by artificially increasing the rank of the range of the projection matrix, the RTF of interest can be identified. An experimental study is carried out to evaluate the performance of the BOP-SRI algorithm in various signal to noise ratio (SNR) and signal to interference ratio (SIR) conditions and to demonstrate its effectiveness in speech extraction tasks.

Acoustic Feedback Suppression for Multi-Microphone Hearing Devices Using a Soft-Constrained Null-Steering Beamformer

Acoustic feedback occurs in hearing aids due to the coupling between the hearing aid loudspeaker and microphone(s). In order to reduce the acoustic feedback, adaptive filters are commonly used to estimate the feedback contribution in the microphone(s). While theoretically allowing for perfect feedback cancellation, in practice the adaptive filter typically converges to a biased optimal solution due to the closed-loop acoustical system of the hearing aid. Previously it has therefore been proposed to suppress the acoustic feedback contribution for an earpiece with multiple integrated microphones and loudspeakers using a fixed null-steering beamformer and hence avoiding a biased adaption. While previous null-steering beamforming approaches aimed at perfect preservation of the incoming signal using its relative transfer function (RTF), in this article we propose to use a soft constraint that allows to trade off between incoming signal preservation and feedback suppression. We formulate the computation of the beamformer coefficients both as a least-squares optimization procedure, aiming to minimize the residual feedback power, and as a min-max optimization procedure, aiming to directly maximize the maximum stable gain of the hearing aid. Experimental evaluations were performed using measured acoustic feedback paths from a custom earpiece with two microphones in the vent and a third microphone in the concha. Results show that the proposed fixed null-steering beamformer using the RTF-based soft constraint provides a reduction of the acoustic feedback by 7–8 dB compared to the previously proposed RTF-based hard constraint while limiting the distortions of the incoming signal in the beamformer output.

Cognitive-Driven Binaural Beamforming Using EEG-Based Auditory Attention Decoding

Identifying the target speaker in hearing aid applications is an essential ingredient to improve speech intelligibility. Recently, a least-squares-based auditory attention decoding (AAD) method has been proposed to identify the target speaker from single-trial EEG recordings in an acoustic scenario with two competing speakers. Aiming at enhancing the target speaker and suppressing the interfering speaker and ambient noise, in this article, we propose a cognitive-driven speech enhancement system, consisting of a binaural beamformer which is steered based on AAD and estimated relative transfer function (RTF) vectors, which require estimates of the direction-of-arrivals (DOAs) of both speakers. For binaural beamforming and to generate reference signals for AAD, we consider either minimum-variance-distortionless-response (MVDR) beamformers or linearly-constrained-minimum-variance (LCMV) beamformers. Contrary to the binaural MVDR beamformer, the binaural LCMV beamformer allows to preserve the spatial impression of the acoustic scene and to control the suppression of the interfering speaker, which is important when intending to switch attention between speakers. The speech enhancement performance of the proposed system is evaluated in terms of the binaural signal-to-interference-plus-noise ratio ( $\text {SINR}$ ) improvement in anechoic and reverberant conditions. Furthermore, we investigate the impact of RTF and DOA estimation errors and AAD errors on the speech enhancement performance. The experimental results show that the proposed system using LCMV beamformers yields a larger decoding performance and binaural $\text {SINR}$ improvement compared to using MVDR beamformers.

Square Root-Based Multi-Source Early PSD Estimation and Recursive RETF Update in Reverberant Environments by Means of the Orthogonal Procrustes Problem

Multi-channel short-time Fourier transform (STFT) domain-based processing of reverberant microphone signals commonly relies on power-spectral-density (PSD) estimates of early source images, where early refers to reflections contained within the same STFT frame. State-of-the-art approaches to multi-source early PSD estimation, given an estimate of the associated relative early transfer functions (RETFs), conventionally minimize the approximation error defined with respect to the early correlation matrix, requiring non-negative inequality constraints on the PSDs. Instead, we here propose to factorize the early correlation matrix and minimize the approximation error defined with respect to the early-correlation-matrix square root. The proposed minimization problem -- constituting a generalization of the so-called orthogonal Procrustes problem -- seeks a unitary matrix and the square roots of the early PSDs up to an arbitrary complex argument, making non-negative inequality constraints redundant. A solution is obtained iteratively, requiring one singular value decomposition (SVD) per iteration. The estimated unitary matrix and early PSD square roots further allow to recursively update the RETF estimate, which is not inherently possible in the conventional approach. An estimate of the said early-correlation-matrix square root itself is obtained by means of the generalized eigenvalue decomposition (GEVD), where we further propose to restore non-stationarities by desmoothing the generalized eigenvalues in order to compensate for inevitable recursive averaging. Simulation results indicate fast convergence of the proposed multi-source early PSD estimation approach in only one iteration if initialized appropriately, and better performance as compared to the conventional approach.

EMTF XML: New data interchange format and conversion tools for electromagnetic transfer functions

Initial processing of magnetotelluric (MT) data collected at a site results in a small data file that defines the MT transfer functions (MT TFs) or variants at a discrete set of frequencies. Although not data intensive, the TF represents almost all of the information about earth’s conductivity found in the raw data and provides a key input to geophysical inversion and to geomagnetic hazard-mitigation procedures. However, to be useful for scientific interpretation, MT TFs must be accompanied by carefully recorded metadata. The value of these data is amplified if they are collected into a common database in a discoverable fashion; to achieve this, the metadata must also be searchable. We have developed a new format for MT and related electromagnetic transfer function extensible markup language (EMTF XML), which is a novel, self-describing, searchable, and extensible way to store the data. We also evaluate our open-source, highly capable conversion tools that implement format conversion between historical MT TF formats (electrical data interchange [EDI], EDI SPECTRA, Z-files, and potentially others) and EMTF XML and we examine the associated issues of data rotation. We have paired our work with the open-source format conversion tools, EMTF FCU v4.0, with a user guide and a set of use case examples. We hope that this effort will assist the MT community in creating a more open data-sharing culture and practices.