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In this paper, a study addressing the task of tracking multiple concurrent speakers in reverberant conditions is presented. Since both past and future observations can contribute to the current location estimate, we propose a forward-backward approach, which improves tracking accuracy by introducing near-future data to the estimator, in the cost of an additional short latency. Unlike classical target tracking, we apply a non-Bayesian approach, which does not make assumptions with respect to the target trajectories, except for assuming a realistic change in the parameters due to natural behaviour. The proposed method is based on the recursive expectation-maximization (REM) approach. The new method is dubbed forward-backward recursive expectation-maximization (FB-REM). The performance is demonstrated using an experimental study, where the tested scenarios involve both simulated and recorded signals, with typical reverberation levels and multiple moving sources. It is shown that the proposed algorithm outperforms the regular common causal (REM).

Over the past two decades, synthetic biology has yielded ever more complex genetic circuits able to perform sophisticated functions in response to specific signals. Yet, genetic circuits are not immediately transferable to an outside-the-lab setting where their performance is highly compromised. We propose introducing a scale step to the design-build-test workflow to include factors that might contribute to unexpected genetic circuit performance. As a proof-of-concept, we designed and tested a genetic circuit under different temperatures, mediums, inducer concentrations, and bacterial growth phases. We determined that the circuit’s performance is dramatically altered when these factors differ from the optimal lab conditions. Based on these results, a scaling effort, coupled with a learning process, proceeded to generate model predictions for the genetic circuit’s performance under untested conditions, which is currently lacking in synthetic biology application design. As the synthetic biology discipline transitions from proof-of-concept genetic programs to appropriate and safe application implementations, more emphasis on a scale step is needed to ensure correct and robust performances.

Multiple input changes can cause unwanted switching variations, or glitches, in the output of genetic combinational circuits. These glitches can have drastic effects if the output of the circuit causes irreversible changes within or with other cells such as a cascade of responses, apoptosis, or the release of a pharmaceutical in an off-target tissue. Therefore, avoiding unwanted variation of a circuit's output can be crucial for the safe operation of a genetic circuit. This paper investigates what causes unwanted switching variations in combinational genetic circuits using hazard analysis and a new dynamic model generator. The analysis is done in previously built and modeled genetic circuits with known glitching behavior. The dynamic models generated not only predict the same steady states as previous models but can also predict the unwanted switching variations that have been observed experimentally. Multiple input changes may cause glitches due to propagation delays within the circuit. Modifying the circuit's layout to alter these delays may change the likelihood of certain glitches, but it cannot eliminate the possibility that the glitch may occur. In other words, function hazards cannot be eliminated. Instead, they must be avoided by restricting the allowed input changes to the system. Logic hazards, on the other hand, can be avoided using hazard-free logic synthesis. This paper demonstrates this by showing how a circuit designed using a popular genetic design automation tool can be redesigned to eliminate logic hazards.

Motivation Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. Results The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene’s dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. Availability and implementation The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. Supplementary information Supplementary data are available at Bioinformatics online.

Localization of acoustic sources has attracted a considerable amount of research attention in recent years. A major obstacle to achieving high localization accuracy is the presence of reverberation, the influence of which obviously increases with the number of active speakers in the room. Human hearing is capable of localizing acoustic sources even in extreme conditions. In this study, we propose to combine a method based on human hearing mechanisms and a modified incremental distributed expectation-maximization (IDEM) algorithm. Rather than using phase difference measurements that are modeled by a mixture of complex-valued Gaussians, as proposed in the original IDEM framework, we propose to use time difference of arrival measurements in multiple subbands and model them by a mixture of real-valued truncated Gaussians. Moreover, we propose to first filter the measurements in order to reduce the effect of the multipath conditions. The proposed method is evaluated using both simulated data and real-life recordings.

The problem of distributed localization for ad hoc wireless acoustic sensor networks (WASNs) is addressed in this paper. WASNs are characterized by low computational resources in each node and by limited connectivity between the nodes. Novel bi-directional tree-based distributed estimation--maximization (DEM) algorithms are proposed to circumvent these inherent limitations. We show that the proposed algorithms are capable of localizing static acoustic sources in reverberant enclosures without a priori information on the number of sources. Unlike serial estimation procedures (like ring-based algorithms), the new algorithms enable simultaneous computations in the nodes and exhibit greater robustness to communication failures. Specifically, the recursive distributed EM (RDEM) variant is better suited to online applications due to its recursive nature. Furthermore, the RDEM outperforms the other proposed variants in terms of convergence speed and simplicity. Performance is demonstrated by an extensive experimental study consisting of both simulated and actual environments.

A direction of arrival (DOA) estimator for concurrent speakers in a noisy environment with unknown noise power is presented. Spatially colored noise, if not properly addressed, is known to degrade the performance of DOA estimators. In our contribution, the DOA estimation task is formulated as a maximum likelihood (ML) problem, which is solved using the expectation-maximization (EM) procedure. The received microphone signals are modelled as a sum of the speech and noise components. The noise power spectral density (PSD) matrix is modelled by a time-invariant full-rank coherence matrix multiplied by the noise power. The PSDs of the speech and noise components are estimated as part of the EM procedure. The benefit of the presented algorithm in a simulated noisy environment using measured room impulse responses is demonstrated.

Intuitive spoken dialogues are a prerequisite for human-robot inter- action. In many practical situations, robots must be able to identify and focus on sources of interest in the presence of interfering speak- ers. Techniques such as spatial filtering and blind source separa- tion are therefore often used, but rely on accurate knowledge of the source location. In practice, sound emitted in enclosed environments is subject to reverberation and noise. Hence, sound source localiza- tion must be robust to both diffuse noise due to late reverberation, as well as spurious detections due to early reflections. For improved robustness against reverberation, this paper proposes a novel ap- proach for sound source tracking that constructively exploits the spa- tial diversity of a microphone array installed in a moving robot. In previous work, we developed speaker localization approaches using expectation-maximization (EM) approaches and using Bayesian ap- proaches. In this paper we propose to combine the EM and Bayesian approach in one framework for improved robustness against rever- beration and noise.

A blind source separation technique in noisy environment is proposed based on spectral masking and minimum variance distortionless response (MVDR) beamformer (BF). Formulating the maximum-likelihood of the direction of arrivals (DOAs) and solving it using the expectation-maximization, enables the extraction of the masks and the associated MVDR BF as byproducts. The proposed direction of arrival estimator uses an explicit model of the ambient noise, which results in more accurate DOA estimates and good blind source separation. The experimental study demonstrates both the DOA estimation results and the separation capabilities of the proposed method using real room impulse responses in diffuse noise field.

A novel direction of arrival (DOA) estimator for concurrent speakers in reverberant environment is presented. Reverberation, if not properly addressed, is known to degrade the performance of DOA estimators. In our contribution, the DOA estimation task is formulated as a maximum likelihood (ML) problem, which is solved using the expectation-maximization (EM) procedure. The received microphone signals are modelled as a sum of anechoic and reverberant components. The reverberant components are modelled by a timeinvariant coherence matrix multiplied by time-varying reverberation power spectral density (PSD). The PSDs of the anechoic speech and reverberant components are estimated as part of the EM procedure. It is shown that the DOA estimates, obtained by the proposed algorithm, are less affected by reverberation than competing algorithms that ignore the reverberation. Experimental study demonstrates the benefit of the presented algorithm in reverberant environment using measured room impulse responses (RIRs).

Recent years have seen intensive progress in measuring protein translation. However, the contributions of coding sequences to the efficiency of the process remain unclear. Here, we identify a universally conserved profile of translation efficiency along mRNAs computed based on adaptation between coding sequences and the tRNA pool. In this profile, the first approximately 30-50 codons are, on average, translated with a low efficiency. Additionally, in eukaryotes, the last approximately 50 codons show the highest efficiency over the full coding sequence. The profile accurately predicts position-dependent ribosomal density along yeast genes. These data suggest that translation speed and, as a consequence, ribosomal density are encoded by coding sequences and the tRNA pool. We suggest that the slow "ramp" at the beginning of mRNAs serves as a late stage of translation initiation, forming an optimal and robust means to reduce ribosomal traffic jams, thus minimizing the cost of protein expression.

A network of microphone pairs is utilized for the joint task of localizing and separating multiple concurrent speakers. The recently presented incremental distributed expectation-maximization (IDEM) is addressing the first task, namely detection and localization. Here we extend this algorithm to address the second task, namely blindly separating the speech sources. We show that the proposed algorithm, denoted distributed algorithm for localization and separation (DALAS), is capable of separating speakers in reverberant enclosure without a priori information on their number and locations. In the first stage of the proposed algorithm, the IDEM algorithm is applied for blindly detecting the active sources and to estimate their locations. In the second stage, the location estimates are utilized for selecting the most useful node of microphones for the subsequent separation stage. Separation is finally obtained by utilizing the hidden variables of the IDEM algorithm to construct masks for each source in the relevant node.

We propose a novel model for discrete linear periodic time varying (LPTV) systems using wavelets. The new model is compared with the 'raised model', which is commonly used for modeling LPTV systems. In fact, it turns out that the new model can be viewed as a generalization of the raised model. The wavelets model will be shown to be particularly suitable for adaptive identification of LPTV systems. It offers a compromise between time-and frequency-based algorithms. Time resolution is needed for modeling reasons and minimizing processing delay. Frequency resolution enables faster convergence of adaptive algorithms in general and the least mean square algorithm used here, in particular. Simulations show that for a colored input using the new model results not only in faster convergence compared to the raised model based algorithm, but also produces a lower steady-state error. This, at no significant additional cost in numerical complexity.

The challenge of localizing number of concurrent acoustic sources in reverberant enclosures is addressed in this paper. We formulate the localization task as a maximum likelihood (ML) parameter estimation problem, and develop a distributed expectation-maximization (DEM) procedure, based on the Incremental EM (IEM) framework. The algorithm enables localization of the speakers without a center point. Unlike direction search, localization is a distributed task in nature, since the sensors must be spatially deployed. Taking advantage of the distributed constellation of the sensors we propose a distributed algorithm that enables multiple processing nodes and considers communication constraints between them. The proposed DEM has surprising advantages over conventional expectation-maximization (EM) schemes. Firstly, it is less sensitive to initial conditions. Secondly, it converges much faster than the conventional EM. The proposed algorithm is tested by an extensive simulation study.