Sound zone control with fixed acoustic contrast and simultaneous tracking of acoustic transfer functions.

As environmental changes are inevitable, online algorithms that can track the variation of acoustic transfer functions are preferred in sound zone control (SZC) systems. In this paper, it is found that for the time domain SZC, the nonuniqueness problem caused by the coupling in multichannel transfer function modeling can be solved with time-varying control filters. To keep a stable acoustic contrast (AC) performance irrespective of environmental changes and also avoid the extreme distortion introduced by maximizing the AC in the time domain, the objective of maintaining a desired level of AC is used and an adaptive control algorithm is developed. The simulations validate that the proposed method tracks the environmental changes and adapts the control filters to give a stable AC performance.

Maximizing the acoustic contrast with constrained reconstruction error under a generalized pressure matching framework in sound zone control.

In sound zone control, the pressure matching (PM) algorithm is generally considered to achieve relatively low acoustic contrast (AC) regardless of the desired sound field. In this paper, we propose a generalized PM framework to generate a desired sound field with variable AC. The framework indicates that the choice of the desired sound field solely determines the AC performance of PM when the physical setting and acoustic environment remain unchanged. In the extreme, PM obtains the same AC as the acoustic contrast control method, which is widely accepted as the algorithm leading to the maximum AC. Based on the proposed framework, we design a variant coordinate descent algorithm to adjust the desired sound field by maximizing the AC under the constraint of the reconstruction error. Simulation results validate the efficacy and flexibility of the proposed framework in constrained sound zone control.

Improved pre-test likelihood estimation of coronary artery disease using phonocardiography.

Aims: Current early risk stratification of coronary artery disease (CAD) consists of pre-test probability scoring such as the 2019 ESC guidelines on chronic coronary syndromes (ESC2019), which has low specificity and thus rule-out capacity. A newer clinical risk factor model (risk factor-weighted clinical likelihood, RF-CL) showed significantly improved rule-out capacity over the ESC2019 model. The aim of the current study was to investigate if the addition of acoustic features to the RF-CL model could improve the rule-out potential of the best performing clinical risk factor models. Methods and results: Four studies with heart sound recordings from 2222 patients were pooled and distributed into two data sets: training and test. From a feature bank of 40 acoustic features, a forward-selection technique was used to select three features that were added to the RF-CL model. Using a cutoff of 5% predicted risk of CAD, the developed acoustic-weighted clinical likelihood (A-CL) model showed significantly (P < 0.05) higher specificity of 48.6% than the RF-CL model (specificity of 41.5%) and ESC 2019 model (specificity of 6.9%) while having the same sensitivity of 84.9% as the RF-CL model. Area under the curve of the receiver operating characteristic for the three models was 72.5% for ESC2019, 76.7% for RF-CL, and 79.5% for A-CL. Conclusion: The proposed A-CL model offers significantly improved rule-out capacity over the ESC2019 model and showed better overall performance than the RF-CL model. The addition of acoustic features to the RF-CL model was shown to significantly improve early risk stratification of symptomatic patients suspected of having stable CAD.

Spectral analysis of heart sounds associated with coronary artery disease.

Objective. The aim of this study was to find spectral differences of diagnostic interest in heart sound recordings of patients with coronary artery disease (CAD) and healthy subjects.Approach. Heart sound recordings from three studies were pooled, and patients with clear diagnostic outcomes (positive: CAD and negative: Non-CAD) were selected for further analysis. Recordings from 1146 patients (191 CAD and 955 Non-CAD) were analyzed for spectral differences between the two groups using Welch's spectral density estimate. Frequency spectra were estimated for systole and diastole segments, and time-frequency spectra were estimated for first (S1) and second (S2) heart sound segments. An ANCOVA model with terms for diagnosis, age, gender, and body mass index was used to evaluate statistical significance of the diagnosis term for each time-frequency component.Main results. Diastole and systole segments of CAD patients showed increased energy at frequencies 20-120 Hz; furthermore, this difference was statistically significant for the diastole. CAD patients showed decreased energy for the mid-S1 and mid-S2 segments and conversely increased energy before and after the valve sounds. Both S1 and S2 segments showed regions of statistically significant difference in the time-frequency spectra.Significance. Results from analysis of the diastole support findings of increased low-frequency energy from previous studies. Time-frequency components of S1 and S2 sounds showed that these two segments likely contain heretofore untapped information for risk assessment of CAD using phonocardiography; this should be considered in future works. Further development of features that build on these findings could lead to improved acoustic detection of CAD.

Model-based distributed node clustering and multi-speaker speech presence probability estimation in wireless acoustic sensor networks.

The knowledge of speech presence probability (SPP) plays an essential role in noise estimation and speech enhancement. Single channel SPP estimation and centralized multi-channel SPP estimation have been well studied. However, how to estimate SPP in wireless acoustic sensor networks (WASNs) remains a great challenge and few efforts can be found in this topic, particularly for WASN applications with multiple speakers. Accordingly, this paper is devoted to the problem of SPP estimation in WASNs and it presents a distributed model-based SPP estimation method for multi-speaker detection, which does not need any fusion center. A distributed k-means clustering method is first used to cluster the nodes into subnetworks, which detect different speakers. For each node in the subnetwork, the speech and noise power spectral densities are estimated locally by using a model-based method, then a distributed SPP estimator is developed and applied in every subnetwork. A distributed consensus method is used to obtain the distributed clustering and the distributed SPP estimation. Simulation results show that the proposed distributed clustering method can assign nodes into subnetworks based on their noisy observations. Moreover, the proposed distributed SPP estimator achieves robust speech detection performance under different noise conditions.

Metrics for vector quantization-based parametric speech enhancement and separation.

Speech enhancement and separation algorithms sometimes employ a two-stage processing scheme, wherein the signal is first mapped to an intermediate low-dimensional parametric description after which the parameters are mapped to vectors in codebooks trained on, for example, individual noise-free sources using a vector quantizer. To obtain accurate parameters, one must employ a good estimator in finding the parameters of the intermediate representation, like a maximum likelihood estimator. This leaves some unanswered questions, however, like what metrics to use in the subsequent vector quantization process and how to systematically derive them. This paper aims at answering these questions. Metrics for this are presented and derived, and their use is exemplified on a number of different signal models by deriving closed-form expressions. The metrics essentially take into account in the vector quantization process that some parameters may have been estimated more accurately than others and that there may be dependencies between the estimation errors.

Theorems on positive data: on the uniqueness of NMF.

We investigate the conditions for which nonnegative matrix factorization (NMF) is unique and introduce several theorems which can determine whether the decomposition is in fact unique or not. The theorems are illustrated by several examples showing the use of the theorems and their limitations. We have shown that corruption of a unique NMF matrix by additive noise leads to a noisy estimation of the noise-free unique solution. Finally, we use a stochastic view of NMF to analyze which characterization of the underlying model will result in an NMF with small estimation errors.