arXiv subjects

Simon Doclo

Publications and source records attributed to Simon Doclo.

At least 19 recordsLinked to original sources

arXiv2026

Multi-Source Position and Direction-of-Arrival Estimation Based on Euclidean Distance Matrices

A popular method to estimate the positions or directions-of-arrival (DOAs) of multiple sound sources using an array of microphones is based on steered-response power (SRP) beamforming. For a three-dimensional scenario, SRP-based methods require joint optimization of three continuous variables for position estimation or two continuous variables for DOA estimation, which can be computationally expensive when high localization accuracy is desired. In this paper, we propose novel methods for multi-source position and DOA estimation by exploiting properties of Euclidean distance matrices (EDMs) and their respective Gram matrices. All methods require estimated time-differences of arrival (TDOAs) between the microphones. In the proposed multi-source position estimation method, only a single continuous variable per source, representing the distance to a reference microphone, needs to be optimized. For each source, the optimal distance variable and set of candidate TDOA estimates are determined by minimizing a cost function defined using the eigenvalues of the Gram matrix. The estimated relative source positions are then mapped to absolute source positions by solving an orthogonal Procrustes problem. The proposed multi-source DOA estimation method eliminates the need for continuous variable optimization. The optimal set of candidate TDOA estimates is determined by minimizing a cost function defined using the eigenvalues of a rank-reduced Gram matrix. For two sources in a noisy and reverberant environment, experimental results for different source and microphone configurations with six microphones show that the proposed EDM-based method consistently outperforms the SRP-based method in terms of position and DOA estimation accuracy and run time.

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arXiv2026

A Study on Online Mask-based Beamforming Using Per-channel Masking for Spatially Distributed Microphones

Mask-based beamforming is a popular geometry-agnostic approach for speech enhancement, typically applying a single mask across all microphones to estimate the required covariance matrices. While effective for compact arrays, this strategy may be suboptimal for spatially distributed microphones, where signal characteristics may vary strongly across microphones. To effectively capture the spatial diversity across microphones, we extend the mask-based beamformer to a multi-channel formulation, where each microphone is pre-filtered by a separate mask before covariance estimation. To address time-varying acoustic scenes, caused by spectro-temporal nonstationarity, we adopt a frame-causal online implementation with a sliding window. Experiments with simulated compact arrays and distributed microphones show that multi-channel masking yields a benefit over using a single mask when microphone signals differ substantially, while retaining similar performance in compact arrays. We further demonstrate the robustness of the multi-channel masking approach by comparing oracle ideal ratio masks to blind DNN-based mask estimation.

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arXiv2026

Robust Soft-Constrained Spatially Selective Active Noise Control for Hearables Under Secondary Path Variations

Spatially selective active noise control (SSANC) hearables aim to attenuate noise from certain directions at the eardrum while preserving desired speech arriving from selected directions. Existing SSANC systems typically assume an accurate estimate of the secondary path from the loudspeaker to the inner error microphone. In practice, however, this path varies across users and device fits, which can degrade performance and compromise system stability. This paper proposes a robust soft-constrained optimization framework that computes a single control filter by minimizing the average cost over a set of secondary-path estimates derived from human measurements. Simulations show that the proposed approach achieves slightly lower mean performance than the matched case but substantially narrows the performance spread under secondary-path mismatch. Real-time experiments using the tested head-and-torso simulator show good agreement with the simulations.

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arXiv2026

Distributed Multichannel Wiener Filtering for Topology-Unconstrained Wireless Acoustic Sensor Networks

This paper introduces the topology-independent distributed multichannel Wiener filter (TI-dMWF), a novel algorithm for distributed node-specific signal estimation in wireless acoustic sensor networks (WASNs) with unconstrained topologies. The TI-dMWF enables each node in the network to compute its centralized multichannel Wiener filter solution by exchanging only low-dimensional fused signals, without requiring iterative estimation, unlike state-of-the-art approaches such as the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm. The TI-dMWF is proven optimal when each source is observed by either all nodes or only one node. Theoretical analysis and numerical simulations confirm that it achieves centralized estimation performance in a single run. Its latency as a function of the pruned-tree depth and its computational complexity are also analyzed. Its robustness is assessed in reverberant-room simulations under estimated second-order statistics, various network topologies, and deviations from the assumed observability model.

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arXiv2026

In-the-Loop Training of Deep Feedback Cancellation for Hearing Aids

Acoustic feedback limits the maximum gain in hearing aids. In addition to several approaches based on adaptive filtering, recently a deep-neural-network-based feedback cancellation (DFC) approach has been proposed, which is trained via an open-loop framework. Since open-loop-trained DFC (DFC-OL) can become unstable during inference at high gains, in this paper we propose an in-the-loop-trained DFC (DFC-IL) that integrates the DFC directly into the optimisation loop. This allows the model to be exposed to unstable conditions during training. A two-stage training strategy involving pre-training on stable systems and fine-tuning on a wider gain range enables DFC-IL to learn robust howling reduction. Experimental results on measured feedback paths demonstrate that in scenarios with small gains, the proposed DFC-IL performs similarly to DFC-OL, and both exceed the performance of adaptive filters. In scenarios with high amplification gains, DFC-IL clearly outperforms DFC-OL by maintaining system stability.

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arXiv2026

Flexible Multi-Channel Target Speaker Extraction Using Geometry-Conditioned Spatially Selective Non-linear Filters

Recently, a spatially selective non-linear filter (SSF) has been proposed for target speaker extraction, using the target direction-of-arrival (DOA) as a spatial cue. Since learned intermediate features are tied to the microphone geometry, the performance of the SSF degrades significantly when evaluated on mismatched array geometries. In this paper, we propose a geometry-conditioned SSF (GC-SSF), which incorporates a geometry-conditioning branch based on FiLM layers. Furthermore, we propose a feature that jointly encodes the DOA and the microphone positions (DOA-MPE). The conditioning branch modulates the intermediate feature maps of the SSF using the DOA-MPE feature to capture the spatial relationship between the microphone positions and the target speaker. Experimental results across circular, uniform linear, and random microphone arrays show that the proposed GC-SSF generalizes better to mismatched geometries while maintaining high spatial selectivity, demonstrating its ability to effectively adapt the filtering process to different array geometries

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arXiv2026

Distributed Multichannel Wiener Filtering for Wireless Acoustic Sensor Networks

[This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.] In a wireless acoustic sensor network (WASN), devices (i.e., nodes) can collaborate through distributed algorithms to collectively perform audio signal processing tasks. This paper focuses on the distributed estimation of node-specific desired speech signals using network-wide Wiener filtering. The objective is to match the performance of a centralized system that would have access to all microphone signals, while reducing the communication bandwidth usage of the algorithm. Existing solutions, such as the distributed adaptive node-specific signal estimation (DANSE) algorithm, converge towards the multichannel Wiener filter (MWF) which solves a centralized linear minimum mean square error (LMMSE) signal estimation problem. However, they do so iteratively, which can be slow and impractical. Many solutions also assume that all nodes observe the same set of sources of interest, which is often not the case in practice. To overcome these limitations, we propose the distributed multichannel Wiener filter (dMWF) for fully connected WASNs. The dMWF is non-iterative and optimal even when nodes observe different sets of sources. In this algorithm, nodes exchange neighbor-pair-specific, low-dimensional (fused) signals estimating the contribution of sources observed by both nodes in the pair. We formally prove the optimality of dMWF and demonstrate its performance in simulated speech enhancement experiments. The proposed algorithm is shown to outperform DANSE in terms of objective metrics after short operation times, highlighting the benefit of its iterationless design.

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arXiv2026

Fast-Converging Distributed Signal Estimation in Topology-Unconstrained Wireless Acoustic Sensor Networks

This paper focuses on distributed signal estimation in topology-unconstrained wireless acoustic sensor networks (WASNs) where sensor nodes only transmit fused versions of their local sensor signals. For this task, the topology-independent (TI) distributed adaptive node-specific signal estimation (DANSE) algorithm (TI-DANSE) has previously been proposed. It converges towards the centralized signal estimation solution in non-fully connected and time-varying network topologies. However, the applicability of TI-DANSE in real-world scenarios is limited due to its slow convergence. The latter results from the fact that, in TI-DANSE, nodes only have access to the in-network sum of all fused signals in the WASN. We address this low convergence speed by introducing an improved TI-DANSE algorithm, referred to as TI-DANSE+, in which updating nodes separately use the partial in-network sums of fused signals coming from each of their neighbors. Nodes can maximize the number of available degrees of freedom in their local optimization problem, leading to faster convergence. This is further exploited by combining TI-DANSE+ with a tree-pruning strategy that maximizes the number of neighbors at the updating node. In fully connected WASNs, TI-DANSE+ converges as fast as the original DANSE algorithm (the latter only defined for fully connected WASNs) while using peer-to-peer data transmission instead of broadcasting and thus saving communication bandwidth. If link failures occur, the convergence of TI-DANSE+ towards the centralized solution is preserved without any change in its formulation. Altogether, the proposed TI-DANSE+ algorithm can be viewed as an all-round alternative to DANSE and TI-DANSE which (i) merges the advantages of both, (ii) reconciliates their differences into a single formulation, and (iii) shows advantages of its own in terms of communication bandwidth usage.

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arXiv2026

Investigation of Speech and Noise Latent Representations in Single-channel VAE-based Speech Enhancement

Recently, a variational autoencoder (VAE)-based single-channel speech enhancement system using Bayesian permutation training has been proposed, which uses two pretrained VAEs to obtain latent representations for speech and noise. Based on these pretrained VAEs, a noisy VAE learns to generate speech and noise latent representations from noisy speech for speech enhancement. Modifying the pretrained VAE loss terms affects the pretrained speech and noise latent representations. In this paper, we investigate how these different representations affect speech enhancement performance. Experiments on the DNS3, WSJ0-QUT, and VoiceBank-DEMAND datasets show that a latent space where speech and noise representations are clearly separated significantly improves performance over standard VAEs, which produce overlapping speech and noise representations.

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arXiv2026

I-DCCRN-VAE: An Improved Deep Representation Learning Framework for Complex VAE-based Single-channel Speech Enhancement

Recently, a complex variational autoencoder (VAE)-based single-channel speech enhancement system based on the DCCRN architecture has been proposed. In this system, a noise suppression VAE (NSVAE) learns to extract clean speech representations from noisy speech using pretrained clean speech and noise VAEs with skip connections. In this paper, we improve DCCRN-VAE by incorporating three key modifications: 1) removing the skip connections in the pretrained VAEs to encourage more informative speech and noise latent representations; 2) using $β$-VAE in pretraining to better balance reconstruction and latent space regularization; and 3) a NSVAE generating both speech and noise latent representations. Experiments show that the proposed system achieves comparable performance as the DCCRN and DCCRN-VAE baselines on the matched DNS3 dataset but outperforms the baselines on mismatched datasets (WSJ0-QUT, Voicebank-DEMEND), demonstrating improved generalization ability. In addition, an ablation study shows that a similar performance can be achieved with classical fine-tuning instead of adversarial training, resulting in a simpler training pipeline.

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arXiv2026

DNN-Based Online Source Counting Based on Spatial Generalized Magnitude Squared Coherence

The number of active sound sources is a key parameter in many acoustic signal processing tasks, such as source localization, source separation, and multi-microphone speech enhancement. This paper proposes a novel method for online source counting by detecting changes in the number of active sources based on spatial coherence. The proposed method exploits the fact that a single coherent source in spatially white background noise yields high spatial coherence, whereas only noise results in low spatial coherence. By applying a spatial whitening operation, the source counting problem is reformulated as a change detection task, aiming to identify the time frames when the number of active sources changes. The method leverages the generalized magnitude-squared coherence as a measure to quantify spatial coherence, providing features for a compact neural network trained to detect source count changes framewise. Simulation results with binaural hearing aids in reverberant acoustic scenes with up to 4 speakers and background noise demonstrate the effectiveness of the proposed method for online source counting.

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arXiv2025

Reference Microphone Selection for Guided Source Separation based on the Normalized L-p Norm

Guided Source Separation (GSS) is a popular front-end for distant automatic speech recognition (ASR) systems using spatially distributed microphones. When considering spatially distributed microphones, the choice of reference microphone may have a large influence on the quality of the output signal and the downstream ASR performance. In GSS-based speech enhancement, reference microphone selection is typically performed using the signal-to-noise ratio (SNR), which is optimal for noise reduction but may neglect differences in early-to-late-reverberant ratio (ELR) across microphones. In this paper, we propose two reference microphone selection methods for GSS-based speech enhancement that are based on the normalized $\ell_p$-norm, either using only the normalized $\ell_p$-norm or combining the normalized $\ell_p$-norm and the SNR to account for both differences in SNR and ELR across microphones. Experimental evaluation using a CHiME-8 distant ASR system shows that the proposed $\ell_p$-norm-based methods outperform the baseline method, reducing the macro-average word error rate.

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arXiv2025

Subjective quality evaluation of personalized own voice reconstruction systems

Own voice pickup technology for hearable devices facilitates communication in noisy environments. Own voice reconstruction (OVR) systems enhance the quality and intelligibility of the recorded noisy own voice signals. Since disturbances affecting the recorded own voice signals depend on individual factors, personalized OVR systems have the potential to outperform generic OVR systems. In this paper, we propose personalizing OVR systems through data augmentation and fine-tuning, comparing them to their generic counterparts. We investigate the influence of personalization on speech quality assessed by objective metrics and conduct a subjective listening test to evaluate quality under various conditions. In addition, we assess the prediction accuracy of the objective metrics by comparing predicted quality with subjectively measured quality. Our findings suggest that personalized OVR provides benefits over generic OVR for some talkers only. Our results also indicate that performance comparisons between systems are not always accurately predicted by objective metrics. In particular, certain disturbances lead to a consistent overestimation of quality compared to actual subjective ratings.

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arXiv2025

A Steered Response Power Method for Sound Source Localization With Generic Acoustic Models

The steered response power (SRP) method is one of the most popular approaches for acoustic source localization with microphone arrays. It is often based on simplifying acoustic assumptions, such as an omnidirectional sound source in the far field of the microphone array(s), free field propagation, and spatially uncorrelated noise. In reality, however, there are many acoustic scenarios where such assumptions are violated. This paper proposes a generalization of the conventional SRP method that allows to apply generic acoustic models for localization with arbitrary microphone constellations. These models may consider, for instance, level differences in distributed microphones, the directivity of sources and receivers, or acoustic shadowing effects. Moreover, also measured acoustic transfer functions may be applied as acoustic model. We show that the delay-and-sum beamforming of the conventional SRP is not optimal for localization with generic acoustic models. To this end, we propose a generalized SRP beamforming criterion that considers generic acoustic models and spatially correlated noise, and derive an optimal SRP beamformer. Furthermore, we propose and analyze appropriate frequency weightings. Unlike the conventional SRP, the proposed method can jointly exploit observed level and time differences between the microphone signals to infer the source location. Realistic simulations of three different microphone setups with speech under various noise conditions indicate that the proposed method can significantly reduce the mean localization error compared to the conventional SRP and, in particular, a reduction of more than 60% can be archived in noisy conditions.

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arXiv2025

Closed-Form Successive Relative Transfer Function Vector Estimation based on Blind Oblique Projection Incorporating Noise Whitening

Relative transfer functions (RTFs) of sound sources play a crucial role in beamforming, enabling effective noise and interference suppression. This paper addresses the challenge of online estimating the RTF vectors of multiple sound sources in noisy and reverberant environments, for the specific scenario where sources activate successively. While the RTF vector of the first source can be estimated straightforwardly, the main challenge arises in estimating the RTF vectors of subsequent sources during segments where multiple sources are simultaneously active. The blind oblique projection (BOP) method has been proposed to estimate the RTF vector of a newly activating source by optimally blocking this source. However, this method faces several limitations: high computational complexity due to its reliance on iterative gradient descent optimization, the introduction of random additional vectors, which can negatively impact performance, and the assumption of high signal-to-noise ratio (SNR). To overcome these limitations, in this paper we propose three extensions to the BOP method. First, we derive a closed-form solution for optimizing the BOP cost function, significantly reducing computational complexity. Second, we introduce orthogonal additional vectors instead of random vectors, enhancing RTF vector estimation accuracy. Third, we incorporate noise handling techniques inspired by covariance subtraction and whitening, increasing robustness in low SNR conditions. To provide a frame-by-frame estimate of the source activity pattern, required by both the conventional BOP method and the proposed method, we propose a spatial-coherence-based online source counting method. Simulations are performed with real-world reverberant noisy recordings featuring 3 successively activating speakers, with and without a-priori knowledge of the source activity pattern.

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arXiv2025

Binaural Localization Model for Speech in Noise

Binaural acoustic source localization is important to human listeners for spatial awareness, communication and safety. In this paper, an end-to-end binaural localization model for speech in noise is presented. A lightweight convolutional recurrent network that localizes sound in the frontal azimuthal plane for noisy reverberant binaural signals is introduced. The model incorporates additive internal ear noise to represent the frequency-dependent hearing threshold of a typical listener. The localization performance of the model is compared with the steered response power algorithm, and the use of the model as a measure of interaural cue preservation for binaural speech enhancement methods is studied. A listening test was performed to compare the performance of the model with human localization of speech in noisy conditions.

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arXiv2025

Binaural Speech Enhancement Using Complex Convolutional Recurrent Networks

From hearing aids to augmented and virtual reality devices, binaural speech enhancement algorithms have been established as state-of-the-art techniques to improve speech intelligibility and listening comfort. In this paper, we present an end-to-end binaural speech enhancement method using a complex recurrent convolutional network with an encoder-decoder architecture and a complex LSTM recurrent block placed between the encoder and decoder. A loss function that focuses on the preservation of spatial information in addition to speech intelligibility improvement and noise reduction is introduced. The network estimates individual complex ratio masks for the left and right-ear channels of a binaural hearing device in the time-frequency domain. We show that, compared to other baseline algorithms, the proposed method significantly improves the estimated speech intelligibility and reduces the noise while preserving the spatial information of the binaural signals in acoustic situations with a single target speaker and isotropic noise of various types.

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arXiv2025

Incremental Averaging Method to Improve Graph-Based Time-Difference-of-Arrival Estimation

Estimating the position of a speech source based on time-differences-of-arrival (TDOAs) is often adversely affected by background noise and reverberation. A popular method to estimate the TDOA between a microphone pair involves maximizing a generalized cross-correlation with phase transform (GCC-PHAT) function. Since the TDOAs across different microphone pairs satisfy consistency relations, generally only a small subset of microphone pairs are used for source position estimation. Although the set of microphone pairs is often determined based on a reference microphone, recently a more robust method has been proposed to determine the set of microphone pairs by computing the minimum spanning tree (MST) of a signal graph of GCC-PHAT function reliabilities. To reduce the influence of noise and reverberation on the TDOA estimation accuracy, in this paper we propose to compute the GCC-PHAT functions of the MST based on an average of multiple cross-power spectral densities (CPSDs) using an incremental method. In each step of the method, we increase the number of CPSDs over which we average by considering CPSDs computed indirectly via other microphones from previous steps. Using signals recorded in a noisy and reverberant laboratory with an array of spatially distributed microphones, the performance of the proposed method is evaluated in terms of TDOA estimation error and 2D source position estimation error. Experimental results for different source and microphone configurations and three reverberation conditions show that the proposed method considering multiple CPSDs improves the TDOA estimation and source position estimation accuracy compared to the reference microphone- and MST-based methods that rely on a single CPSD as well as steered-response power-based source position estimation.

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