Signal Processing
Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding
Adam Mounir, Stella Douka, Arnault H. Caillet et al. (+2 more)
Convolutional
EEG decoders are trained at a fixed width, usually set by their authors on other data. Growing methods add
neurons during training where the loss could decrease the most, but whether they improve compared to a reference width is untested on
EEG. Here, we grow three convolutional backbones on 12 motor-imagery
datasets under three protocols and compare each with its reference model per subject. The growing ShallowFBCSPNet scores 2.9 points above its reference model with only half the parameters (0.57x), SCCNet changes by at most 1.2 points. Deep4Net growing models show decreased accuracy, but they require adaptation that prevent to compare faithfully the results. These differences follow the selection step, which keeps a candidate
neuron relying on a dynamic threshold from singular values decomposition. Overall, these results suggest that growth helps when its criterion can rank the candidate
neurons, and that the rate of skipped
neuron addition tells where a decoder can be grown small from scratch.
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All authors Adam Mounir, Stella Douka, Arnault H. Caillet, Bruno Aristimunha, Sylvain Chevallier
Audio and Speech Processing · Cross-listed from cs.CL
In-Context Adaptation of Encoder-Decoder Models in Speech Recognition
Yen Meng, Sharon Goldwater and Hao Tang
In-context learning offers an appealing approach to adapt automatic
speech recognition (ASR) models to new speakers, accents, and domains by providing
speech-text pairs as demonstrations at inference time. Recent work shows that some LLM-based
speech models are capable of ASR in-context adaptation, when providing interleaved
speech-text demonstrations. In this work, we ask whether in-context adaptation is an inherent ability for all encoder-decoder models. We study two forms of demonstration, collated and interleaved demonstration, across six encoder-decoder models, spanning conventional cross-
attention-based and LLM-based architectures. We find that all tested models are able to perform in-context adaptation out of the box, achieving up to 30% relative improvement in the oracle experiments and up to 23% using first-pass hypotheses. Through controlled experiments on three English
datasets, we show that lexical and speaker information both contribute to successful adaptation. While interleaved demonstration is effective in certain cases, collated demonstration brings consistent adaptation across the board. Our results suggest that in-context adaptation for ASR is not unique to specific architectures, training, or demonstration approaches.
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Signal Processing
Interference Analysis in Spectrum Sharing Scenarios between Fixed Services and 6G Mobile Networks
Samuel S. Silva, Igor. M. Guerreiro, Yuri. C. B. Silva et al. (+1 more)
This study investigates the feasibility of coexistence between an incumbent fixed service (FS) and a 6G network in the same location under spectrum sharing. For coexistence to be possible, a protection criterion must be met. Regulatory agencies define this criterion as the ratio of inter-network interference to noise at the FS receiver. Simulation results indicate that this coexistence is not possible without the implementation of some interference mitigation strategy. To this end, a distance-based protection zoning and two user selection strategies were investigated from the fixed service perspective, assuming the 6G network is in uplink. The results indicate that meeting the protection criterion can be achieved when only few users in uplink are selected, while still providing optimistic capacity performance for such selected users in the shared band.
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All authors Samuel S. Silva, Igor. M. Guerreiro, Yuri. C. B. Silva, Behrooz Makki
Signal Processing
Unified Target-Speaker ASR with Text and Enrollment Speech Cues
Yuxiang Mei, Yuchen Yan, Dongxing Xu et al. (+2 more)
Target-speaker automatic
speech recognition (TS-ASR) aims to recognize a designated speaker while suppressing interfering
speech in multi-talker environments. Conventional TS-ASR typically relies on an enrollment utterance, whereas text-guided methods use known lexical content, such as a wake word, to identify the target speaker from the observed mixture. These two cues provide complementary information but are usually studied separately. We propose a Unified Dual-Cue TS-ASR framework that supports text cues, enrollment
speech, or both within a single model. Text cues interact with the mixture representation to extract target-speaker information conditioned on known lexical content, while an independent enrollment utterance provides complementary speaker information. Cross-
attention cue-conditioning modules are integrated into shared Conformer blocks, and negative-cue sampling provides cue-validity supervision during dual-cue training. Experiments on 30,000 two-speaker mixtures across five recording/domain conditions and four oracle text-cue lengths show that, with five-character text cues, the concatenated dual-cue method achieves 8.80% CER, compared with 17.32% for text-only and 29.06% for enrollment-only inference. It also outperforms parallel dual-cue fusion (9.49% CER) and yields lower dual-cue CER across all five evaluation subsets. These results demonstrate the benefit of jointly exploiting complementary lexical and speaker information for target-speaker ASR.
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All authors Yuxiang Mei, Yuchen Yan, Dongxing Xu, Jiaen Liang, Yanhua Long
Audio and Speech Processing · Cross-listed from cs.SD
Controlling Speaking Rate in Autoregressive TTS via Activation Steering
Francesco Verdini, Antonis Asonitis, Aref Farhadipour et al. (+4 more)
Autoregressive text-to-
speech (TTS) systems synthesize natural
speech but, once trained, offer little control over speaking rate. We show that speaking rate can be steered at inference time, without retraining, by clamping a single decoder block's activation along a discovered speed axis. A decoder-block analysis recovers the rate axis, a neutral operating point, and a per-step intensity scale; at inference, the activation's projection onto this axis is set to a fixed scalar. Learning this direction from synthetically time-stretched and time-compressed
speech yields rate control that largely preserves speaker identity, generalizes across model architectures, and maintains high naturalness in objective and human evaluations. Unlike standard additive steering, which breaks at the slow extreme, clamping remains stable on all three systems tested; at moderate targets, the better rule depends on the model. Finally, we show that rate information is decodable across layers but causally steerable only within a mid-depth window, and demonstrate the effectiveness of our approach on the public Seed-TTS-Eval
benchmark.
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All authors Francesco Verdini, Antonis Asonitis, Aref Farhadipour, Marzieh Razavi, Pierre-Edouard Honnet, Vijeta Avijeet, Juan Pablo Zuluaga Gomez
Systems and Control
Deep Behaviour Cloning of Model Predictive Control for Real-Time Operation of a Hydrogen-Diesel Dual-Fuel Engine
Alexander Winkler, Neeraj Naduvath Mana, David Gordon et al. (+1 more)
Hydrogen-diesel dual-fuel (H2DF) combustion engines offer a promising pathway for decarbonising hard-to-electrify transport sectors, yet their highly nonlinear dynamics and coupled process variables demand constraint-aware control strategies. Model Predictive Control (MPC) meets these requirements but requires an online optimisation in every combustion cycle, which limits deployment on low-cost embedded hardware. This paper trains a feedforward deep neural network (DNN) by behaviour cloning (BC) to imitate an MPC expert, using 86,000 engine cycles of demonstration data collected at 1500 min-1 on a modified Cummins QSB 4.5-litre hydrogen dual-fuel engine. Two variants, one with process feedback and one without, are validated experimentally. Both track unseen fast-transient load steps (3-8 bar indicated mean effective pressure, IMEP) with normalised root mean square error (NRMSE) values of 7.80% and 9.03% against the expert's 8.01%, while keeping mean NOx and particulate matter emissions at or below those of the expert. Inference takes 2 ms or less on a Raspberry Pi 400 (ARM Cortex-A72 at 2.2 GHz), including 1 ms communication latency, compared to up to 7 ms for the MPC expert, a 3.5x speedup. Open-loop profiling on a low-cost ESP32 microcontroller at 180 MHz gives 4.3 ms per inference, 4x faster than required for the 18 ms cycle window. Beyond the training range the cloned policy saturates its controls but exceeds the pressure-rise-rate limit. To the authors' knowledge, this is the first experimental BC controller for cycle-to-cycle combustion control of an internal combustion engine (ICE), trained from demonstrations recorded on the engine itself.
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All authors Alexander Winkler, Neeraj Naduvath Mana, David Gordon, Jakob Andert
Audio and Speech Processing · Cross-listed from cs.SD
Tokens Change, Structure Endures: Spectral Watermarking for Generated Speech
Kanghwi Lee, Kyeongseok Jeong and Jeongmin Liu
Watermarking is a promising tool for establishing the provenance of AI-generated
speech. While many neural audio watermarking methods rely on a separately trained watermark generator,
token-level watermarking is a training-free alternative that operates directly during generation. Its main weakness is retokenization: decoding generated
speech to a waveform and encoding it again can change
token identities and erode the watermark. To make the watermark robust to these changes, we propose Redwing, REtokenization-Durable Watermarking IN Generation. It builds a graph from the
token substitutions observed under retokenization, whose
Laplacian yields a basis that assigns similar values to
tokens likely to substitute for one another. Over this basis,
embedding and detection functions are jointly optimized to preserve watermark signal through retokenization while limiting
embedding distortion and detector variability on unwatermarked
speech. On the Moshi full-duplex system, after eight consecutive passes of Mimi resynthesis, Redwing achieves 80.7% TPR at a calibrated 1% FPR, compared with 8.3% for KGW and at most 7.3% for WMAR. It also has the highest TPR after eight passes through three other neural codecs (77.5-93.0%), and the gains generalize to TTS models at a
speech-quality cost close to that of KGW. These results show that retokenization is not merely a source of noise: its transition structure can be exploited as a design principle for robust
token-level watermarking.
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Audio and Speech Processing
YuE2: Unifying Symbolic and Audio Music Generation at Frontier Quality
Ruibin Yuan, Jiahao Pan, Junyan Jiang et al. (+32 more)
Symbolic models make melody, harmony, rhythm, and form explicit but typically stop before a finished recording; audio models produce complete songs while leaving composition implicit. We introduce YuE2, which unifies symbolic and audio music generation at frontier quality through symbolic planning. A single AR-NAR Mixture-of-
Transformers (MoT) first writes a readable score specifying melody and harmony, expands it into semantic music
tokens, and realizes it as full-song audio. In comparisons using the same checkpoint, experts prefer symbolic planning for overall quality and musicality, with 49.3% of overall preferences versus 34.6% without planning. Experts also favor the unified model over a separate
language model and
diffusion Transformer. On WildSongBench, YuE2 scores 6.73 on SongBench Global Avg, exceeding all evaluated public baselines. Selecting from eight candidates (best-of-8), YuE2 reaches 6.96, the highest observed mean among all evaluated systems. Expert listening further establishes its competitiveness with proprietary song generators, favoring best-of-8 over Suno v4.5 and yielding nearly balanced preferences against Suno v5. To learn this generation process from recordings without aligned scores, we introduce MERT2 and SheetSage2 to supply semantic and symbolic supervision. MERT2 sets a new state of the art in music representation learning, surpassing previous best results on 14 of 15 MARBLE metrics; SheetSage2 leads 12 of 15
benchmark-metric pairs in our lead-sheet transcription comparison. The same checkpoint follows score edits while largely preserving unedited musical content and generates zero-shot covers without cover-specific training. Its readable score also enables agentic music editing, with external
language models translating user feedback into revisions of the composition.
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All authors Ruibin Yuan, Jiahao Pan, Junyan Jiang, Zhiyue Wu, Ziya Zhou, Jiankai Sun, Yizhi Li, Ge Zhang, Yicheng Gu, Zeyue Tian, Junyu Dai, Hanfeng Lin, Kai Li, Shangda Wu, Xuanjie Liu, Jiaming Wang, Zihan Liu, Yue Wang, Yinghao Ma, Hanzhi Yin, Kangrui Chen, Xinyue Zhang, Ziyang Ma, Mengqi Liao, Hejia Zhao, Guowei Huang, Chao Yan, Lei Ke, Jianwei Yu, Bei Liu, Joe Guo, Liumeng Xue, Gus Xia, Wei Xue, Yike Guo
Audio and Speech Processing · Cross-listed from cs.SD
Transformer-based Neural Beamforming for Real-Time Speech Enhancement on Smart Low-Power Hearable Devices
Luca Bompani, Marco Fariselli, Giovanni Oltrecolli et al. (+1 more)
Accurate, efficient, and low-latency spatial beamforming is a key component in emerging smart hearable devices, enhancing
speech while suppressing noise and interference. However, handling multiple input sources under strict real-time constraints poses significant challenges for the low-power, resource-constrained microcontroller units (MCUs) used in hearables. We present an optimized methodology for the real-time execution of a neural-network-based minimum variance distortionless response (MVDR) beamformer on MCUs. Using six microphones and a three-stage mixed-precision scheme (float32 MVDR, int8 CNN, float16
Transformer), the pipeline pairs a CNN that estimates the MVDR weights with a lightweight
Transformer that applies a per-frame correction. By time-slicing weight estimation with beamforming, it achieves a 15~ms per-frame latency while refreshing a complete set of CNN-derived weights every 564~ms. The deployed mixed-precision pipeline attains a short-time objective intelligibility (STOI) of 97.65%, a scale-invariant signal-to-noise ratio (SI-SNR) of 20.26~dB, and a wideband PESQ of 3.676 at an average power of 45.9~mW. A
speech activity detection (SAD) module (98.5% accuracy, 0.62~mJ per inference) bypasses the pipeline during silence; under realistic deployment conditions, the system exceeds the 16~h all-day target on a 100~mAh battery, with an estimated lifetime of up to $\sim$20~h. To our knowledge, this is the first real-time multi-channel
Transformer-based neural beamforming pipeline deployed on an MCU-class device.
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All authors Luca Bompani, Marco Fariselli, Giovanni Oltrecolli, Francesco Conti
Systems and Control · Cross-listed from cs.IT
Concurrent Coded Signal-Multiplexing Ranging for Half-Duplex Asynchronous Networks
Zijian Zhang and Yuan Shen
Signal-multiplexing network ranging (SM-NR) shares broadcasts across node pairs, but its sequential operation leads to a ranging cycle that grows linearly with network size. This paper proposes a concurrent coded SM-NR (CC-SM-NR) framework for asynchronous half-duplex networks. Firstly, the CC-SM-NR protocol coordinates concurrent transmissions through binary transmit-listen codewords. The transmit-listen schedule defined by these codewords ensures reciprocal observations subject to a finite concurrency limit. Then, we derive the exact minimum number of transmit-listen rounds without a concurrency limit, which reveals that the minimum grows logarithmically with network size. To account for practical scenarios, we establish the necessary and sufficient conditions for the constant-weight feasibility of codewords under a finite concurrency limit. Subsequently, we propose a low-complexity scheduling
algorithm that achieves the minimum round count within the constant-weight codeword class. To support higher observation redundancy, this scheduling design is extended through a greedy construction. Finally, simulation results demonstrate the effectiveness of the proposed schemes for network ranging.
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Audio and Speech Processing · Cross-listed from cs.SD
DuraS2ST: Chain-of-Thought and Reinforcement Learning for Duration-Aligned Speech-to-Speech Translation
Yayue Deng, Dingdong Wang, Yuxuan Hu et al. (+7 more)
Speech-to-
speech translation (S2ST) in time-sensitive applications such as
video dubbing requires not only semantic fidelity and speaker preservation, but also strict duration consistency to avoid audio-visual misalignment. However, existing S2ST systems largely generate target
speech without explicit temporal planning, making duration control an unresolved challenge. We introduce DuraS2ST, a duration-aligned
reasoning framework that enables a single
speech language model to first generate an explicit chain-of-thought (CoT) for planning target wording and phonetic length, and then synthesize the corresponding
speech tokens. To support this paradigm, we construct DuraSet-440K, a high-quality duration-aligned CoT corpus for supervised initialization. We further optimize the model with multi-modal multi-dimensional reinforcement learning, using a Duration Margin Reward to balance translation quality and duration consistency, and Modality-Aware Reward Attribution to assign rewards to appropriate
token spans. Experiments on CVSS-T show that DuraS2ST achieves a strong balance between translation quality and duration consistency, outperforming competitive open-source and commercial baselines. Project page: https://github.com/Mia11939/DuraS2ST.
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All authors Yayue Deng, Dingdong Wang, Yuxuan Hu, Jinyu Li, Yanqing Liu, Yuanyuan Wang, Weidong Chen, Helen M. Meng, Shujie Liu, Xixin Wu
Signal Processing
Error Performance Analysis of CFO-Impaired OFDM-Based Rate-Splitting SCMA
Minerva Priyadarsini, Kuntal Deka, Zilong Liu et al. (+1 more)
Rate-splitting sparse code multiple access (RS-SCMA) superimposes QAM-modulated common messages and SCMA-encoded private messages over the same resource elements. This paper analyzes the bit error rate (BER) performance of orthogonal frequency division multiplexing (OFDM)-based RS-SCMA in the presence of carrier frequency offset (CFO) over frequency-selective Rayleigh fading channels. A finite-alphabet BER framework is developed for common-layer detection, successive interference cancellation (SIC), and private-layer SCMA detection. The proposed analysis explicitly retains the common and private-layer signal components and models the unresolved intra- and inter-block CFO leakage as conditionally Gaussian interference using its conditional second-order statistics. Residual interference caused by erroneous common-layer cancellation and the effect of a tunable message-splitting factor are also incorporated. The results show that CFO produces a high-SNR BER floor that increases with the normalized frequency offset. Under the considered conditions, the overloaded SCMA private layer is more severely affected and dominates the overall BER. Simulations with perfect SIC further show that eliminating common-layer decision errors has only a marginal impact on the private-layer BER floor, indicating that the dominant degradation arises from CFO-impaired private-layer detection rather than SIC error propagation. Monte Carlo simulations validate the derived BER expressions and demonstrate their accuracy in capturing the layer-wise and overall BER performance over the considered CFO range.
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All authors Minerva Priyadarsini, Kuntal Deka, Zilong Liu, Sanjeev Sharma
Signal Processing
Wave-Domain Semantic Equalization Using a Practical Dynamic Metasurface Antenna with Strong Mutual Coupling
Yassine Sghaier, Emilio Calvanese Strinati and Philipp del Hougne
Semantic mismatch between independently trained AI-native
agents in heterogeneous networks can impair semantic communications. Hybrid analog-digital semantic equalization can align the incompatible latent representations without retraining the semantic transceivers. We study a practical realization of this approach based on a fabricated dynamic metasurface
antenna (DMA), an emerging low-cost, low-power, ultracompact technology for hybrid analog-digital beamforming. We model the DMA-assisted channel using multiport-network theory (MNT), accounting for mutual coupling (MC), structural scattering, and binary lossy tuning states. We use the experimentally estimated MNT parameters of our fabricated 19-GHz DMA prototype with strong MC. We jointly optimize digital pre- and post-equalizers and the DMA at a reference receiver geometry for latent-space
alignment, then freeze the digital stages and adapt only the DMA after receiver motion. In our CIFAR-10 image-classification task at the receiver, the jointly optimized system achieves 94.5% accuracy, while DMA-only adaptation restores a median accuracy of 90.3% after receiver motion. Our semantic-aware wave-domain adaptation substantially outperforms semantic-unaware
benchmarks. We further observe that varying the DMA configuration across channel uses provides little additional benefit for either semantic-aware or semantic-unaware optimization.
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Audio and Speech Processing
Domain-Adaptive Dual-Gating Mixture of Experts for Generalizable Speech Deepfake Detection
Siqing Qin, Zhe Li, Kong Aik Lee et al. (+1 more)
Recent advances in
speech deepfake detection (SDD) have leveraged the Mixture of Experts (MoE) to enhance generalization capacity. However, existing gating networks often overlook the acoustic and temporal cues of deepfakes. In this work, we propose a novel domain-adaptive dual-gating MoE (DADGMoE) framework for SDD under unseen attack types and acoustic conditions. Our innovative dual-gating mechanism leverages Sinc-layer-based filters to process both low-level acoustic signals (raw waveforms) and high-level
speech representations from a large self-supervised learning (SSL) model. It further incorporates domain prototypes to guide expert routing based on implicit deepfake patterns. The lightweight affine experts process the routed inputs. Experiments show that our DADGMoE significantly outperforms the baseline, achieving up to a 40.8% relative EER reduction on challenging out-of-
dataset benchmarks. This framework demonstrates superior generalization capabilities and efficient design.
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All authors Siqing Qin, Zhe Li, Kong Aik Lee, Man-Wai Mak
Audio and Speech Processing
DGS-MLDG: Domain Gradient Surgery Guided Meta-Learning for Domain Generalization in Speech Deepfake Detection
Siqing Qin, Kong Aik Lee, Youzhi Tu et al. (+2 more)
Speech deepfake detection faces significant challenges due to domain shifts. Domain generalization (DG), particularly meta-learning for domain generalization (MLDG), offers a promising solution by simulating and mitigating domain shifts. However, MLDG is often hindered by conflicting gradients between its meta-train and meta-test objectives, leading to suboptimal performance. To address this problem, we propose domain gradient surgery (DGS), a meta-learning method that resolves conflicts through an asymmetric projection strategy. DGS removes the destructive component from the meta-test gradient, ensuring a conflict-free optimization trajectory versus the meta-train gradient. Furthermore, we introduce layer-wise DGS (LW-DGS), an efficient variant of DGS that dynamically identifies and intervenes only conflict-prone layers. Extensive experiments on challenging
benchmarks demonstrate that DGS-MLDG and LW-DGS-MLDG achieve an average relative EER reduction of 5.29% and 4.04%, respectively.
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All authors Siqing Qin, Kong Aik Lee, Youzhi Tu, Eng Siong Chng, Man-Wai Mak
Signal Processing · Cross-listed from cs.LG
Geometric Inductive Biases for Semi-Supervised Equalization: The Constellation-Aware Transformer
Avi Caciularu
Decoding signals over unknown channels with minimal pilot overhead is a critical challenge in next-generation communications. Existing deep learning approaches typically rely on generic encoders that struggle to model long-range temporal dependencies or efficiently capture the channel's physical properties from scarce data. We argue that standard architectures suffer from agnostic estimation gaps, as they must implicitly learn the constellation geometry that is already known. We introduce the Constellation-Aware
Transformer (CAT), a novel architecture that explicitly injects
geometric inductive biases into the equalization process. CAT is composed of a stack of custom TransFIRmer blocks, which use an "early interaction" paradigm to co-process received signals and ideal constellation symbols. Each block features a split Feed-Forward Network that applies a Finite Impulse Response (FIR)-inspired filter for deconvolution and a parallel MLP for
geometric refinement. We show that this design is structurally aligned with the optimal linear (
MIMO Wiener) receiver: its
attention can implement a matched-filter bank, and its bidirectional FIR branch provides the non-causal filtering that block MMSE equalization requires. In the semi-supervised setting, CAT needs fewer pilots than VAE and standard
Transformer baselines: on two of our three ISI channels, it reaches a lower SER with 64 pilots than they do with 128.
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Systems and Control
Nonlinear Participation Factor-based Power System Model Reduction Addressing Near-Resonance Conditions
Mahsa Sajjadi and Kai Sun
This paper proposes an adaptive model reduction approach based on nonlinear participation factors (NPFs) which determines the most effective selection of unimportant generators in a power system to be linearized to accelerate time-domain simulation for the entire system. The method enables a dynamic transition between the full-order model, and a hybrid reduced model. It uses modal energies to rank system modes under contingencies, and computes NPFs for highly energized modes based on Normal Form theory. To accelerate the computation of NPFs and make it achievable online, a tensor contraction technique is introduced. The proposed approach is tested on a 48-machine, 140-bus NPCC system using both partitioned and unpartitioned strategies. It demonstrates significant simulation speedup while preserving better accuracy than a linear participation factor-based method if the nonlinear behaviors of the system cannot be ignored, especially when a near-resonance condition is presented.
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Signal Processing
Joint Transceiver and Group Index Modulation Design for Wideband Integrated Sensing and Communications Under Beam Squint
Kaiwen Zheng and Shijian Gao
The combination of ultra-wide bandwidth and large-scale multiple-input multiple-output arrays with frequency-independent phase shifters induces beam squint, causing frequency and distance dependent array gain variations across subcarriers and degrading integrated sensing and communications performance. Transceiver beamforming and group index modulation (GIM) can alleviate the impact of beam squint on communication and sensing performance. However, their designs are coupled in the frequency domain, as beamforming shapes the subcarrier responses on which GIM grouping and activation depend. To address this coupling, we propose a joint transceiver beamforming and GIM design that maximizes a weighted combination of normalized worst-case communication and sensing distances. An alternating optimization
algorithm jointly refines the transceiver beamformers and GIM configuration. Simulation results show that the proposed joint design reduces bit error rate in the high signal-to-noise ratio region and achieves a more favorable communication-sensing trade-off, while maintaining an average sensing AUC comparable to that of the considered GIM based
benchmark schemes.
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Audio and Speech Processing
Pruned CTC for Memory-Efficient Large-Vocabulary ASR Training
Yifan Yang, Xiaoyu Yang, Zengrui Jin et al. (+10 more)
Connectionist temporal classification (CTC) naturally supports offline and streaming
speech recognition with utterance-level supervision, but conventional implementations materialize frame-by-vocabulary activations in
memory, making CTC training with native LLM vocabularies prohibitively
memory-intensive. A key observation is that every valid CTC
alignment uses only target
tokens and blank, and their union across a batch typically forms a small subset of the full vocabulary. We introduce Pruned CTC, which restricts
alignment computation to this subset while retaining full-vocabulary normalization. We prove that this vocabulary reduction is exactly equivalent to full-vocabulary CTC in loss and gradients. Head-and-loss activation
memory no longer scales linearly with vocabulary size. We further apply finite-beam
alignment pruning. Building on Pruned CTC, we develop LLM-CTC, which adapts pretrained LLMs for non-autoregressive ASR while retaining causal
attention and native vocabularies, and extend it to bounded-history streaming, avoiding chunk-level
speech–text
alignments. Experiments show that, with Zipformer-M encoder and 180K vocabulary, Pruned CTC reduces full-step
memory by 5.1$\times$ with only 17% step-time overhead. Across three corpora, it matches standard CTC accuracy. On GigaSpeech, across six Qwen3 model sizes from 0.6B to 32B, LLM-CTC remains within 7% relative WER of LLM-CE with 7 to 10$\times$ faster recognition; when fine-tuning Qwen3-ASR for bounded-history streaming, LLM-CTC remains within 3% relative WER of matched offline models on the test set. Together, these results establish Pruned CTC as a scalable sequence objective for native-vocabulary LLM ASR across offline and streaming settings.
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All authors Yifan Yang, Xiaoyu Yang, Zengrui Jin, Xian Shi, Yuxuan Wang, Yu Xi, Ziyang Ma, Qi Chen, Ruiyang Xu, Hui Wang, Dongchao Yang, Jin Xu, Xie Chen
Systems and Control
Hierarchical Multi-agent Reinforcement Learning for Warehouse Robot Coordination under Communication Loss
Weihao Sun, Gehui Xu and Andreas A. Malikopoulos
In this paper, we propose a hierarchical multi-
agent reinforcement learning framework for coordinating
robot teams in warehouse environments under communication loss. We partition the
robot team into groups, with centralized coordination within each group and distributed coordination across groups. Each group uses a recurrent predictor to estimate unavailable interaction information due to communication loss. A higher-level policy then generates a compact coordination reference that conditions the local control policy within each group. A predictive safety filter evaluates and modifies the proposed controls when they violate safety constraints. Simulation results show improved task completion under communication loss, reduced communication growth as the team size increases, and safe operation in the tested scenarios.
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Systems and Control · Cross-listed from math.OC
lapanda: A Matrix-Free Differentiable Solver for Nonconvex Constrained Optimization Layers
Yuankun Chen, Zifei Nie, Kangyu Lin et al. (+2 more)
Differentiable optimization brings the structural guarantees of mathematical optimization to network pipelines, allowing them to be trained end-to-end. However, its application remains challenging for nonconvex constrained problems, as existing differentiable solvers often suffer from limited modeling expressiveness due to their reliance on specialized problem structures, while also incurring substantial computation time and
memory overhead in both the forward and backward passes. To address these challenges, we propose lapanda, a
matrix-free differentiable solver for nonconvex optimization with general constraints. It reformulates the problem to a sequence of augmented Lagrangian subproblems, each handled by a first-order inner solver through a proximal averaged quasi-Newton
algorithm with adaptive linesearch, thus enabling efficient forward optimization. We establish local well-posedness of the solution map and convergence of the outer iterations, and further derive a sensitivity
alignment between the original problem and the final subproblem in the backward pass, demonstrating that the subproblem sensitivity, which can be computed efficiently in a
matrix-free manner, provides a principled approximation to the exact optimizer sensitivity. We evaluate lapanda on nonconvex constrained Rosenbrock
benchmarks, imitation learning with several representative constrained optimal control problems, and embedded robotic obstacle-avoidance tasks. Compared with state-of-the-art differentiable solvers, lapanda delivers substantial reductions in computation time and
memory footprint while maintaining reliable constraint satisfaction and learning performance.
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All authors Yuankun Chen, Zifei Nie, Kangyu Lin, Ján Drgoňa, Liang Wu
Systems and Control
A Minimum-Order Functional Observer Beyond the Darouach and Luenberger Constructions
Tyrone Fernando
This paper develops a general minimum-order functional observer framework that contains the classical Darouach functional observer and the reduced-order Luenberger observer as special cases. The required functional dimension is determined from the triple $(A,C,L)$ through a set of functional observability indices. The minimum dimension permitted by the algebraic observer-existence condition is characterized, and the complete family of augmentations attaining this dimension is determined. Necessary-and-sufficient spectral feasibility conditions are then established for this dimension to be the minimum achievable observer order. The resulting existence conditions reduce exactly to the Darouach conditions when no augmentation is required and to the classical observability condition in the reduced-order Luenberger case. More generally, the framework admits minimum-order functional observers of intermediate order, which may exist even when neither classical observer is available.
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Signal Processing
Study on coexisting 6G D-MIMO and massive MIMO networks operating in TDD
Maria Clara R. Lobão, Yuri C. B. Silva, Igor M. Guerreiro et al. (+2 more)
In this study, the coexistence between a multiple-input and multiple-output (
MIMO) primary network (PN) and a distributed
MIMO (D-
MIMO) secondary network (SN) is analyzed when both networks operate in time division duplex (TDD) and their uplink (UL) and downlink (DL) phases are not synchronized. The impact of the interference is observed when the networks operate with different 3GPP slot patterns, which vary in terms of UL/DL proportion and slot position, resulting in a significant impact on their overall spectral efficiency (SE). Given that, we propose a slot pattern coordination to reduce the perceived interference on the PN network by controlling the features of the pattern used by the SN. The results show that this strategy helps to avoid loss of performance at the PN.
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All authors Maria Clara R. Lobão, Yuri C. B. Silva, Igor M. Guerreiro, Roberto P. Antonioli, Behrooz Makki
Systems and Control
Control Data Scheduling over Shared Communication Channels: A Sparse and Collision-Free Mechanism
Yuxing Zhong, Zhaohua Yang, Fuhai Nan et al. (+2 more)
In systems where controllers operate remotely and communicate with actuators over shared communication channels, it is crucial to efficiently schedule the control data transmission to reduce bandwidth usage and actuator effort. In this article, we investigate a novel scheduling mechanism that jointly coordinates control actions across different controllers and different time steps. We propose an
algorithm based on the alternating direction method of multipliers (ADMM) to solve the resulting optimization problem. While ADMM is often treated as a black-box solver, the proposed
algorithm offers a clear physical interpretation, ensures convergence to a stationary point, and is computationally efficient. Simulation results validate our theoretical results and demonstrate the effectiveness of our proposed
algorithm.
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All authors Yuxing Zhong, Zhaohua Yang, Fuhai Nan, Daniel E. Quevedo, Ling Shi
Signal Processing
Zone-Based Interference Management for Underlay Spectrum Sharing in Cell-Free Networks
Mariana M. Ferreira, Juno V. Saraiva, Yuri C. B. Silva et al. (+4 more)
Underlay spectrum sharing enables flexible coexistence between a primary network (PN) and a secondary network (SN), but inter-network interference remains a critical limiting factor. Existing mitigation strategies often rely on instantaneous channel state information (CSI), incurring high signaling overhead. This work proposes a zoning-based interference management framework that leverages large-scale channel statistics to partition the PN into interference zones and classify secondary APs (S-APs) into risk tiers. The zone-level characterization enables selective power-reduction policies with reduced coordination complexity. Simulation results confirm that the proposed framework protects the PN while preserving SN spectral efficiency (SE) in distributed
cell-free deployments.
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All authors Mariana M. Ferreira, Juno V. Saraiva, Yuri C. B. Silva, Francisco R. P. Cavalcanti, Igor M. Guerreiro, Roberto P. Antonioli, Behrooz Makki
Signal Processing
Three Stages of ISAC Signal Design: Principles, Methods and Standardization
Zhiyong Feng, Zhiqing Wei, Haotian Liu et al. (+3 more)
Emerging scenarios, including low-altitude economy and intelligent transportation, require ultra-reliable communi- cation and high-precision sensing, making integrated sensing and communication (
ISAC) a key enabler for sixth-generation (6G) networks. The
ISAC signal serves as the carrier for both communication and sensing, and its design directly affects the overall performance of
ISAC system. This article presents a progressive roadmap of
ISAC signal design. First, we establish a multi-dimensional performance metric framework of
ISAC signal covering communication, sensing, and
radio-frequency (RF) performance. Second, we introduce a three-stage
ISAC signal design paradigm, namely UNLEASH POTENTIAL, EXPAND DIMENSIONS, and DEEPEN COLLABORATION. This paradigm progresses from the exploitation of existing signals, through the design of new waveform bases and multi-dimensional signal structures, to the
ISAC signal design with multi-node cooperation. Finally, we propose a corresponding standardization roadmap that evolves from compatibility with existing communication sys- tems toward native and cooperation-enabled
ISAC. The resulting framework provides practical guidance for
ISAC signal design and the standard
evolution toward 6G.
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All authors Zhiyong Feng, Zhiqing Wei, Haotian Liu, Yichen Wu, Lin Wang, Yuhan Long
Audio and Speech Processing
An Efficient Parametric Codec for Low-Bitrate First-Order Ambisonics
Wei-Ting Lai, Amy Bastine, Lachlan Birnie et al. (+2 more)
Driven by the rapid growth of immersive teleconferencing and generative spatial audio, efficient low-bitrate coding of first-order Ambisonics (FOA) has become increasingly important. In this work, we develop a lightweight parametric FOA codec that retains the standard Directional Audio Coding analysis and synthesis while redesigning the spatial metadata quantization scheme. Rather than quantizing direction-of-arrival (DOA) and diffuseness independently, we combine them into a 3-D directivity vector and jointly quantize these vectors across frequency bands via residual vector quantization (RVQ). The RVQ codebooks are optimized within minutes via stage-wise k-means without backpropagation, yielding a constant-bitrate (CBR) representation that decouples metadata rate from the number of frequency bands. Evaluations show that our approach outperforms low-bitrate perceptual codecs in FOA reconstruction, remains competitive with neural codecs on the downstream Sound Event Localization and Detection task, and maintains robust performance when paired with different external monaural codecs. Given its lightweight training, strong performance, and CBR design, we consider the proposed method to be a favorable and reproducible baseline for future FOA codec research.
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All authors Wei-Ting Lai, Amy Bastine, Lachlan Birnie, Thushara D. Abhayapala, Prasanga N. Samarasinghe
Systems and Control
Grid-Forming E-STATCOMs for Stable Integration of Large-Scale Data Centers: Modeling and Control
Prabhat Ranjan Bana, Novan Zakkia, Jean-Philippe Hasler et al. (+1 more)
The rapid expansion of large-scale AI data centers (AIDC) is introducing new stability challenges, particularly in weak or low-inertia networks characterized by fast, step-like demand variations and strict requirements on voltage and dynamic performance. This paper investigates the use of grid-forming (GFM) Enhanced STATCOMs (E-STATCOMs) to support reliable integration of such facilities. A power-admittance-based linear modelling framework is developed to capture system interactions and is validated through detailed EMT simulations. The results demonstrate that E-STATCOMs provide fast, well-damped responses to abrupt load changes while effectively mitigating low-frequency oscillations and interactions with network resonances. By enabling tunable dynamic behavior via a load balancer, virtual impedance, and coordinated active-reactive power support, the proposed approach allows precise shaping of system response and improved regulation at the point of connection. These features make E-STATCOMs a flexible and scalable solution for integrating large data centers into weak grids and long transmission systems, supported by a design-oriented framework that facilitates parameter selection and performance assessment without extensive reliance on EMT studies to meet grid codes and AIDC interconnection requirements.
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All authors Prabhat Ranjan Bana, Novan Zakkia, Jean-Philippe Hasler, Christer Danielsson
Signal Processing · Cross-listed from cs.LG
TerMeZO: Ternary Sparse Zeroth-Order Optimization for Fine-tuning BitNet Models at the Edge
Houssem Sifaou, Prabodh Katti, Bipin Rajendran et al. (+1 more)
Fine-tuning anguage models (LLMs) with first-order optimizers requires a
memory several times larger than that required for inference.
Memory-efficient zeroth-order optimization (MeZO) sidesteps this cost by estimating gradients from forward passes only. However, for BitNet architectures, a family of LLMs with ternary {-1,0,1\} weights and 8-bit activations, fine-tuning requires updating full-precision latent weights, and thus the
memory footprint of MeZO no longer matches that of inference. A promising solution is to finetune only a subset of the latent weights, but existing sparse zeroth-order (ZO) methods either ignore the ternary structure or require first-order gradient information to build a sparse mask, which is at odds with the purpose of ZO fine-tuning. We propose TerMeZO, a sparse MeZO scheme that exploits the geometry of the ternary quantizer itself to identify the latent weights that are more likely to change values during fine-tuning, at no additional data or
memory cost. Our convergence analysis shows that TerMeZO can converge faster than full-parameter MeZO, owing to its optimized reduction of the fine-tuning effective dimension. We run extensive experiments on BitNet models ranging from 1B to 3B parameters, spanning classification, instruction-following, and mathematical
reasoning tasks. TerMeZO matches or exceeds the performance of full-parameter MeZO while substantially reducing the fine-tuning
memory footprint.
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All authors Houssem Sifaou, Prabodh Katti, Bipin Rajendran, Osvaldo Simeone
Systems and Control · Cross-listed from cs.RO
A Disk-Shaped Magnetoelastic Torque Sensor for Robotic Joints Using Permanent Magnetization
Bruno Brajon, Enrico Gasparin, Nicole Yazigy et al. (+5 more)
Direct torque sensing is a growing need in the robotics community to enable precise control and interactions where torque estimation from motor current is not sufficient. This paper presents a novel disk-shaped magnetoelastic torque sensor with a compact axial envelope of about 1 cm, suitable for integration in robotic joints. A four-magnetometer architecture is used to measure the field modulated by the stress affecting a narrow magnetized region while rejecting the effects of parasitic cross forces. A custom-designed
magnetic shield enhances the torque sensitivity while reducing the external stray fields by a factor 7x. The device measures the torque with an accuracy of 1.34 %FS relative to the 50 Nm full scale (FS). The paper details the development of the sensor through the mechanical design, the magnetization procedure, and the experimental validation. The results demonstrate the potential of the proposed sensor for robotic applications.
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All authors Bruno Brajon, Enrico Gasparin, Nicole Yazigy, Lisa Salamin, Florian Copt, Guzmán Borque Gallego, Elias Klauser, Gaël Close