arXiv subjects

Diego A. Cuji

Publications and source records attributed to Diego A. Cuji.

5 recordsLinked to original sources

arXiv2026

P2Flow: Phoneme-aware Progressive Flow Matching for Extreme Speech Super-Resolution

Generative models have recently demonstrated considerable promise in speech super-resolution (SSR). Nevertheless, the majority of existing work has concentrated on standard or versatile SSR configurations, leaving the extreme setting with severely limited spectral inputs largely unexplored. In this regime, current approaches exhibit marked performance degradation, underscoring the need for dedicated solutions. To bridge this gap, we introduce P2Flow, a phoneme-aware progressive flow matching (FM) framework designed for extreme SSR with three main strategies. First, our model leverages phonetic information to reconstruct missing spectral components. Furthermore, it employs a progressive architectural design that hierarchically restores distinct frequency regions. Finally, we incorporate post-training of the vocoder to enhance overall waveform fidelity. Extensive experiments are conducted on the TIMIT and VCTK datasets under both 1 kHz to 16 kHz and 2 kHz to 16 kHz settings, demonstrating that P2Flow yields state-of-the-art results across multiple evaluation metrics.

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arXiv2026

Underwater Acoustic Channel Library

The development of communication systems critically depends on realistic channel models, yet there are no widely accepted standards in the underwater acoustic communications community. The situation is in stark contrast to terrestrial radio communications, where channel models have been standardized and are widely available. To address this gap, we present an open-access library of underwater acoustic channels derived from field experiments conducted across geographically distinct locations and varying propagation conditions, including shallow and deep water, short and long range, and fixed and mobile platforms. Each channel is described by a time-varying impulse response extracted from at-sea recordings using an adaptive algorithm that separately identifies the multipath structure and the Doppler-induced phase and delay drift. Each channel is also accompanied by a site-specific ambient noise model, which captures the statistics of colored Gaussian noise and impulsive noise. Spatial diversity reception across an array of hydrophones is supported for most channels, while time diversity is included for single-hydrophone scenarios. The library is accompanied by a simple replay interface through which a user supplies an arbitrary transmit signal, passes it through a chosen channel, adds noise at a desired signal-to-noise ratio (SNR), and obtains the received signal. The models are validated by comparing the output SNR of a baseline receiver operating on replayed signals with its performance on the original at-sea recordings, demonstrating close agreement across all channels. The library, including all channel impulse responses, noise model parameters, and replay software, is freely available for download as an open-source package.

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arXiv2026

A Survey of Advancing Audio Super-Resolution and Bandwidth Extension from Discriminative to Generative Models

Audio super-resolution (SR), also referred to as bandwidth extension (BWE), aims to reconstruct high-fidelity signals from low-resolution (LR) or band-limited (BL) observations, an inherently ill-posed task due to the ambiguity of missing high-frequency (HF) content. This survey provides a comprehensive overview of the field, with a particular focus on the paradigm shift from discriminative mapping to modern generative modeling. We first review early discriminative deep neural network (DNN) models, which formulate BWE/SR as a deterministic mapping problem and are prone to regression-to-the-mean effects and spectral over-smoothing. We then systematically review generative approaches, including autoregressive (AR) models, variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion and score-based models, flow-based methods, and Schrödinger bridges. Across these approaches, we examine key design aspects, including representation domain, architecture, conditioning mechanisms, and trade-offs among reconstruction fidelity, perceptual quality, robustness, and computational efficiency. We further conduct unified experiments on representative discriminative and generative methods to provide controlled empirical evidence for these trade-offs. Furthermore, we discuss emerging directions involving large language models (LLMs) and multimodal foundation models, and highlight open challenges in perceptual evaluation, practical deployment, and real-world generalization. By providing a structured taxonomy and unified perspective, this survey establishes a comprehensive foundation and offers a practical roadmap for advancing BWE/SR from deterministic point estimation toward distribution-aware generative modeling.

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arXiv2026

Transmit Beamforming for High-Rate Underwater Acoustic Communications

Transmit beamforming for underwater acoustic communications is challenging because channel state information must be obtained through feedback and may be noisy or outdated. In this paper, we investigate an angle-based beamforming strategy for a single-user link that reduces reliance on full channel knowledge by exploiting stable geometric features of the propagation field. In particular, the beam is steered toward a principal propagation path that remains relatively stable over time. Experimental results using the SPACE and MACE data sets demonstrate reliable communication with excellent data-detection mean-squared error and zero bit errors.

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arXiv2026

Universal adaptive beamforming: A Bayesian approach

We present a Bayesian universal beamforming framework for adaptive array processing in dynamic underwater acoustic environments with unknown and time-varying propagation geometry. Motivated by ideas from universal prediction and estimation, the proposed approach discretizes the angular domain into a finite set of steering hypotheses and recursively computes posterior probabilities over competing spatial models using observation-dependent likelihood functions. For Gaussian observation models, the posterior update reduces to an exponential-weights recursion driven by hypothesis-dependent beamformer evidence metrics. The resulting framework performs soft spatial inference and adaptive beamforming by continuously redistributing posterior probability across competing steering hypotheses while forming posterior-weighted combinations of branch outputs. The formulation naturally connects to classical adaptive beamformers including matched filtering and minimum mean-square error (MMSE) beamforming. In addition, the framework is extended toward broadband underwater acoustic communication receivers through frequency-domain beamformer synthesis and adaptive equalization. Posterior probabilities are updated according to branch-specific equalization errors, enabling joint spatial-temporal adaptation under multipath propagation, Doppler-induced distortions, and time-varying channel conditions. Experimental results using MACE data demonstrate reliable communication performance with low overhead, low data detection mean-squared error, and zero observed bit errors.

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