arXiv subjects

Jorge Ortigoso-Narro

Publications and source records attributed to Jorge Ortigoso-Narro.

3 recordsLinked to original sources

arXiv2025

Real-Time Object Tracking with On-Device Deep Learning for Adaptive Beamforming in Dynamic Acoustic Environments

Advances in object tracking and acoustic beamforming are driving new capabilities in surveillance, human-computer interaction, and robotics. This work presents an embedded system that integrates deep learning-based tracking with beamforming to achieve precise sound source localization and directional audio capture in dynamic environments. The approach combines single-camera depth estimation and stereo vision to enable accurate 3D localization of moving objects. A planar concentric circular microphone array constructed with MEMS microphones provides a compact, energy-efficient platform supporting 2D beam steering across azimuth and elevation. Real-time tracking outputs continuously adapt the array's focus, synchronizing the acoustic response with the target's position. By uniting learned spatial awareness with dynamic steering, the system maintains robust performance in the presence of multiple or moving sources. Experimental evaluation demonstrates significant gains in signal-to-interference ratio, making the design well-suited for teleconferencing, smart home devices, and assistive technologies.

cs.SD↗

arXiv2025

Frequency-Invariant Beamforming in Elevation and Azimuth via Autograd and Concentric Circular Microphone Arrays

The use of planar and concentric circular microphone arrays in beamforming has gained attention due to their ability to optimize both azimuth and elevation angles, making them ideal for spatial audio tasks like sound source localization and noise suppression. Unlike linear arrays, which restrict steering to a single axis, 2D arrays offer dual-axis optimization, although elevation control remains challenging. This study explores the integration of autograd, an automatic differentiation tool, with concentric circular arrays to impose beamwidth and frequency invariance constraints. This enables continuous optimization over both angles while maintaining performance across a wide frequency range. We evaluate our method through simulations of beamwidth, white noise gain, and directivity across multiple frequencies. A comparative analysis is presented against standard and advanced beamformers, including delay-and-sum, modified delay-and-sum, a Jacobi-Anger expansion-based method, and a Gaussian window-based gradient descent approach. Our method achieves superior spatial selectivity and narrower mainlobes, particularly in the elevation axis at lower frequencies. These results underscore the effectiveness of our approach in enhancing beamforming performance for acoustic sensing and spatial audio applications requiring precise dual-axis control.

cs.SD↗

arXiv2024

L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection

Chronic Low Back Pain (CLBP) afflicts millions globally, significantly impacting individuals' well-being and imposing economic burdens on healthcare systems. While artificial intelligence (AI) and deep learning offer promising avenues for analyzing pain-related behaviors to improve rehabilitation strategies, current models, including convolutional neural networks (CNNs), recurrent neural networks, and graph-based neural networks, have limitations. These approaches often focus singularly on the temporal dimension or require complex architectures to exploit spatial interrelationships within multivariate time series data. To address these limitations, we introduce \hbox{L-SFAN}, a lightweight CNN architecture incorporating 2D filters designed to meticulously capture the spatial-temporal interplay of data from motion capture and surface electromyography sensors. Our proposed model, enhanced with an oriented global pooling layer and multi-head self-attention mechanism, prioritizes critical features to better understand CLBP and achieves competitive classification accuracy. Experimental results on the EmoPain database demonstrate that our approach not only enhances performance metrics with significantly fewer parameters but also promotes model interpretability, offering valuable insights for clinicians in managing CLBP. This advancement underscores the potential of AI in transforming healthcare practices for chronic conditions like CLBP, providing a sophisticated framework for the nuanced analysis of complex biomedical data.

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