Jorge Abraham Martinez Castaneda
40 records found
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Effcient Auditory Coding for Bat Vocalizations
Testing Auditory Kernel Effciency on Rhinolophus Affnis Calls
Abstract—Effcient neural coding is a theoretical model in sensory neuroscience, positing that biological systems maximize information transfer to the brain while minimizing neural resources. While this concept has been extensively studied in the context of human speech perception
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Assessing speech intelligibility for individuals with dysarthria is critical for understanding the severity of motor speech disorders and evaluating speech therapy interventions. Traditional subjective as- sessments, while effective, are resource-intensive and prone to bias, whic
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Respiratory Rate (RR) is a vital health indicator, especially in infant monitoring, where early detection of abnormalities or variabilities in RR is crucial. Traditionally, the respiratory rate is extracted using contact-based methods, which, although reliable, can be quite intru
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Clear communication in public address systems is essential, especially in environments where safety or information clarity is critical. Speech intelligibility is often assessed using objective intelligibility metrics (OIMs), which predict intelligibility through mathematical mode
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This study investigates the performance of two objective speech quality metrics, Perceptual Evaluation of Speech Quality (PESQ) and Virtual Speech Quality Objective Listener (ViSQOL), in predicting human-rated speech quality scores, which are essential for telecommunication syste
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Objective speech intelligibility metrics (OIMs) are widely used in various fields, including public ser- vice announcements. These metrics do not directly predict the intelligibility of a speech (defined as the ratio of understandable words in an audio sample), but produce values
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The measurement of the heart rate (HR) is of vitalimportance in modern medicine. Advancements in medical technology have resulted in a myriad of techniques to measure and analyze these bio-signals, and the advent of telemedicine and the post-COVID-19 world has placed greater emph
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Heart rate (HR) is a critical indicator of an individual’s health, serving as a key metric for detecting potential cardiac issues. This paper explores a method for real-time heart rate measurement using RGB camera footage, aimed at general health monitoring. The proposed method u
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Reverberation is a key aspect when designing the interior of buildings, and must be carefully considered in the context of the function of the room. Defined by the reverberation time (RT), it is known to have a big influence on the intelligibility and quality of audio in closed s
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Estimating reverberation time (RT60) accurately is crucial for enhancing the acoustic quality of various environments as it decides how you feel the sound fades away subjectively. Traditional methods, such as Sabine's equation, require extensive prior knowledge and assume ideal c
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Evaluation of Perceptual Accuracy in Simulated Room Impulse Responses
Designing and Implementing a Subjective Testing Methodology for the Perceptual Evaluation of Simulated Room Impulse Responses
The accurate simulation of Room Impulse Responses (RIRs) is important in a variety of applications in acoustics such as automatic speech recognition, speech enhancement, and architectural acoustic design. While objective metrics for evaluating RIRs have been researched extensivel
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In building design, it is important to consider certain materials for certain acoustical properties. Specifically, the time it takes for an audio signal to decrease in volume by 60 dB is important. This can be estimated with Sabine's and Eyring's formula's, which both make use of
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Automatic Speech Recognition (ASR) systems are becoming increasingly popular in this day and age. Unfortunately, due to inherent biases within these systems, performance disparities exist among specific demographic groups. Bias metrics can be used to measure this bias. Within ASR
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Dutch State-of-the-art Automatic Speech Recognition (ASR) systems do not perform equally well for different speaker groups. Existing metrics to quantify this bias rely on demographic metadata, which is often unavailable. Recent advances in the field use machine learning to find g
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Exploring the Relationship Between Bias and Speech Acoustics in Automatic Speech Recognition Systems
An Experimental Investigation Using Acoustic Embeddings and Bias Metrics on a Dataset of Spoken Dutch
Automatic Speech Recognition (ASR) systems have become an integral part of daily lives. Despite their widespread use, these systems can exhibit biases that express themselves in the differences in their accuracy and performance across different demographic groups. Methods quantif
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How to measure bias in automatic speech recognition systems?
A bias metric without a reference group
This paper presents a novel approach to measuring bias in Automatic Speech Recognition (ASR) systems by proposing a metric that does not use the conventional approach of a reference group. Current methods typically measure bias through comparison with a ’norm’ or minimum error gr
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Pre-Trained Models on Scanned Historic Watermarks
A Comparative Analysis Exploring Pre-Trained Models on Scanned Historic Watermarks
This paper tackles the problem of evaluating the task of finding similar scanned historical watermarks - small images embedded in historical paper that have been digitized to be processed on a computer - using pre-trained neural networks. This research aims to identify an efficie
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Curve Reconstruction and Approximation in Binarised Scanned Historic Watermark Images
A Study of Techniques Aiding Binarisation for an Automated Watermark Similarity-matching Pipeline
A curve is a continuously bending line with no angles that can be found anywhere in the real world, forming shapes and outlines. They are also the building blocks of historic watermarks, imprinted images on paper that may be used to identify its manufacturers. Their shapes consis
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Binarization of Historical Watermarks
A Review of Thresholding Techniques Applied to Historical Watermark Images
A watermark image is a scan of a historical paper document that contains a watermark, which is a motif embedded in the paper that provides valuable information on the origins of a document. Developing tools to automatically identify watermarks can make this information more acces
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Automated Processing of scanned historic watermarks
A Comparison of Feature Extraction Techniques for Binarized Content-Based Image Retrieval
Feature extraction techniques for content-based image retrieval are explored, focusing on black-and-white images in the context of historical watermarks. Orthogonal moments and texture features are found to be most applicable. Seven methods are evaluated: four different orthogona
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