Voice Signal Analysis
Voice Signal Analysis - Related Videos
04:04
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
0 Views •
2025
This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and specificity.
11:17
Acquisition and Analysis of an ECG (electrocardiography) Signal
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2023
Source: Peiman Shahbeigi-Roodposhti and Sina Shahbazmohamadi, Biomedical Engineering Department, University of Connecticut, Storrs, Connecticut An electrocardiograph is a graph recorded by electric potential changes occurring between electrodes placed on a patient's torso to demonstrate cardiac activity. An ECG signal tracks heart rhythm and many cardiac diseases, such as poor blood flow to the heart and structural abnormalities. The action potential created by contractions of the heart wall...
01:23
Small-Signal Analysis of MOSFET Amplifiers
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2025
In small-signal analysis, a MOSFET transistor amplifier acts as a linear amplifier when operating in its saturation region. The gate-to-source voltage (VGS) of the MOSFET is the sum of the DC biasing voltage and the small time-varying input signal. This combination sets up the operating point and modulates the drain current (ID) that flows from the drain to the source. When a small AC signal is superimposed on the DC bias voltage at the gate, the instantaneous drain current comprises three...
01:21
Small-Signal Analysis of BJT Amplifiers
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2025
Small signal analysis is a fundamental approach used in electronics to understand how a Bipolar Junction Transistor (BJT) amplifier processes signals. In the active region, the BJT is designed for linear amplification. The transistor's behavior under these conditions is governed by its instantaneous base-emitter voltage VBE, a sum of the DC bias VBE, and a small AC signal VBE, resulting in the collector current iC. Here, the collector current has a DC component and an AC component. Here, VT is...
06:49
Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
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Cited by 28 •
2014
Here a novel region of interest analysis protocol based on sorting best-fit ellipses assigned to regions of positive signal within two-dimensional time lapse image sequences is demonstrated. This algorithm may enable investigators to comprehensively analyze physiological Ca2+ signals with minimal user input and bias.
FAQs
The main measurements in Voice signal analysis serve different analytical purposes. Amplitude describes the strength of the recorded signal, fundamental frequency captures a key frequency-related property, timing represents when vocal events occur, and spectral content shows how the signal is characterized across frequencies. Together, these features connect observable acoustics with vocal production and perceived sound.
Filtering, Fourier analysis, and spectrograms provide complementary views of a voice signal. Filtering is used within the signal-processing workflow, Fourier analysis examines spectral content, and spectrograms display how spectral patterns change over time. Using these approaches together helps investigators evaluate both frequency-related structure and temporal variation instead of relying on a single summary measurement.
Acoustic measurements add objective values and identifiable patterns to clinical examination. Voice signal analysis can characterize aspects of vocal function and document changes associated with voice disorders, while examination provides the broader clinical assessment. This combined approach supports more measurable evaluation without treating computational results as a replacement for clinical examination.
A typical workflow begins with a recorded voice signal and converts its acoustic waveform into measurable features. Analysts may apply filtering, Fourier analysis, or spectrogram-based visualization to expose changes across time. The resulting measurements can then be interpreted for vocal characterization or considered alongside clinical examination when evaluating vocal function or possible disorders.
Clinical assessment can use measured changes in amplitude, fundamental frequency, timing, and spectral content to describe vocal function more objectively. These features may reveal patterns associated with voice disorders and provide a way to document vocal characteristics. Their main value is complementary: they strengthen the information available during assessment rather than replacing clinical examination.
Within bioengineering, Voice signal analysis extends beyond evaluation of disorders. Measurable vocal features can support speech technologies, assistive communication systems, and human-machine interfaces. The approach also contributes to monitoring tools that track changes in vocal health over time, allowing engineered systems to respond to observable differences in amplitude, frequency, timing, or spectral content.