Audio Amplitude Research Articles

Detection of the Compromising Audio Signal by Analyzing Its AM Demodulated Spectrum

The information technology and communication (IT&C) market consists of computing and telecommunication technology systems, which also include a variety of audio devices. Preserving the confidentiality of transmitted information through these devices stands as a critical concern across various domains and businesses. Notably, spurious electromagnetic emanations emitted by audio devices can be captured and processed, potentially leading to eavesdropping incidents. The evaluation of electronic devices for potential security vulnerabilities often involves employing Transient Electromagnetic Pulse Emanation Standard (TEMPEST) technology. This paper introduces a novel approach to TEMPEST testing specifically tailored for audio devices. The outcomes of the proposed approach offer valuable insights into TEMPEST equipment testing, aiming to mitigate the potential risks posed by threats exploitable by eavesdroppers in everyday scenarios. The present work delves into the examination of two ubiquitous global electronic devices: a notebook and a pair of in-ear headphones. The symmetrical framework of this study arises from the intrinsic similarity that, despite belonging to distinct categories, both devices possess the capability to emit electromagnetic emissions that contain compromised audio signals. This assertion is substantiated by the measurement results presented herein. The proposed methodology involves the analysis of the audio amplitude modulation (AM) demodulated signal in the frequency domain. This innovative approach not only mitigates operator fatigue but also significantly reduces the testing time required for these devices and instrument running hours and leads to the development of new applications.

An Empathy Evaluation System Using Spectrogram Image Features of Audio.

Watching videos online has become part of a relaxed lifestyle. The music in videos has a sensitive influence on human emotions, perception, and imaginations, which can make people feel relaxed or sad, and so on. Therefore, it is particularly important for people who make advertising videos to understand the relationship between the physical elements of music and empathy characteristics. The purpose of this paper is to analyze the music features in an advertising video and extract the music features that make people empathize. This paper combines both methods of the power spectrum of MFCC and image RGB analysis to find the audio feature vector. In spectral analysis, the eigenvectors obtained in the analysis process range from blue (low range) to green (medium range) to red (high range). The machine learning random forest classifier is used to classify the data obtained by machine learning, and the trained model is used to monitor the development of an advertisement empathy system in real time. The result is that the optimal model is obtained with the training accuracy result of 99.173% and a test accuracy of 86.171%, which can be deemed as correct by comparing the three models of audio feature value analysis. The contribution of this study can be summarized as follows: (1) the low-frequency and high-amplitude audio in the video is more likely to resonate than the high-frequency and high-amplitude audio; (2) it is found that frequency and audio amplitude are important attributes for describing waveforms by observing the characteristics of the machine learning classifier; (3) a new audio extraction method is proposed to induce human empathy. That is, the feature value extracted by the method of spectrogram image features of audio has the most ability to arouse human empathy.

An Experimental Framework for Wellness Assessment Using the Internet of Things

Everyday wellness assessment is a promising step toward improving the health status of an individual. This article presents an experimental framework to address the growing concern of individual wellness using the advantages of the Internet of Things (IoT). The introduced framework uses an IoT device to read data from five environmental variables and a smartphone application to examine any correlation between environmental parameters and wellness. Ambient temperature, barometric pressure, humidity, luminosity, and audio amplitude data from a mobile IoT device are collected while the user answers three psychological surveys, regarding sleep quality, stress, and distress. The data are forwarded to a server to examine any potential correlation. The feasibility of the framework is examined through experimentation. An experiment with 20 participants is conducted. According to experimental results, parameters such as the luminosity and ambient audio have an effect on individual well being.

Rancang Bangun Purwarupa Penerima Paket APRS Berbasis Raspberry Pi 2 untuk Stasiun Bumi

The design of Raspberry Pi 2-based APRS packets for UGM earth stations has been designed, which is much cheaper than commercial devices on the market. A TV tuner is used as a signal receiver, the receiver is accessed via a laptop wirelessly. A directed antenna with a controller is used to automatically point to the satellite. The test was carried out by receiving the APRS package emitted by the International Space Station (ISS) satellite and the LAPAN-A2 satellite. The results showed that the tool was able to get the ISS satellite APRS package with a total of 6 packages out of 10 packets emitted. The package received has an average amplitude of 1,200 Hz and 2,200 Hz which is much smaller than the overall audio amplitude. This indicates that there is high noise in the signal. While the APRS package from the LAPAN-A2 satellite has not been successfully obtained.

Sensing Behavioral Change over Time

Personality traits describe individual differences in patterns of thinking, feeling, and behaving ("between-person" variability). But individuals also show changes in their own patterns over time ("within-person" variability). Existing approaches to measuring within-person variability typically rely on self-report methods that do not account for fine-grained behavior change patterns (e.g., hour-by-hour). In this paper, we use passive sensing data from mobile phones to examine the extent to which within-person variability in behavioral patterns can predict self-reported personality traits. Data were collected from 646 college students who participated in a self-tracking assignment for 14 days. To measure variability in behavior, we focused on 5 sensed behaviors (ambient audio amplitude, exposure to human voice, physical activity, phone usage, and location data) and computed 4 within-person variability features (simple standard deviation, circadian rhythm, regularity index, and flexible regularity index). We identified a number of significant correlations between the within-person variability features and the self-reported personality traits. Finally, we designed a model to predict the personality traits from the within-person variability features. Our results show that we can predict personality traits with good accuracy. The resulting predictions correlate with self-reported personality traits in the range of r = 0.32, MAE = 0.45 (for Openness in iOS users) to r = 0.69, MAE = 0.55 (for Extraversion in Android users). Our results suggest that within-person variability features from smartphone data has potential for passive personality assessment.

QRDA: Quantum Representation of Digital Audio

Multimedia refers to content that uses a combination of different content forms. It includes two main medias: image and audio. However, by contrast with the rapid development of quantum image processing, quantum audio almost never been studied. In order to change this status, a quantum representation of digital audio (QRDA) is proposed in this paper to present quantum audio. QRDA uses two entangled qubit sequences to store the audio amplitude and time information. The two qubit sequences are both in basis state: |0〉 and |1〉. The QRDA audio preparation from initial state |0〉 is given to store an audio in quantum computers. Then some exemplary quantum audio processing operations are performed to indicate QRDA’s usability.

Analysis of D/A and A/D Conversions in Quantization-based Audio Watermarking

Due to its advantages, quantization-based embedding has been introduced into audio watermarking to improve robustness performance. Existing audio watermarking algorithms often focus on a given attack. However, in some transmission environments, digital audio files may suffer from the different attacks. For example, effects of D/A and A/D conversions (denoted as DA/AD in this paper) on audio watermarking may be modeled as modification of signal energy, phase changes, and noises corruption. These effects present an important challenge to audio watermarking. Extensive experiments show that phase changes of audio signals caused by the DA/AD may be represented as some extent of temporal scaling. Furthermore, we analyze the performance of quantization-based audio watermarking against these attacks in the DA/AD, and present corresponding calculation expressions of BER (Bit Error Rate) in case of Gaussian noise and modification of audio amplitude, and then investigate the influences of temporal scaling caused by the DA/AD on audio watermarking. As a conclusion, audio watermarking quantization-based is very susceptible to the DA/AD.

Speech digitization system

A speech digitization system including novel encoder and decoder circuits that minimizes the number of resolution bits required to produce a given level of speech quality by optimizing the information content of the digital output signal from the encoder. This is accomplished by providing a companded speech digitization system that includes an amplitude function generator which is adapted to produce an amplitude function signal that maintains substantial duty cycles on the digital output signal over the entire audio amplitude range. Included in the novel amplitude function generator is a unique bias network that serves to center the duty cycle swing of the digital output signal from the encoder around 50% where the information content of the signal is statistically maximized.