DSP PAPER REVIEW

Digital Signal Processing: Paper Reviw

DSP helps us to thoroughly analyse almost all biological phenomena, physiological functions and psycho-physiological conditions. DSP is designed to process continuous real-world analog signals.

DSP helps us to thoroughly analyse almost all biological phenomena, physiological functions and psycho-physiological conditions. DSP is designed to process continuous real-world analog signals.

• Speech analysis is a field that estimates parameters for producing speech using acoustic analysis.
• The voice fundamental frequency (F0) is a crucial component, with biomechanical features and potential added as cognitive criterion.
• F0 tracking algorithms do not recognize the temporal location of vocal chord vibrations but rely on typical time intervals between vibrational numbers.
• Autocorrelation-based averaging increases tracking accuracy through calculation. F0 extraction is crucial in speech processing and has various applications.

Title of Paper

APPLICATION OF DIGITAL SIGNAL PROCESSING IN BIOLOGICAL SCIENCES

• Speech signal F0 extraction methods are suggested for calculating short-time speech frames.
• These methods involve identifying the delay in time that increases autocorrelation or identifying the frequency whose harmonic frequency peaks match the frame's frequency spectrum.
• Recent F0 estimation techniques offer superior performance compared to established methods.
• Consistently estimated F0 can be beneficial for various applications and investigate the impact of effort, strain, physical and mental load, and stress.

How shall we overcome

F0 Extraction?

Comparisons of acoustical differences between categories of sound, illustrating similarities between tonal and human speech sounds relative to tool sounds; spectrogram of human speech and a bottlenose dolphin

whistle.

DSP analyses audio and speech signal processing,

video and digital image processing, signal processing, radar

(and sonar) signal processing, spectral estimation, statistical

signal processing, signal processing for communications, sensors

and biomedical signal processing, seismic data processing

ADS v2.0

F0: Voice Fundamental Frequency

Speech analysis is a field that estimates parameters for producing speech using acoustic analysis. The voice fundamental frequency (F0) is a crucial component, with biomechanical features and potential added as cognitive criterion. F0 tracking algorithms do not recognize the temporal location of vocal chord vibrations but rely on typical time intervals between vibrational numbers. Autocorrelation-based averaging increases tracking accuracy through calculation. F0 extraction is crucial in speech processing and has various applications.

To process real-world analog signals, convert them to digital using an ADC, advanced technologies like RISC microprocessors, FPGAs, digital signal controllers, and stream/systolic array processors. This technology can be used for arbitrary sampling rate conversion.

Sound Signals

• ADS v2.0 is a speech analysis application that uses digital signal processing principles to improve efficiency in filter phases and basic voice parameters computation.
• The interactive, real-time application caters to medical and psycho-physiological stress assessment needs.
• It supports standard speech visualization and specialized functions for specific applications.
• The new approach transforms one-dimensional sound quantity into 2-dimensional wave shapes and uses pattern recognition procedures for classification.
• The application has been implemented, tested, and proven its value.

Living organisms communicate through chemical language or sound signals, containing essential information for survival. Animal vocalizations range from periodic vibrations to atonal turbulent noise, offering insight into neural control of vocalization features. Acoustic characteristics of animals and humans can reveal important attributes like size, age, sex, reproductive status, and emotional state. Formant-like spectral features are present in vocalizations of non-human animals, including alligators, birds, and mammals, including nonhuman primates.

Application:

• To increase system performances, data base with referent samples is stored in the operative memory.
• DSP module has responsibility to filter input signal.
• After that,normalization and pick elimination procedures are performed.
• Recognizing procedure compares normalized input signal withall samples from referent samples base, and one sample, which ismost similar to input one, represents the final result of the process.
• The parameter data file that is generated could be printedimmediately.
• Those problems are solved with FILTER application.
• Usingthat defaults, the application filters the original ASCII prm.txtinto format that is suitable for statistical analysis (usage of SPSSsoftware, for instance).

Conclusion

• Proposed software application showed applicability for individuals' stress and emotional tension estimation in real working situation.
• The results of the study on air trafficcontrollers indicated that F0 increased significantly after beingexposed to stress in comparison to the resting state, as well asin subjects who worked under regular conditions.
• Such findingsindicate that F0 shows significant sensitivity both to stress andworking intervals of relatively long duration, when accumulationof workload takes place.

Variations of fundamental frequency of pronounced material (test word divided into segments) in

three different functional states – rest, regular working situation, and in the state of stress.