Digital Audio System Research Articles

FPGA-Based Design of a Ready-to-Use and Configurable Soft IP Core for Frame Blocking Time-Sampled Digital Speech Signals †

‘Frame blocking’ or ‘Framing’ is a technique that divides a time-sampled speech or audio signal into consecutive and equi-sized short-time frames, either overlapped or non-overlapped, for analysis. The framing hardware architectures (FHA) in the literature support framing speech or audio samples of specific word size with specific frame size and frame overlap size. However, speech and audio applications often require framing signal samples of varied word sizes with varied frame sizes and frame overlap sizes. Therefore, the existing FHAs must be redesigned appropriately to keep up with the variability in word size, frame size and frame overlap size, as demanded across multiple applications. Redesigning the existing FHAs for each specific application is laborious, prompting the need for a configurable intellectual property (IP) core. The existing FHAs are inappropriate for creating configurable IP cores as they lack adaptability to accommodate variability in frame size and frame overlap size. Therefore, to address these issues, a novel FHA, adaptable to accommodate the desired variability, is proposed. Furthermore, the proposed FHA is transformed into a field-programmable gate array-based soft, ready-to-use and configurable frame blocking IP core using the Xilinx® Vivado™ tool (San Jose, CA, USA). The resulting IP core is versatile, offering configurability for framing in numerous applications incorporating real-time digital speech and audio systems. This research article discusses the proposed FHA and frame blocking IP core in detail.

Estimating the first and second derivatives of discrete audio data

A new method for estimating the first and second derivatives of discrete audio signals intended to achieve higher computational precision in analyzing the performance and characteristics of digital audio systems is presented. The method could find numerous applications in modeling nonlinear audio circuit systems, e.g., for audio synthesis and creating audio effects, music recognition and classification, time-frequency analysis based on nonstationary audio signal decomposition, audio steganalysis and digital audio authentication or audio feature extraction methods. The proposed algorithm employs the ordinary 7 point-stencil central-difference formulas with improvements that minimize the round-off and truncation errors. This is achieved by treating the step size of numerical differentiation as a regularization parameter, which acts as a decision threshold in all calculations. This approach requires shifting discrete audio data by fractions of the initial sample rate, which was obtained by fractional delay FIR filters designed with modified 11-term cosine-sum windows for interpolation and shifting of audio signals. The maximum relative error in estimating first and second derivatives of discrete audio signals are respectively in order of 10-13\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10^{-13}$$\\end{document} and 10-10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10^{-10}$$\\end{document} over the entire audio band, which is close to double-precision floating-point accuracy for the first and better than single-precision floating-point accuracy for the second derivative estimation. Numerical testing showed that this performance of the proposed method is not influenced by the type of signal being differentiated (either stationary or nonstationary), and provides better results than other known differentiation methods, in the audio band up to 21 kHz.

Acoustic dynamics in performance halls: Enhancing violin sound quality in the FPSD orchestra room

With an emphasis on improving violin sound quality, this study explores the acoustic dynamics of the FPSD Orchestra Room at Universitas Pendidikan Indonesia. Direct and reflected sound are the two factors that impact the phenomena of interior sound. This research emphasizes the crucial balance between sound absorption and reflection to optimize the acoustic environment and ensure clarity and richness in violin performances. Improving acoustic conditions is vital to increase audience experience and performance quality. The study used a qualitative descriptive research style combining literature review and observations. The research site was the FPSD Orchestra Room, renowned for its historical significance and contribution to music education. The observations revealed significant acoustic obstacles that adversely affected the violin sound's clarity, including excessive reverberation and unequal sound dispersion. The literature review revealed the theoretical underpinnings of sound propagation, reverberation, absorption, reflection, and diffraction. The acoustic environment can be greatly enhanced by introducing sound-absorbing materials to optimize reverberation duration, controlling reflections with diffusers, and utilizing adaptive acoustic systems. Furthermore, combining virtual acoustics technology with digital sound processing systems allows for dynamic control over acoustic parameters, increasing the performance space's adaptability. These improvements provide a customized acoustic environment that supports a variety of musical performances and guarantees the audience a clear, engaging auditory experience. This study emphasizes the significance of fusing cutting-edge technology innovations with conventional acoustic design concepts to attain optimal sound quality in performance halls. Innovation and research must never stop to improve these acoustic settings even further.

Simultaneous Emotional Stimuli Prolong the Timing of Vibrotactile Events.

Temporal information plays a crucial role in human everyday life. Yet, perceived time is subject to distortions. Emotion, for instance, is a powerful time modulator in that emotional events are perceived longer than neutral events of the same length. However, it is unknown how exposure to emotional stimuli influences the time perception of a simultaneous neutral tactile event. To fill this gap, we tested the effect of emotional auditory sounds on the perception of neutral vibrotactile feedback. We used neutral and emotional (i.e., pleasant-high arousal, pleasant-low arousal, unpleasant-high arousal, and unpleasant-low arousal) auditory stimuli from the International Digitized Sound System (IADS). Tactile information was a vibrotactile stimulus at a fixed intensity and presented through a custom-made vibrotactile sleeve. Participants listened to auditory stimuli which were temporally coupled with vibrotactile stimulation for 2,3,4, or 5 s. Their task was to focus on the duration of vibrotactile information and reproduce elapsed time. We tested the effects of valence and arousal of auditory stimuli on the perceived duration of vibrotactile information. Simultaneously presented auditory stimuli, in general, lengthened the perceived duration of the neutral vibrotactile information compared to neutral auditory stimuli. We conclude that emotional events influence time perception of simultaneous neutral haptic events.

Application-Specific Integrated Circuit of an Inter-IC Sound Digital Filter for Audio Systems

In digital audio systems, filters and equalizers are essential modules for audio improvement at the input and output stages. Due to their computational complexity, most audio tasks are processed with digital signal processors. Due to the fact that latency in audio systems is a critical specification and audio trends require higher sample rates, noise canceling, and bigger data sizes, having an independent high-resolution equalizer would reduce the computational power needed for audio systems. This research had the goal of designing and implementing a hardware architecture for a configurable filter bank based on finite impulse response (FIR) filters and a noise-cancellation stage with an inter-integrated circuit (I2C) communication interface, which allows the filter configuration. The system was implemented as a standalone integrated circuit (IC) for which its inputs were the inter-IC sound (I2S) bus control signals. The digital audio system was optimized to perform one-cycle convolutional operations by implementing a vector–vector arithmetic logic unit. Furthermore, this applied research provides the register transfer level description and the functional verification of the digital design, the system-on-chip (SoC) implementation in TSMC 180 nm technology, and the post-silicon validation with a printed circuit board for testing the output digital signals of the system.

Coverage Optimization of Eureka Digital Sound Broadcasting Single Frequency Network Using Simulated Annealing and Particle Swarm Optimization

Due to scarcity of bandwidth available for sound broadcasting, Digital sound broadcasting Technology is emerging to be the Technology of resort in sound broadcasting industry towards replacing the analogue sound broadcasting currently dominated by FM Radio. There are many digital sound broadcasting systems being proposed with different performance and bandwidth efficiency. Static delays are artificial delays intentionally introduced at each Transmitter in order to minimize interference in a Single Frequency Network (SFN). In this paper, we have looked at the Terrestrial digital audio broadcasting (T-DAB) system specifically to optimize its final SFN coverage by finding an optimal set of static delays for transmitters. For the sake of simulation, hexagonal model of transmitters operating under Single Frequency Network (SFN) was used. The aim of this study is to maximize SFN coverage by using optimal set of artificial static delays, Particle Swarm Optimization (PSO) have strong ability in finding the global optimistic result while Stimulated Annealing (SA) algorithm has a strong ability to find the local Optimistic result and therefore based on their unique strength, these methods were selected so that our study can have a good comparison in terms of coverage by using both global and local optimistic results. We report the increase of coverage by 1.12% and 2.38% using Simulated Annealing and Particle Swarm Optimization technique respectively.

Semiotic Analysis of the Changes of Ondel-ondel Costume Elements as a Betawi Cultural Negotiation

Recognized as one of Jakarta’s icons, the large pair of puppets known as ondel-ondel has undergone significant transformation since its origin centuries ago. Classically built in a fairly simple form, ondel-ondel presents itself in various ways at every performance. Now, ondel-ondel is increasingly found throughout Jakarta, having gone through numerous changes in its costume elements. Whereas it is usually meant to be shown as a couple, nowadays a single ondel-ondel can be found cruising alleys with a mere digital sound system as musical accompaniment. Once respected and feared for its looks, today it is belittled by its own people. Due to this phenomenon, this research aimed to answer the questions of who influenced this ideological change, which ruling elite encouraged their ideology, and what the ideology is. Interviews and direct observations produced stories and photographs, while literature and media studies provided a historical background. Roland Barthes’ semiotic connotations with a historical perspective were used to read the hidden concepts behind these signs, which connect the changes of the costume elements through the important events in Betawi society during the period of 1970–2020. The different interests of each party brought more conflict in their relations, which created a dynamic cultural negotiation. Based on the results, the terms for the ondel-ondel models were identified, specifically a personification model, Islamic model, and commercial model, each named after the historical events and ideology brought about by Jakarta’s ruling elites.

Does Affective Content of Sounds Affect Auditory Time-to-collision Estimation?

ABSTRACT In prior studies, mean estimates of time-to-collision (TTC) of approaching objects were shorter for threatening pictures (e.g., snakes) than non-threatening pictures (e.g., rabbits) but judgments of analogous auditory stimuli were not examined. The aim of the present study was to determine whether the affective content of an approaching object presented in the auditory domain can affect TTC estimates. Using simulations of sound-emitting objects selected from the International Affective Digitized Sound system, we compared TTC judgments of approaching objects that were threatening (hissing rattlesnake, buzzing bee, dentist drill) and control objects that had the same loudness and spectral properties but were unidentifiable. The snake sound, but not the drill or bee sounds, was judged as arriving earlier than the corresponding control sound, providing partial support that previously reported threat-related effects on TTC estimates for visual objects can also occur in the auditory domain.

Analysis of the Single Frequency Network Gain in Digital Audio Broadcasting Networks

The single frequency network (SFN) is a popular solution in modern digital audio and television system networks for extending effective coverage, compared to its traditional single-transmitter counterpart. As benefits of this configuration appear to be obvious, this paper focuses on the exact analysis of so-called SFN gain—a quantitative effect of advantage in terms of the received signal strength. The investigations cover a statistical analysis of SFN gain values, obtained by means of computer simulations, with respect to the factors influencing the coverage, i.e., the protection level, the reception mode (fixed, portable, mobile), and the receiver location (outdoor, indoor). The analyses conclude with an observation that the most noteworthy contribution of the SFN gain is observed on the far edges of the networks, and the least one close to the transmitters. It is also observed that the highest values of the SFN gain can be expected in the fixed mode, while the protection level has the lowest impact.

Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity.

In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.

Changes in dental hygiene students' working posture following digital sound feedback.

This study aimed to observe changes in working posture by measuring the REBA (Rapid Entire Body Assessment) score of dental hygiene students according to digital sound feedback linked with a smartphone application. This study was conducted on 28 fourth-year dental hygiene students who received theoretical and practical training on dental posture in the second year and then practised on mannequins and patients for about four semesters. Periodontal instrumentation was performed freely by applying digital sound notification feedback for four weeks after baseline, 30minutes per week. REBA was measured after performing periodic structure construction without providing digital sound notification feedback for the last 1-2minutes. Follow-up was conducted the same way 2-3weeks after the intervention period. The REBA score for total, neck and trunk of all subjects showed statistically significant decreases post-intervention compared with the baseline scores (total p<.001, neck p<.001 and trunk p=.042). A digital sound feedback system was shown to be effective in encouraging correct working posture in dental hygiene students by helping them improve their REBA scores.

FNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses

This study aims to investigate the generalizability of the semi-metric analysis of the functional connectivity (FC) for functional near-infrared spectroscopy (fNIRS) by applying it to detect the dichotomy in differential FC under affective and neutral emotional states in nursing students and registered nurses during decision making. The proposed method employs wavelet transform coherence to construct FC networks and explores semi-metric analysis to extract network redundancy features, which has not been considered in conventional fNIRS-based FC analyses. The trials of the proposed method were performed on 19 nursing students and 19 registered nurses via a decision-making task under different emotional states induced by affective and neutral emotional stimuli. The cognitive activities were recorded using fNIRS, and the emotional stimuli were adopted from the International Affective Digitized Sound System (IADS). The induction of emotional effects was validated by heart rate variability (HRV) analysis. The experimental results by the proposed method showed significant difference (FDR-adjusted p = 0.004) in the nursing students’ cognitive FC network under the two different emotional conditions, and the semi-metric percentage (SMP) of the right prefrontal cortex (PFC) was found to be significantly higher than the left PFC (FDR-adjusted p = 0.036). The benchmark method (a typical weighted graph theory analysis) gave no significant results. In essence, the results support that the semi-metric analysis can be generalized and extended to fNIRS-based functional connectivity estimation.

A Low Power Sigma-Delta Modulator with Hybrid Architecture.

Analogue-to-digital converters (ADC) using oversampling technology and the Σ-∆ modulation mechanism are widely applied in digital audio systems. This paper presents an audio modulator with high accuracy and low power consumption by using a discrete second-order feedforward structure. A 5-bit successive approximation register (SAR) quantizer is integrated into the chip, which reduces the number of comparators and the power consumption of the quantizer compared with flash ADC-type quantizers. An analogue passive adder is used to sum the input signals and it is embedded in a SAR ADC composed of a capacitor array and a dynamic comparator which has no static power consumption. To validate the design concept, the designed modulator is developed in a 180 nm CMOS process. The peak signal to noise distortion ratio (SNDR) is calculated as 106 dB and the total power consumption of the chip is recorded as 3.654 mW at the chip supply voltage of 1.8 V. The input sine wave of 0 to 25 kHz is sampled at a sampling frequency of 3.2 Ms/s. Moreover, the results achieve a 16-bit effective number of bits (ENOB) when the amplitude of the input signal is varied between 0.15 and 1.65 V. By comparing with other modulators which were realized by a 180 nm CMOS process, the proposed architecture outperforms with lower power consumption.

Jeff Morris: Hearing Voices

Jeff Morris: Hearing Voices

Towards an ontology of digital arts. Media environments, interactive processes and effects of presence

During the Nineties, the diffusion of information and communication technologies allowed a dramatic transformation in art practices. Radically new aesthetic experiences, such as tele-presence, immersivity, responsivity, hyper-mediacy and multimediality, emerge in the framework of the digital arts and call into question not only the traditional status of the work of art but also the fundamental relation with the beholder. The aim of this paper is to define a conceptual framework for the ontology of digital arts by identifying some ontological features that are distinctive to digital idioms. Such an analysis tries to outline how aesthetic and technical innovations affect our cognitive and sensorial relationship with technological artifacts. In the first part, the relation between technogenesis and ontology, as well as the key topics of the ontology of digital arts are discussed. The second part deals with the notion of presence. Despite traditional understandings of digital arts, mainly focused on immateriality, simulation and mediation, my analysis demonstrate how the notion of presence can provide an original perspective in order to understand the ontological status of the mediatized artistic practices. In the last three decades the generalisation of information and telecommunication technologies has played a major role in the transformation of the arts, opening the field to important experiments in the domain of computer graphics, digital audio, robotics and motion capture systems… (Dixon 2007). Peculiar forms of aesthetic experience such as tele-presence, immersivity, responsivity, hyper-mediation and multimediality, progressively arise from digital arts and question not only the status of artwork but also, more generally, the foundational relationship between this latter and the recipient.

Komunikasi Suara Sinyal Digital Dengan Transmisi Frekuensi 2,4 Ghz

The limitations of developing high-tech communication systems cause the area of people who can use this service to be uneven. One technology that can be used for areas not yet reached by this high-tech communication service is to use radio communication technology. Today radio communication technology continues to develop and produce an increasingly limited frequency spectrum. One way to improve the efficient use of available frequency spectrums is the transition from analog technology to digital technology. A very significant benefit in the use of digital technology is to save the use of radio frequency spectrum bandwidth with signal compression techniques. One device that still uses analog technology is a handy talky (HT). In addition to the lack of frequency spectrum efficiency, HT technology still requires a radio station license (ISR) for its use. This experiment designed a two-way digital audio communication system that was transmitted using the NRF24L01 radio frequency transceiver module on the 2.4Ghz frequency. Analog input data from the microphone will be converted into digital data through the ADC (Analog to Digital Converter) process using a microcontroller and the digital data that will be sent is first digitally modulated using GFSK modulation then transmitted digitally. On the other hand data that already contains voice information received again by another NRF24L01 transceiver is received by the Arduino microcontroller and then formed into a PWM signal (pulse width modulation) and then released to the speaker. So the method of using digital transmission can produce efficiencies in the frequency spectrum.&#x0D;

Characterization of a Gm-C Filter Based on CNTFETs for Use in Digital Audio Broadcasting Systems

Anti-aliasing filters play an important role in analog front-end circuits, and their suitable design can improve the performance of digital audio broadcasting systems. In this paper, a Gm-C anti-aliasing filter based on carbon nanotube field effect transistors (CNTFETs) is proposed. Using CNTFETs provides low power consumption and appropriate frequency response. Simulation results show that these characteristics are widely dependent on the physical parameters of CNTFETs. The effects of chirality, the number of CNTs in each CNTFET, and distance of CNTs from each other on power consumption and frequency response are investigated. Among these parameters, the chirality and number of CNTs respectively have nonlinear and linear relationships with both power consumption and cutoff frequency. On the other hand, changing the distance between CNTs has the least effect on the output characteristics. Also, the output features of the filter for different gate oxides and channel lengths are studied. The overall result of the simulations demonstrates that the filters based on CNTFETs are promising candidates to be used in future nano-scale integrated circuits.

Reliability of Lagged Poincaré Plot Parameters in Ultrashort Heart Rate Variability Series: Application on Affective Sounds.

The number of studies about ultrashort cardiovascular time series is increasing because of the demand for mobile applications in telemedicine and e-health monitoring. However, the current literature still needs a proper validation of heartbeat nonlinear dynamics assessment from ultrashort time series. This paper reports on the reliability of the Lagged Poincaré Plot (LPP) parameters-calculated from ultrashort cardiovascular time series. Reliability is studied on simulated as well as on real RR series. Simulated RR series are generated and LPP parameters estimated for ultrashort time series (from 15 to 60 s) are compared to those estimated from 1 h. All LPP parameters estimated from time series longer than 35s presented a Spearman's correlation coefficient higher than 0.99. RR series acquired from 32 healthy subjects during 5-min resting state sessions are used to test the LPP approach in experimental data. The usefulness of ultrashort term parameters in real data is accomplished also studying their ability to discriminate positive and negative valence of auditory stimuli taken from the International Affective Digitized Sound System (IADS) dataset. The achieved accuracies in the recognition of elicitation along the valence dimension, using only the LPP parameters, were of 77.78% for 1 min 28 s series, and of 79.17% for 35 s series.

Affective content analysis of music emotion through EEG

Emotion recognition of music objects is a promising and important research issues in the field of music information retrieval. Usually, music emotion recognition could be considered as a training/classification problem. However, even given a benchmark (a training data with ground truth) and using effective classification algorithms, music emotion recognition remains a challenging problem. Most previous relevant work focuses only on acoustic music content without considering individual difference (i.e., personalization issues). In addition, assessment of emotions is usually self-reported (e.g., emotion tags) which might introduce inaccuracy and inconsistency. Electroencephalography (EEG) is a non-invasive brain-machine interface which allows external machines to sense neurophysiological signals from the brain without surgery. Such unintrusive EEG signals, captured from the central nervous system, have been utilized for exploring emotions. This paper proposes an evidence-based and personalized model for music emotion recognition. In the training phase for model construction and personalized adaption, based on the IADS (the International Affective Digitized Sound system, a set of acoustic emotional stimuli for experimental investigations of emotion and attention), we construct two predictive and generic models \(AN\!N_1\) (“EEG recordings of standardized group vs. emotions”) and \(AN\!N_2\) (“music audio content vs. emotion”). Both models are trained by an artificial neural network. We then collect a subject’s EEG recordings when listening the selected IADS samples, and apply the \(AN\!N_1\) to determine the subject’s emotion vector. With the generic model and the corresponding individual differences, we construct the personalized model H by the projective transformation. In the testing phase, given a music object, the processing steps are: (1) to extract features from the music audio content, (2) to apply \(AN\!N_2\) to calculate the vector in the arousal-valence emotion space, and (3) to apply the transformation matrix H to determine the personalized emotion vector. Moreover, with respect to a moderate music object, we apply a sliding window on the music object to obtain a sequence of personalized emotion vectors, in which those predicted vectors will be fitted and organized as an emotion trail for revealing dynamics in the affective content of music object. Experimental results suggest the proposed approach is effective.

A more precise, repeatable and diagnostic alternative to surface electromyography – an appraisal of the clinical utility of acoustic myography

Acoustic myography (AMG) enables a detailed and accurate measurement of those muscles involved in a particular movement and is independent of electrical signals between the nerve and muscle, measuring solely muscle contractions, unlike surface electromyography (sEMG). With modern amplifiers and digital sound recording systems, measurements during physical activity both inside and outside a laboratory setting are now possible and accurate. Muscle sound gives a representation of the work of each muscle group during a complex movement, and under certain forms of movement even reveals both concentric and eccentric activity, something that sEMG is incapable of. Recent findings suggest that AMG has a number of advantages over sEMG, being simple to use, accurate and repeatable as well as being intuitive to interpret. The AMG signal comprises three physiological parameters, namely efficiency/coordination (E-score), spatial summation (S-score) and temporal summation (T-score). It is concluded that modern AMG units have the potential to accurately assess patients with neuromuscular and musculoskeletal complaints in hospital clinics, home monitoring situations as well as sports settings.