Audio Applications Research Articles

A Review of the Challenges of Adaptive Filtering Technology in High-fidelity Audio Signal Processing

Abstract. High-fidelity audio signal processing plays an important role in modern audio technology. With the increasing demand for this technology in various audio application scenarios, the optimization of adaptive filters has emerged as a significant challenge in this field. This paper focuses on improving the performance of adaptive filters in high-fidelity audio signal processing which aims to improve the adaptability and efficiency of filters in complex audio environments by improving algorithms. In this study, the adaptive filtering algorithm based on wavelet transform and particle swarm optimization is used to verify the audio data processing by simulation and real data processing. The research data was collected using the standard audio signal database and the actual collected high-fidelity audio samples. The results show that the improved adaptive filter can significantly improve the performance of complex audio environments, improve the clarity and fidelity of audio signals and reduce the computational complexity. It is also suitable for real-time processing scenarios with limited resources. The conclusion shows that this method provides an efficient solution for high-fidelity audio signal processing and has a wide application prospect.

Usability Testing of the Metadata Management of Audio Archive

The aim of this research is to test the usability of the audio archive metadata management application. This application is used to record all song collection data in the form of cassettes and vinyl and is also used to search for collection data easily and quickly. This research used System Usage Scale (SUS) method to test the application usability. The SUS Testing was carried out by distributing a questionnaire which consists of 10 statements to 30 respondents to determine the assessment scale based on percentile rank, grade, adjective rating, acceptability, and net promoter score. The result of the system usage scale testing showed an average total score of 83. By detail, this result explained that the percentile range assessment scale is between 90-95%, grade is A, adjective rating is in the Excellent category, acceptability is in the acceptable category, and net promoter is in the promoter category. Thus, it can be concluded that the audio archive metadata management application is suitable for use as a search and retrieval tool for song collections in the form of cassettes and vinyl. However, in order to increase the application’s usefulness, the application can be more developed in the future by adding additional features to meet the criteria as a better metadata management tool.

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. 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.

Analysis of representation and generalization capabilities of pre-trained audio models in urban environments

In the last decade, urban noise pollution has become a significant environmental concern that can be mitigated with the help of audio detection algorithms for classifying different sources of noise and creating more informative noise maps. In this context, machine learning, particularly transfer learning, is an essential technology that enables accurate analysis of urban noise sources. However, the choice of the pre-trained model used to compute audio embeddings can significantly influence the performance of downstream classification tasks. This paper aims to compare the embeddings of various pre-trained models on different data collection campaigns in the context of the Sons al balcó project and quantify the robustness of audio representations. To achieve this, we develop metrics and statistically test the presence of distribution shifts in learned latent features. To evaluate the quality of the embeddings, we perform both qualitative and quantitative analysis using dimensionality reduction methods and assess the performance on downstream tasks using data from different collection campaigns. Results highlight major differences between general purpose and specific models. Our findings suggest the need for careful consideration during the choice of the pre-trained model to use in audio event detection applications.

Extended King integral for modeling of parametric array loudspeakers with axisymmetric profiles.

Parametric array loudspeakers have been widely used in audio applications for generating directional audio beams. However, accurately calculating audio sound with a low computational load remains challenging, even for basic axisymmetric source profiles. This work addresses this challenge by extending the King integral in linear acoustics to incorporate both cumulative and local nonlinear effects, under the framework of the quasilinear solution without the paraxial approximation. The proposed method exploits the azimuthal symmetry in cylindrical coordinates to simplify modeling. To further improve computational efficacy, fast Hankel and Fourier transforms are employed for the radial and beam radiation directions, respectively. Numerical results with both uniform and focusing profiles demonstrate the advantages of the proposed approach over the traditional spherical wave expansion and direct integration methods, especially for larger aperture sizes. Specifically, for typical configurations with source aperture size of 0.2 m, we observe at least a 24-fold improvement in computational speed and a 227-fold reduction in memory requirements. These advancements allow us, for the first time, to present the sound field radiated by parametric array loudspeakers with a large aperture size of up to 0.5 m, without paraxial approximations. The implementation codes are available on https://github.com/ShaoZhe-LI/PAL_King.

Chaos-based audio encryption: Efficacy of 2D and 3D hyperchaotic systems

Secure communication in the digital age is necessary; securing audio data becomes very critical since this is normally transmitted across susceptible networks. Traditional approaches to encryption, like Advanced Encryption Standard and Rivest–Shamir–Adleman, are pretty solid but mostly too computationally intensive for real-time audio applications. This paper presents a new audio encryption scheme using chaotic systems, characterized by high sensitivity to initial conditions, pseudo-randomness, and determinism. These properties make chaotic systems ideal for generating keys for cryptographic purposes. Indeed, complex keys that would be nearly impossible to reverse-engineer without exact initial parameters can be obtained with them. This methodology first digitizes the audio signal and subsequently encrypts each sample according to chaotic sequences from multiple systems, like 2D Memristive Hyperchaotic Maps and 3D Hyperchaotic Quadratic Maps. The encryption processes enormously increase the randomness of the encrypted audio and blind the original data, demonstrating the strength of the chaotic systems in securing audio information.Simulation of this encryption approach confirms its effectuality in providing strong security for audio data while maintaining efficiency, thus being suitable for real-time applications. The developed encryption schemes show high resistance to differential attacks, while the quality of the encrypted audio remains acceptable after transformation. This result can demonstrate chaotic systems’ potential to provide a secure and efficient solution for audio communications, which has broad applications in telecommunications, military communications, and confidential conferencing systems. This work, described in the paper, thus satisfies the growing demand for robust security in digital communications and opens up avenues of future research toward chaotic parameter optimization and an extension to other forms of data, enhancing the security features in all types of digital communication channels.

Significance of chirp MFCC as a feature in speech and audio applications

A novel feature, based on the chirp z-transform, that offers an improved representation of the underlying true spectrum is proposed. This feature, the chirp MFCC, is derived by computing the Mel frequency cepstral coefficients from the chirp magnitude spectrum, instead of the Fourier transform magnitude spectrum. The theoretical foundations for the proposal, and the experimental validation using product of likelihood Gaussians, to show the improved class separation offered by the proposed chirp MFCC, when compared with basic MFCC are discussed. Further, real world evaluation of the feature is performed using three diverse tasks, namely, speech–music classification, speaker identification, and speech commands recognition. It is shown in all three tasks that the proposed chirp MFCC offers considerable improvements.

Kombucha–Chlorella–Proteinoid Biosynthetic Classifiers of Audio Signals

ABSTRACTThis paper describes the development of a bioinspired composite material capable of audio classification applications. Hydrogel matrices produced by microorganisms combined with synthetic biology elements, allow for the development of adaptable bioelectronics that connect biology and technology in a customized way. In this study, a composite population of kombucha, chlorella, and proteinoids (thermal proteins) is utilized to respond to acoustic signals converted to electrical waveforms. The kombucha zoogleal mats, which are made and populated by over 60 species of yeasts and bacteria, offer a matrix at the micro level that is connected to the photosynthetic microalgae chlorella. Proteinoids formed through thermal condensation exhibit unique patterns of signaling kinetics. This living material has the ability to be electrically stimulated and can process signals in a way feasible for sensory applications. Using English alphabet audio inputs, a systematic analysis demonstrates the capability to differentiate audio waveforms based solely on biological composite responses. The use of spectral analysis allows for the identification of specific spike timing patterns that encode unique characteristics of individual letters. Moreover, network disturbances result in specific changes in output, so validating the ability to adjust waveform classification. The study demonstrates that kombucha–chlorella–proteinoid composites provide a durable and versatile bioelectronic platform for immediate auditory processing. The work represents progress toward the development of bioelectronic systems that can be customized based on the principles of biological sensory processing, cognition, and adaptation.

Learning an interpretable end-to-end network for real-time acoustic beamforming

Recently, many forms of audio industrial applications, such as sound monitoring and source localization, have begun exploiting smart multi-modal devices equipped with a microphone array. Regrettably, model-based methods are often difficult to employ for such devices due to their high computational complexity, as well as the difficulty of appropriately selecting the user-determined parameters. As an alternative, one may use deep network-based methods, but these are often difficult to generalize, nor can they generate the desired beamforming map directly. In this paper, a computationally efficient acoustic beamforming algorithm is proposed, which may be unrolled to form a model-based deep learning network for real-time imaging, here termed the DAMAS-FISTA-Net. By exploiting the natural structure of an acoustic beamformer, the proposed network inherits the physical knowledge of the acoustic system, and thus learns the underlying physical properties of the propagation. As a result, all the network parameters may be learned end-to-end, guided by a model-based prior using back-propagation. Notably, the proposed network enables an excellent interpretability and the ability of being able to process the raw data directly. Extensive numerical experiments using both simulated and real-world data illustrate the preferable performance of the DAMAS-FISTA-Net as compared to alternative approaches.

Toward deep drum source separation

In the past, the field of drum source separation faced significant challenges due to limited data availability, hindering the adoption of cutting-edge deep learning methods that have found success in other related audio applications. In this letter, we introduce StemGMD, a large-scale audio dataset of isolated single-instrument drum stems. Each audio clip is synthesized from MIDI recordings of expressive drum performances using ten real-sounding acoustic drum kits. Totaling 1224 h, StemGMD is the largest audio dataset of drums to date and the first to comprise isolated audio clips for every instrument in a canonical nine-piece drum kit. We leverage StemGMD to develop LarsNet, a novel deep drum source separation model. Through a bank of dedicated U-Nets, LarsNet can separate five stems from a stereo drum mixture faster than real-time and is shown to considerably outperform state-of-the-art nonnegative spectro-temporal factorization methods.

Development of a high-performance arithmetic and logic unit for efficient digital signal processing based on reversible logic and quantum dots

Digital Signal Processing (DSP) finds a wide range of applications in various fields, including telecommunications, audio and video processing, biomedical engineering, radar systems, and image processing. Previous DSP designs faced limitations in available processing power and computational resources. Insufficient processing power could result in slower execution times, an inability to handle complex algorithms, or limited capacity to process high-speed or large-scale signals. As the demand for minimal power consumption in DSP circuits continues to grow, reversible logic and quantum-dot cellular automata (QCA) have emerged as promising technologies due to their inherent ability to reduce energy loss. Within this landscape, the arithmetic and logic unit (ALU) plays a vital role in complex circuitry, serving as a key component in digital signal processing applications. However, challenges persist, including high quantum cost and the need to limit the number of cells in the ALU design. To address these challenges, our research aims to develop an efficient ALU by integrating reversible logic and QCA technology. Our focus will be on generating essential components, such as Feynman gates, Fredkin gates, and full adder circuits, which serve as foundational building blocks for reversible logic and QCA designs. These components will be combined to construct a comprehensive ALU capable of performing 20 different operations. Our implementation efforts will be centered around QCADesigner, with a specific emphasis on digital signal processing systems that prioritize energy efficiency and optimal utilization of occupied areas.

Fake Voice Detection System

Fake Voice Detection System

Optical wireless communication using a flexible and waterproof perovskite color converter.

In this Letter, the CH3NH3PbBr3 nanocrystals (NCs) are embedded into the interstices of the fluorine (polyvinyl fluoride/polyvinylidene fluoride, PVF/PVDF) matrix on polyethylene terephthalate (PET) substrate to introduce new advantages, such as being flexible and waterproof, while maintaining the high optical performance of perovskites. The sample's photoluminescence (PL) spectra under 325 nm laser is a green emission peaked at 537 nm with full width at half maximum (FWHM) of about 21.2 nm and a fast PL decay time. As a color converter, it shows high optical absorption and can transform light from solar-blind ultraviolet to a blue region into a green region in air, water, and bending conditions. While excited by a 270 nm ultraviolet light-emitting diode (LED), the system's observed -3 dB bandwidth with the color converter is near 4.4 MHz in air and water conditions with well-eye diagrams at a data rate of 30 Mbps. Finally, we demonstrate an audio transmission application with an ultraviolet light source, a color conversion layer, and a low-cost silicon-based photodetector.

Implementing consultation audio recording applications to improve informed decision-making (IDM) in patients (pts) with metastatic castration-resistant prostate cancer (mCRPC) eligible for docetaxel.

103 Background: Pts with mCRPC have multiple treatment options and face challenges to IDM. This leads some to make poorly informed or goal-discordant decisions. Consultation audio recordings are known to improve IDM by improving recall, but uptake has been limited. It is unclear whether pt-administered apps are an effective, feasible strategy to increase access to recordings and improve IDM. Methods: We conducted a single-site implementation trial. Pts were English-speaking with progressive mCRPC and an upcoming oncology visit in which treatment options including docetaxel would be discussed. Pre-visit, a coordinator sent instructions, provided coaching, and sent text reminders to help pts create recordings using their mobile device. To evaluate change in IDM pre- vs post- the audio-recorded visit, we used an investigator-developed questionnaire testing pt knowledge about docetaxel (19 items, 0-100% correct) and the decisional conflict scale-informed subscale (3 items, 0=feels extremely uninformed to 100=feels extremely informed). Change was evaluated using the Wilcoxon signed-rank test. We also measured implementation: rates of consent, instruction receipt, recording, and listening, as well as pt-reported helpfulness of the app in decision-making. Lastly, we interviewed pts to understand benefits, barriers, and facilitators. Results: Of 78 pts approached, 44 (56%) consented, and 41 (53%) were evaluable. Top reasons for not consenting were too busy (7, 21%), illness (5, 15%), and inadequate devices (4, 12%). Mean age was 75y [56-90], and 34 (83%) were White. Median knowledge about docetaxel increased from 47 to 53 (P=0.048), corresponding to one additional correct response. Median informed subscore increased from 50 to 75 (P=0.011), corresponding to an improvement from feeling neither informed nor uninformed to feeling informed. All pts received instructions, 38 (93%) recorded their visits, and 28 (68%) listened to the recording. Twenty-six pts (63%) found the app helpful in decision-making. In pt interviews, benefits of recording were better recall of treatment options and toxicities, greater decision confidence, and peace of mind. Most frequently reported barriers to use were technology unfamiliarity and disconnected pt portal, telehealth, and recording applications; facilitators were app simplicity and caregiver/coordinator assistance. Conclusions: Implementation of self-administered mobilerecordings was feasible and associated with increased pt knowledge and feeling more informed about treatment with docetaxel in pts with mCRPC. Future efforts should focus on non-White, no/limited-English-speaking populations, and increasing recording/listening rates by addressing barriers and augmenting facilitators (e.g., integrating recordings in electronic pt portals). Clinical trial information: NCT05127850 .

The role of multimedia technologies in teaching foreign languages in an international context

Relevance. The research on the impact of multimedia technologies on international educational environments is primarily driven by the constant change in pedagogical strategies and the improvement of technical equipment that accompanies the educational process.Purpose. The study aims to analyse the peculiarities of the use, role and effectiveness of multimedia technologies in modern education on the example of the experience of different countries.Methodology. The comparative and contrastive, pedagogical, analytical and synthetic methods were used in this study. Based on the analysis of various educational environments, the following conclusions were drawn phonetic competence was developed through animated videos, presentations, and video and audio applications, lexical skills were developed through presentations, games, video and audio materials, web applications, grammatical and syntactic competences required the use of audio and video materials.Results. Among the multimedia learning tools mentioned were applications: VLC Media Player, KMPlayer, GOM Player � for playing audio content, Moho, Boom Harmony, FlipaClip � for creating animations, Google Slides, Prezi, Microsoft PowerPoint � for creating presentations. In the international context, the most commonly used applications are Duolingo: Language Lessons, Babbel � Learn Languages, AnkiDroid Flashcards, Quizlet: AI-powered Flashcards, and Memrise: speak a new language. According to the American experience, the greatest impact of multimedia use was on the development of phonetic, grammatical, and lexical skills. From the point of view of the French experience, the highest level of growth was recorded in information comprehension skills, followed by vocabulary, writing skills and argumentation.Conclusions. Based on the Indian and Thai experience, it can be concluded that the growth in language skills was about 50%. This work can be used to analyse pedagogical experience in an international context, to build educational strategies for learning foreign languages through the use of multimedia, and to identify the main criteria that contribute to the improvement of language skills.

Lorenz Kaotik Devre Tabanlı Entropi Kaynağı ile Özgün Gerçek Rasgele Sayı Üretimi ve Ses Şifreleme Uygulaması

This paper introduces a methodology for generating secure cryptographic key bits from analog values obtained from the Lorenz chaotic circuit. The analog values are transferred from the chaotic circuit to the computer via an Analog Discovery-2 device and then post-processed by using a fixed-point number representation format and Von-Neumann corrector for the sampled values on the MATLAB program. By employing this method, the analog values are digitized and randomized as the main idea is to obtain secure and efficient statistically random bits. Furthermore, the generated random bits are utilized for secure audio transmission by demonstrating a practical application of the proposed methodology. In addition to the classical randomness test criteria, and NIST 800.22 statistical test suite, the throughput bit stream is also subjected to Chi-square and FIPS 140-1 tests to further evaluate its effectiveness. The results of these comprehensive tests confirm the successful performance of the proposed system in generating statistically random bits and its secure audio encryption system. The utilization of the Lorenz chaotic circuit as an entropy source in generating true random bits for secure audio transmission applications showcases the potential of the proposed system.

Server Redundancy: Performa Jaringan Mengunakan DNS Failover MikroTik pada Kasus Private Server dan Public Server

The digitization of user services has increased, in line with the need for VPS and cloud computing services, which are rampant among application and platform developers. Quite a few companies that create applications or application users have servers to handle user needs. Testing is carried out using the ICMP Protocol to get real-time results and can be measured. From Scenario 1, carrying out 20 test requests, we get a packet loss of 5% with RTT Avarage of 195,838ms and Mdev 4,103ms. If you apply DNS failover in scenario 2, the client will likely access the web a little slower, as evidenced by the packet loss being 25% greater in value. Compared to scenario 1, having a high standard deviation (Mdev) of round-trip times is not desirable. This variation is also known as jitter. Increased jitter can cause a bad user experience, especially in real-time audio and video streaming applications. However, this is still understandable because it only has a 1-5 second effect on the service. Next, in scenario 3, we can see that private and public servers have relatively high gab with 0% packet loss, which has a small Mdev value of 0.309ms. Therefore, the DNS failover method is a solution for network administrators who have problems related to server migration between public servers and private servers so that services can run even if a server is maintaining or downlinking.

Online adaptive identification of multichannel systems for audio applications.

Impulse responses (IRs) estimation of multi-input acoustic systems is a prerequisite for many audio applications. In this paper, an adaptive identification problem based on the Autostep algorithm is extended to the simultaneous estimation of room IRs for multiple input single output linear time invariant systems without any a priori information. To do so, the proposed algorithm is initially evaluated in a simulated room with several sound sources active at the same time. Finally, an experimental validation is proposed for the cases of a semi-anechoic chamber and an arbitrary room. Special attention is dedicated to the algorithm convergence behavior, considering different meta parameters settings. Results are eventually compared with the other normalized version of the least mean square algorithm.

Nonlinear control-based class-D amplifier for audio intelligent infrastructure applications

Nonlinear control-based class-D amplifier for audio intelligent infrastructure applications

Digital Audio Applications on Students’ Abilities in EFL: A Systematic Literature Review

This systematic review aims to analyze and summarize previous studies on the use of digital audio applications like Spotify or JOOX to improve the abilities of Indonesian EFL learners who face difficulties in learning English due to differences in structure, pronunciation, and vocabulary. This study utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to obtain the results. A comprehensive search was conducted on Google Scholar and Open Knowledge Maps, reviewing a total of ten journal articles published between 2019 and 2023. The selection process followed a systematic approach, adhering to specific inclusion and exclusion criteria. The result found the use of digital audio applications shows a positive impact on students’ abilities. The findings assert that digital audio applications improve students’ abilities with features, English songs, and podcasts. The previous studies agree that digital audio application gives students easier ways to learn pronunciation and vocabulary with features, like lyrics, curated playlists, and English songs. The practical implication of this study is that teachers are advised to use digital audio applications to support students’ abilities with clear objective and careful considerations in using digital audio applications as teaching media.