Sound Source Research Articles

High-speed noncontact acoustic inspection using acoustic irradiation induced vibration from unmanned aerial vehicle

The noncontact acoustic inspection method using laser Doppler vibrometer and acoustic irradiation induced vibration is a method that can be used as an alternative to the hammer method because it uses flexural resonance. Experiments have already been carried out on railway and national highway tunnels, viaducts over 30m, and shotcrete with unevenness in underground cavities, confirming the usefulness of this method. On the other hand, this method has the problem of environmental noise and angular dependence. However, when the sound source is mounted on the UAV, the position of the sound source can be directly opposed to the surface to be measured, and this environmental noise and angle dependency problem is expected to be solved. In addition, since this method does not require a scaffold or an aerial work vehicle, there is a possibility that inspection costs can be reduced and work efficiency can be improved. Therefore, an experiment using a sound source-mounted UAV and a tiled outer wall specimen was performed. From the experimental results, it was clarified that defects within the directivity range of sound waves can be detected even if the UAV fluctuates due to wind. In the future, it will be possible to detect defects with higher accuracy by applying artificial intelligence to the measured waveform.

Combined Effect of Noise Reduction, FBS-Based Spectral Splitting, and Dynamic Range Compression of Speech Signal on Source Localization in Binaural Hearing Aids

Localizing sound sources in three spatial dimensions (azimuth, elevation, and distance) is critical for human hearing comfort. It relies on two binaural cues: interaural time difference (ITD) and interaural level difference (ILD). Cochlear or auditory nerve injury can result in sensorineural hearing loss (SNHL). Hearing aids enable people with sensorineural hearing loss to converse more effectively and hear better. However, there is an apprehension that the binaural hearing aids may degrade the localization cues, thus affecting the source localization. In response to this concern, the current study investigates how the binaural hearing aid algorithm affects source localization by adopting a cascaded structure of noise reduction technique (wiener filter) followed by filter bank summation (FBS) based spectral splitting and dynamic range compression for binaural dichotic presentation. Listening tests for seven different azimuth angles (-90⁰, -60⁰, -30⁰, 0⁰, 30⁰, 60⁰, and - 90⁰) were conducted on six listeners with normal hearing under different signal-to-noise ratio (SNR) conditions as well as on six subjects with mild bilateral sensorineural hearing impairment. Test stimuli included background glass-breaking sound and broadband noise for participants with normal hearing. In an experiment with hearing-impaired subjects, the glass-breaking sound served as one of the test stimuli. The result showed that these binaural hearing aid algorithms had no adverse effects on localization ability.

Constraining dark matter from strong phase transitions in a U1Lμ−Lτ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \ extrm{U}{(1)}_{L_{\\mu }-{L}_{\ au }} $$\\end{document} model: implications for neutrino masses and muon g − 2

In this paper, we study a non-minimal gauged U1Lμ−Lτ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \ extrm{U}{(1)}_{L_{\\mu }-{L}_{\ au }} $$\\end{document} model, where we add two complex singlet scalars, three right-handed Majorana neutrinos (RHN), and a vector-like dark fermion to the Standard Model (SM), all non-trivially charged under the extra gauge symmetry. The model offers an easy resolution to the muon (g – 2) anomaly, which fixes the scale of spontaneous symmetry breaking. Furthermore, the two-zero minor structure in the RHN mass matrix provides successful predictions for neutrino oscillation parameters, including the Dirac phase. The extended scalar sector can easily induce first-order phase transitions. We identify all possible phase transition patterns in the three-dimensional field space. We quantify the associated gravitational waves from the sound wave source and demonstrate that the signatures can be observed in future space-based experiments. We find that strong first-order phase transitions require large values of scalar quartic couplings which constrain the scalar dark matter (DM) relic density to a maximum of 10−2 and 10−5 when we consider the DM direct detection bound. Nonetheless, the model successfully explains the DM relic density via contribution from the vector-like dark fermion. We show the allowed range of the model parameters that can address all the beyond SM issues targeted in this study.

Enhancing Situational Awareness with VAS-Compass Net for the Recognition of Directional Vehicle Alert Sounds

People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study aims to develop a sound alert assistance system (SAAS) to enhance situational awareness and improve travel safety for people with hearing impairments. We proposed the VAS-Compass Net (Vehicle Alert Sound–Compass Net), which integrates three lightweight convolutional neural networks: EfficientNet-lite0, MobileNetV3-Small, and GhostNet. Through employing a fuzzy ranking ensemble technique, our proposed model can identify different categories of vehicle alert sounds and directions of sound sources on an edge computing device. The experimental dataset consisted of images derived from the sounds of approaching police cars, ambulances, fire trucks, and car horns from various directions. The audio signals were converted into spectrogram images and Mel-frequency cepstral coefficient images, and they were fused into a complete image using image stitching techniques. We successfully deployed our proposed model on a Raspberry Pi 5 microcomputer, paired with a customized smartwatch to realize an SAAS. Our experimental results demonstrated that VAS-Compass Net achieved an accuracy of 84.38% based on server-based computing and an accuracy of 83.01% based on edge computing. Our proposed SAAS has the potential to significantly enhance the situational awareness, alertness, and safety of people with hearing impairments on the road.

Audiological and Subjective Benefits in a Child with Microtia and Atresia After Sequential Bilateral Implantation with Active Bone Conduction Devices: A Case Study

Background: With bilateral hearing loss, the main problems for the patient are speech understanding in noise and, especially in asymmetrical hearing loss, an inability to correctly localize sound sources. There are multiple methods of treatment and rehabilitation for people with conductive hearing loss, and one of them is to use an active bone conduction implant. This case study is designed to evaluate the auditory benefits and sound localization accuracy with active bilateral bone conduction implants—in comparison to unilateral ones—in a patient with congenital bilateral conductive hearing loss caused by a congenital malformation. We assess subjective and audiological benefits (functional, directional hearing, speech comprehension in quiet and noise). Case report: This study describes the results in a 15-year-old patient with bilateral congenital malformation of the outer ears and associated conductive hearing loss who was treated with two Bonebridge active bone conduction implants. Speech recognition ability, hearing thresholds, and sound localization were tested under three conditions: unaided, unilateral on the right-side, and bilateral on both sides. The patient filled in an Abbreviated Profile of Hearing Aid Benefit questionnaire (APHAB) to evaluate limitations in daily life caused by hearing impairment. The results show an improvement in free-field hearing thresholds and the ability to discriminate speech, both in quiet and in noise after implantation. Subjectively, the patient had significantly fewer problems with two implants than with one (or with no implant) in terms of hearing in everyday situations. Conclusions: Unilateral use of the Bonebridge device in a patient with congenital bilateral conductive hearing loss did not provide full benefits. However, bilateral implantation improved speech understanding in noise and sound localization.

Equipment Sounds’ Event Localization and Detection Using Synthetic Multi-Channel Audio Signal to Support Collision Hazard Prevention

Construction workplaces often face unforeseen collision hazards due to a decline in auditory situational awareness among on-foot workers, leading to severe injuries and fatalities. Previous studies that used auditory signals to prevent collision hazards focused on employing a classical beamforming approach to determine equipment sounds’ Direction of Arrival (DOA). No existing frameworks implement a neural network-based approach for both equipment sound classification and localization. This paper presents an innovative framework for sound classification and localization using multichannel sound datasets artificially synthesized in a virtual three-dimensional space. The simulation synthesized 10,000 multi-channel datasets using just fourteen single sound source audiotapes. This training includes a two-staged convolutional recurrent neural network (CRNN), where the first stage learns multi-label sound event classes followed by the second stage to estimate their DOA. The proposed framework achieves a low average DOA error of 30 degrees and a high F-score of 0.98, demonstrating accurate localization and classification of equipment near workers’ positions on the site.

A study on aerodynamic pass-by noise based on equivalent source method

Pass-by noise generated by high-speed trains is an important influencing factor on the surrounding environment and is getting more and more attention with the increase of speed. In the past, the aerodynamic pass-by noise prediction was usually conducted with the wind tunnel mode, ignoring the Doppler effect. In this paper, an equivalent source method considering the Doppler effect is proposed to study the aerodynamic pass-by noise. A cylinder case is considered first to verify the effectiveness of the method. Then a DSA 380 pantograph of a Chinese high-speed train is considered. The pantograph is divided into three parts, and each part is equivalent to a monopole or dipole sound source. Results show that the upper part of the pantograph is closer to a dipole source, while the middle and lower parts are closer to a monopole source. When the measurement point is directly above the pantogragh, the contribution of the upper part is the largest, while for both sides measurement points of the pantograph, the contribution of the upper part is the smallest.

Combined Keyword Spotting and Localization Network Based on Multi-Task Learning

The advent of voice assistance technology and its integration into smart devices has facilitated many useful services, such as texting and application execution. However, most assistive technologies lack the capability to enable the system to act as a human who can localize the speaker and selectively spot meaningful keywords. Because keyword spotting (KWS) and sound source localization (SSL) are essential and must operate in real time, the efficiency of a neural network model is crucial for memory and computation. In this paper, a single neural network model for KWS and SSL is proposed to overcome the limitations of sequential KWS and SSL, which require more memory and inference time. The proposed model uses multi-task learning to utilize the limited resources of the device efficiently. A shared encoder is used as the initial layer to extract common features from the multichannel audio data. Subsequently, the task-specific parallel layers utilize these features for KWS and SSL. The proposed model was evaluated on a synthetic dataset with multiple speakers, and a 7-module shared encoder structure was identified as optimal in terms of accuracy, direction of arrival (DOA) accuracy, DOA error, and latency. It achieved a KWS accuracy of 94.51%, DOA error of 12.397°, and DOA accuracy of 89.86%. Consequently, the proposed model requires significantly less memory owing to the shared network architecture, which enhances the inference time without compromising KWS accuracy, DOA error, and DOA accuracy.

Exploding Head Syndrome among Patients Seeking Help for Tinnitus and/or Hyperacusis at an Audiology Department in the UK: A Preliminary Study.

Exploding head syndrome (EHS) is characterized by hearing a sudden loud noise or experiencing a sense of explosion in head during the transition of sleep-wake or wake-sleep. The experience of EHS shares similarities with tinnitus, where an individual perceives a sound without any external sound source. To the authors' knowledge, the possible relationship between EHS and tinnitus has not been explored. Preliminary assessment of prevalence of EHS and its related factors among patients seeking help for tinnitus and/or hyperacusis. Retrospective cross-sectional study. A total of 148 consecutive patients who sought help for tinnitus and/or hyperacusis at an audiology clinic in the United Kingdom. The data regarding demographics, medical history, audiological measures, and self-report questionnaires were collected retrospectively from the patients' records. Audiological measures comprised of pure-tone audiometry and uncomfortable loudness levels. The self-report questionnaires which were administered as a part of standard care comprised of the Tinnitus Handicap Inventory, numeric rating scale of tinnitus loudness, annoyance, and effect on life, hyperacusis questionnaire, Insomnia Severity Index, Generalized Anxiety Disorder scale 7, and Patient Health Questionnaire (PHQ-9). To establish presence of EHS, participants were asked "Do you ever hear a sudden, loud noise, or feel a sense of explosion in your head at night?" EHS was reported by 8.1% of patients with tinnitus and/or hyperacusis (12 out of 148). The patients with and without EHS were compared, but no significant relationships were found, between the presence of EHS and age, gender, tinnitus/hyperacusis distress, symptoms of anxiety or depression, sleep difficulties, or audiological measures. The prevalence of EHS in a tinnitus and hyperacusis population is similar to that in the general population. While there does not seem to be any association with sleep or mental factors, this might be due to the limited variability in our clinical sample (i.e., most patients exhibited high level of distress regardless of EHS). Replication of the results in a larger sample with more variety of symptom severity is warranted.

Effect of Bio-Inspired Cutout Shapes at the Leading Edge of Propellers on Noise and Flight Efficiency

In recent years, the application of bio-inspired structures has garnered attention for enhancing the performance of fluid machinery. In this study, we experimentally investigated the effects of introducing a bio-inspired cutout structure to the propellers of drones, aiming to improve thrust efficiency and reduce noise levels. Our results demonstrated reductions in noise levels compared to conventional propellers. Parametric studies revealed that the roundness of the structure significantly influenced both flight efficiency and noise levels, suggesting its importance for replicating the inherent fluid characteristics found in nature. Additionally, optimal parameters for noise reduction, such as the length of the cutout, angle of incision relative to the flow direction, and the distance between the gap were identified. Although no improvements in flight efficiency were observed, most of the models investigated exhibited only around a 5% reduction in efficiency compared to the standard propellers, suggesting practical applicability for scenarios such as nighttime drone operations in urban areas. The noteworthy reduction in sound pressure levels in the mid- to high-frequency range achieved by the bio-inspired propellers in this study holds the potential to address the issue of drone noise pollution and encourage drone operations in urban areas. Moreover, the confirmed decrease in sound pressure at specific frequencies and the suggested controllability hint at the possibility of enhancing sound source localization performance using drones.

A Non-Destructive Measurement Approach for the Internal Temperature of Shiitake Mushroom Sticks Based on a Data–Physics Hybrid-Driven Model

This study aimed to develop a non-destructive measurement method utilizing acoustic sensors for the efficient determination of the internal temperature of shiitake mushroom sticks during the cultivation period. In this research, the sound speed, air temperature, and moisture content of the mushroom sticks were employed as model inputs, while the temperature of the mushroom sticks served as the model output. A data–physics hybrid-driven model for temperature measurement based on XGBoost was constructed by integrating monotonicity constraints between the temperature of the mushroom sticks and sound speed, along with the condition that limited the difference between air temperature and stick temperature to less than 2 °C. The experimental results indicated that the optimal eigenfrequency for applying this model was 850 Hz, the optimal distance between the sound source and the shiitake mushroom sticks was 8.7 cm, and the temperature measurement accuracy was highest when the moisture content of the shiitake mushroom sticks was in the range of 56~66%. Compared to purely data-driven models, our proposed model demonstrated significant improvements in performance; specifically, RMSE, MAE, and MAPE decreased by 74.86%, 77.22%, and 69.30%, respectively, while R2 increased by 1.86%. The introduction of physical knowledge constraints has notably enhanced key performance metrics in machine learning-based acoustic thermometry, facilitating efficient, accurate, rapid, and non-destructive measurements of internal temperatures in shiitake mushroom sticks.

The Impact of Standing Waves on Localization Ability of Low-Frequency Sound Sources

This paper studies the effect of room modal resonances on the localization of very low–frequency sound sources. A subjective listening test is conducted with 20 participants in an anechoic chamber, where the listener must detect the direction of the sound source for pure sinusoids at 31.5, 50, and 80 Hz. A synthetic standing wave pattern modeling a room resonant effect is created with two additional sound sources located at the left and right sides of the listener. Results show that the perception of low-frequency direction is negatively impacted by the node of the standing wave, even when the standing wave has a relatively low level, whereas the antinode does not have as strong of an effect. The second experiment conducted indicates that variations in the presentation level does not impact the effect of the standing wave. The results of this study suggest that in the low-frequency spectrum, direction judgement is not so strongly a question of this auditory system’s ability but more so of the acoustical properties of the listening environment.

Morten Qvenild – The HyPer(sonal) Piano Project

Towards a (per)sonal topography of grand piano and electronics How can I develop a grand piano with live electronics through iterated development loops in the cognitive technological environment of instrument, music, performance and my poetics? The instrument I am developing, a grand piano with electronic augmentations, is adapted to cater my poetics. This adaptation of the instrument will change the way I compose. The change of composition will change the music. The change of music will change my performances. The change in performative needs will change the instrument, because it needs to do different things. This change in the instrument will show me other poetics and change my ideas. The change of ideas demands another music and another instrument, because the instrument should cater to my poetics. And so it goes… These are the development loops I am talking about. I have made an augmented grand piano using various music technologies. I call the instrument the HyPer(sonal) Piano, a name derived from the suspected interagency between the extended instrument (HyPer), the personal (my poetics) and the sonal result (music and sound). I use old analogue guitar pedals and my own computer programming side by side, processing the original piano sound. I also take out control signals from the piano keys to drive different sound processes. The sound output of the instrument is deciding colors, patterns and density on a 1x3 meter LED light carpet attached to the grand piano. I sing, yet the sound of my voice is heavily processed, a processing decided by what I am playing on the keys. All sound sources and control signal sources are interconnected, allowing for complex and sometimes incomprehensible situations in the instrument´s mechanisms. Credits: First supervisor: Henrik Hellstenius Second Supervisors: Øyvind Brandtsegg and Eivind Buene Cover photo by Jørn Stenersen, www.anamorphiclofi.com All other photo, audio and video recording/editing by Morten Qvenild, unless stated.

Effect of Interpolation Artifacts on Perceived Stability of Nearby Sources in a Navigable Reverberant Virtual Environment

Convolution with spatial Room Impulse Responses can achieve realistic auralizations. When combined with interpolation between spatially distributed RIRs, this technique can be used to create navigable virtual environments. This study explores the impact of various interpolation parameters on the perceived auditory stability of a nearby static sound source with listener movements in a reverberant environment. The auditory scene was rendered via third-order Ambisonic RIR convolution combined with magnitude-least-squares binaural decoding using nonindividualized head-related transfer functions. First, the estimated direction of arrival as a function of the listener’s position within a 2D grid of RIRs under various configurations is examined as an objective metric. The perceived stability of the auditory source is then assessed through a perceptual experiment. Participants freely explored a virtual scene reproduced over headphones and a tracked head-mounted display. They were asked to rate the stability of a nonvisual source under various conditions of RIR grid density, interpolation panning method, and reverberation time. Results indicate no need to use an RIR grid size finer than 1m to optimize source stability when using a three-nearest-neighbor interpolation scheme.

A Highly-Sensitive Omnidirectional Acoustic Sensor for Enhanced Human-Machine Interaction.

Acoustic sensor-based human-machine interaction (HMI) plays a crucial role in natural and efficient communication in intelligent robots. However, accurately identifying and tracking omnidirectional sound sources, especially in noisy environments still remains a notable challenge. Here, a self-powered triboelectric stereo acoustic sensor (SAS) with omnidirectional sound recognition and tracking capabilities by a 3D structure configuration is presented. The SAS incorporates a porous vibrating film with high electron affinity and low Young's modulus, resulting in high sensitivity (3172.9 mVpp Pa-1) and a wide frequency response range (100-20 000 Hz). By utilizing its omnidirectional sound recognition capability and adjustable resonant frequency feature, the SAS can precisely identify the desired audio signal with an average deep learning accuracy of 98%, even in noisy environments. Moreover, the SAS can simultaneously recognize multiple individuals in the auxiliary conference system and the driving commands under background music in self-driving vehicles, which marks a notable advance in voice-based HMI systems.

Auditory and vestibular function in mitochondrial patients harbouring the m.3243A>G variant

Abstract Hearing impairment is a frequent clinical feature in patients with mitochondrial disease harbouring the pathogenic variant, m.3243A>G. However, auditory neural dysfunction, its perceptual consequences, as well as implications for patient management are not established. Similarly, the association with vestibular impairment has not yet been explored. This case-control study investigated in 12 adults with genetically confirmed m.3243A>G adults (nine females; 45.5±16.3 years [range 18–66]; 47.1±21.5dB hearing level, dB) compared to 12 age, sex, and hearing level matched controls with sensory (cochlear-level) hearing loss (nine females; 46.6±11.8 years [range 23–59]; 47.7±25.4 hearing level, dB). Participants underwent a battery of electro-acoustic, electrophysiologic and perceptual tests which included: pure tone audiometry, otoacoustic emissions, auditory brainstem responses, auditory temporal processing measures, monaural/binaural speech perception, balance and vestibular testing, and self-reported questionnaires (dizziness, hearing disability). Our findings showed evidence of auditory neural abnormality and perceptual deficits greater than expected for cochlear pathology. Compared with matched controls with sensory hearing loss, adults with mitochondrial disease harbouring m.3243A>G had abnormal electrophysiologic responses from the VIIIth nerve and auditory brainstem (P = 0.005), an impaired capacity to encode rapidly occurring acoustic signal changes (P = 0.005), a reduced ability to localise sound sources (P = 0.028) and impaired speech perception in background noise (P = 0.008). Additionally, vestibular dysfunction (P = 0.011), greater perceived dizziness (P = 0.001) and reduced stance time (balance) (P=0.009) were also seen in participants with m.3243A>G mitochondrial disease when compared to matched counterparts. This pilot study revealed that auditory evaluation, including evoked potential responses from the auditory nerve/brainstem and speech perception in noise tests should form an important part of the management for individuals with m.3243A>G-related mitochondrial disease. Those presenting with hearing impairment and symptoms concerning balance and dizziness should undergo vestibular testing and appropriate management.

Simulation and experimental research on the propagation mechanisms of acoustic energy inside the pipeline to detect the leakage

Leakage from pipelines has negative impacts on property and environment. The diameters of internal acoustic detectors in pipelines are small and not easily blocked, making it suitable for pipelines under various working conditions. Studying the characteristics of pipeline leakage sound signal and the propagation mechanisms within pipelines is crucial for timely detection of small leaks. In this study, a 3D simulation model of pipeline leakage was established, and a CFD/CAA hybrid calculation method was used to obtain effective leakage flow field information via LES. The equivalent sound sources in the flow field were extracted based on the Lighthill acoustic analogy theory. Determine the characteristic frequency range of the leakage sound signal was 1–2 kHz. This study founded that an increase in the leakage pressure and aperture size both lead to an enhancement in the leakage sound signal, and the propagation of leakage sound signal in pipelines is affected by background flow. An experimental pipeline leakage system was designed, and the accuracy of the simulation results was verified experimentally.

Non-stationary mechanical sound source separation: An all-neural beamforming network driven by time–frequency convolution and self-attention

To tackle the challenging task of extracting target signal features amidst significant interference from complex, time-varying non-stationary noise in industrial scenarios, we propose a novel time-domain all-neural beamforming network tailored for non-stationary mechanical sound source separation, named TFANBNet. Leveraging time–frequency convolution and self-attention, TFANBNet promised robust performance in challenging acoustic environments. First, the proposed time–frequency convolution module employs parameterized complex-valued convolutional layers to simulate time–frequency transformations, thereby implicitly extracting time–frequency features. Then, the proposed adaptive attention transformer network enhances local attention interaction by integrating intermediate features, effectively enhancing long-range context modeling. Third, the beamforming module utilizes deep neural networks for beamforming weight estimation, replacing conventional noise covariance matrix computations, which have inherent performance limitations. Finally, the experimental results on the multi-channel spatial reverberation dataset synthesized from the MIMII dataset show that the proposed TFANBNet yields superior separation performance and generalization capabilities compared to the considered competitive methods.

Ensemble width estimation in HRTF-convolved binaural music recordings using an auditory model and a gradient-boosted decision trees regressor

Binaural audio recordings become increasingly popular in multimedia repositories, posing new challenges in indexing, searching, and retrieval of such excerpts in terms of their spatial audio scene characteristics. This paper presents a new method for the automatic estimation of one of the most important spatial attributes of binaural recordings of music, namely “ensemble width.” The method has been developed using a repository of 23,040 binaural excerpts synthesized by convolving 192 multi-track music recordings with 30 sets of head-related transfer functions (HRTF). The synthesized excerpts represented various spatial distributions of music sound sources along a frontal semicircle in the horizontal plane. A binaural auditory model was exploited to derive the standard binaural cues from the synthesized excerpts, yielding a dataset representing interaural level and time differences, complemented by interaural cross-correlation coefficients. Subsequently, a regression method, based on gradient-boosted decision trees, was applied to the formerly calculated dataset to estimate ensemble width values. According to the obtained results, the mean absolute error of the ensemble width estimation averaged across experimental conditions amounts to 6.63° (SD 0.12°). The accuracy of the method is the highest for the recordings with ensembles narrower than 30°, yielding the mean absolute error ranging between 0.8° and 10.2°. The performance of the proposed algorithm is relatively uniform regardless of the horizontal position of an ensemble. However, its accuracy deteriorates for wider ensembles, with the error reaching 25.2° for the music ensembles spanning 90°. The developed method exhibits satisfactory generalization properties when evaluated both under music-independent and HRTF-independent conditions. The proposed method outperforms the technique based on “spatiograms” recently introduced in the literature.

Study on Depth Estimation and Characteristic Analysis of Underwater Source Based on Deep-Sea Broadband Signal Modeling

Studies on estimating the underwater sound source localization using acoustic signal characteristics have mainly been conducted in shallow waters. Recently, technologies for stably and efficiently estimating the underwater sound sources localization using the underwater sound propagation characteristics of the Reliable Acoustic Path(RAP) in deep water areas are being studied. Underwater surveillance technology in deep sea areas is known to have the advantage of having low detection performance variability due to time-varying underwater environments and having a small shadow zone, making it easy to stably detect underwater sound sources and estimate location even from relatively long distance. In this study, we analyzed the sound propagation characteristics based on the actual marine environment in the deep sea of the Korean Peninsula and conducted a study to analyze the estimation performance of sound source depth using the broadband interference pattern of direct wave and sea surface reflected waves radiating from underwater sound sources.