Identification Of Acoustic Signals Research Articles

Research on Acoustic Signal Identification Mechanism and Denoising Methods of Combine Harvesting Loss

Research on Acoustic Signal Identification Mechanism and Denoising Methods of Combine Harvesting Loss

A Novel NLOS Acoustic Signal Identification Method for Indoor Localization Based on Machine Learning (Revised and Resubmit)

A Novel NLOS Acoustic Signal Identification Method for Indoor Localization Based on Machine Learning (Revised and Resubmit)

Indoor Human Action Recognition Based on Dual Kinect V2 and Improved Ensemble Learning Method.

Indoor human action recognition, essential across various applications, faces significant challenges such as orientation constraints and identification limitations, particularly in systems reliant on non-contact devices. Self-occlusions and non-line of sight (NLOS) situations are important representatives among them. To address these challenges, this paper presents a novel system utilizing dual Kinect V2, enhanced by an advanced Transmission Control Protocol (TCP) and sophisticated ensemble learning techniques, tailor-made to handle self-occlusions and NLOS situations. Our main works are as follows: (1) a data-adaptive adjustment mechanism, anchored on localization outcomes, to mitigate self-occlusion in dynamic orientations; (2) the adoption of sophisticated ensemble learning techniques, including a Chirp acoustic signal identification method, based on an optimized fuzzy c-means-AdaBoost algorithm, for improving positioning accuracy in NLOS contexts; and (3) an amalgamation of the Random Forest model and bat algorithm, providing innovative action identification strategies for intricate scenarios. We conduct extensive experiments, and our results show that the proposed system augments human action recognition precision by a substantial 30.25%, surpassing the benchmarks set by current state-of-the-art works.

An attention-based multi-scale convolution network for intelligent underwater acoustic signal recognition

Underwater acoustic signal identification is always an extremely challenging task due to the intricate marine environments and background noise interference. Existing techniques typically focus on handling only one type of specific features for recognition, overlooking a comprehensive representation of distinct vessel attributes and struggling to handle intricate scenarios. To address these challenges, we employ a band-pass filter bank for feature extraction. This process extracts multiple representations of the same data, each concentrating on distinct frequency components. Subsequently, the filtered multi-view representation is channeled into an attention-based multi-scale convolution network (AMSC-Net) for recognition purposes. AMSC-Net integrates a sequence of attention-based multi-scale convolution (AMSC) blocks to capture pivotal features tied to identifying diverse ship-radiated audio categories. Specifically, a set of parallel residual convolution layers with different kernels are designed to capture multi-scale features, and an adaptive channel attention module is followed to highlight dominant parts within global features. To enrich training data in terms of quantity and diversity, a data augmentation strategy is implemented during the training phase. Our proposals are evaluated on two real-world underwater acoustic signal datasets. Experimental results firmly establish the superiority of our proposed AMSC-Net, which substantially outperforms competitive DNN-based methods. Moreover, our proposals exhibit robustness across different noise levels, achieving an overall accuracy of 95.87% and 95.39% for the two datasets at 0 dB signal-to-noise ratio (SNR), respectively.

An Advanced Artificial Fish School Algorithm to Update Decision Tree for NLOS Acoustic Localization Signal Identification with the Dual-Receiving Method

For indoor sensor systems, it is essential to implement an extra supporting area notification part. To inform the real-time coordinates, the time difference of arrival (TDOA) algorithm can be introduced. For these indoor localization systems, their main processes are often built based on the line of sight (LOS) scenario. However, obstacles make the off-the-shelf localization system unable to play its due role in the flexible non-line of sight (NLOS) scenario. So, it is necessary to adjust the signals according to the NLOS identification results. However, the NLOS identification methods before were not effective enough. To address these challenges, on the one hand, this paper proposes an adaptive strategy for a dual-receiving signal processing method. On the other hand, the system is matched with the homologous NLOS identification method based on a novel artificial fish school algorithm (AFSA) and the decision tree model. According to our experiments, our novel AFSA optimization method can obtain a better effect and take less time. The NLOS acoustic signal identification accuracy can be improved significantly in flexible scenarios compared with other methods. Based on these processes, the system will achieve more accurate localization results in flexible NLOS situations.

PLDA in i-vector based underwater acoustic signals classification

ABSTRACT Intelligent recognition of underwater acoustic targets is crucial for exploiting marine resources. In this paper, we propose a deep learning method for underwater acoustic signal recognition consisting of three steps: Firstly, we extract the underwater acoustic signal's identification vector (i-vector) using a Gaussian mixture model. Secondly, we construct a probabilistic linear discriminant analysis (PLDA) model that divides the i-vector into two segments, Finally, we used a multi-long short-term memory neural network (MLSTMNN) to learn the features, outputting the underwater acoustic signal labels through the softmax layer. Experiments using the ShipsEar dataset can obtain a recognition rate of 84.8%; compared to the traditional Mel frequency Cepstral Coefficients (MFCC) and convolutional neural networks, the recognition rate greatly improved.

Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization

The demand for an indoor localization system is increasing, and related research is also becoming more universal. Previous works on indoor localization systems mainly focus on the acoustic signals in Line of Sight (LOS) scenario to obtain accurate localization information, but their effectiveness in Nonline of Sight (NLOS) scenario remains comparatively untouched. These works are usually less efficient as the acoustic signals often bring diffraction, refraction, scattering, energy decays, and so on in NLOS environments. So the system needs adjusting accordingly in a complex NLOS scenario based on NLOS identification results. Therefore, the identification of NLOS acoustic signal turns out to be significant in the indoor localization system. If the system only uses original support vector machine (SVM) to complete NLOS identification, the result turns out to be poor by our test. To address this challenge, we propose a novel indoor localization system, named ZKLocPro, which utilizes an advanced swarm intelligence method to optimize the traditional SVM classification model to deal with NLOS acoustic signal identification. Its results can help the system adjust the localization process if necessary in a complex NLOS scenario. Obviously, it is also significant to build our own NLOS data set, which is suitable for an indoor localization system’s situation. Specifically, four methods are added: (1) new LOS and NLOS acoustic localization signal sample production, rearrangement, and reselecting process; (2) advanced parameter optimization process; (3) elitist strategy; and (4) inertia weight nonlinear decrement. The experimental result shows that our system is efficient and performs better than state-of-the-art congeneric works even in a complex NLOS scenario.

Cortical auditory evoked potentials with different acoustic stimuli: Evidence of differences and similarities in coding in auditory processing disorders

ObjectivesThe use of cortical auditory evoked potentials allows for the study of the processing of acoustic signals at the cortical level, an important step in the diagnostic evaluation process, and for the monitoring of the therapeutic process associated with auditory processing disorders (APD). The differences and similarities in the acoustic coding between different types of stimuli in the context of APD remain unknown to this date. MethodsA total of 37 children aged between 7 and 11 years, with and without APDs (identified based on verbal and non-verbal tests), all with a suitable intelligence quotient with respect to their chronological age, were assessed. Components P1 and N1 were studied using verbal and non-verbal stimuli. ResultsThe comparison between stimuli in each group revealed that the control group had higher latency and amplitude values for speech stimuli, except for the P1 amplitude, while the group with APDs had different results with respect to the amplitudes of P1 and N1, yielding higher values for speech sounds. The differences between the groups varied according to the type of stimulus: the difference was in amplitude for the verbal stimulus and latency for the non-verbal stimulus. ConclusionThe records of components P1 and N1 revealed that the children with APDs performed the coding underlying the detection and identification of acoustic signals, whether verbal and non-verbal, according to a different pattern than the children in the control group.

Research on Local Sound Field Control Technology Based on Acoustic Metamaterial Triode Structure

Cell photoacoustic detection faces the problem where the strength of the sound wave signal is so weak that it easily gets interfered by other acoustic signals. A sonic triode model based on an artificial periodic structure is designed by COMSOL Multiphysics 5.3a software (Stockholm, Sweden), and software simulations are conducted. Experiments show that when a sound wave with a specific frequency is input by the sound wave triode, it can produce an energy amplification effect on the sound wave signals of the same frequency and a blocking effect on the sound wave signals of other frequencies. This contrast effect is more obvious after increasing the sound pressure intensity of the input sound wave signal. It can effectively filter out interference sound signals. The study of the acoustic triode model provides a new approach for the acquisition and identification of acoustic signals in cell photoacoustic detection, which can significantly improve the working efficiency and accuracy of cell photoacoustic detection.

Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform

Partial discharges (PDs) emit energy in several ways, producing electromagnetic emissions in the form of radio waves, light and heat, and acoustic emissions in the audible and ultra-sonic ranges. These emissions enable us to detect, locate, measure, and analyse PD activity in order to identify faults before the development of failures, because once present, the damage caused by PDs always increases, leading to asset losses, outages, protection-system failure, disaster, and huge energy losses. Therefore, it is of great importance to identify different types of PDs and to assess their severity. This paper investigates the acoustic emissions associated with corona discharge (CD) from different types of sources in the time domain, and based on these it is used to detect, identify, and characterize the acoustic signals due to CD activity. Which usually takes place on polluted glass insulators used in high-voltage transmission lines and hence to differentiate abnormal operating conditions from normal ones. A laboratory experiment was conducted by preparing prototypes of the discharge. This study suggests a feature extraction and classification algorithm for CD classification. A wavelet signal processing toolbox is used to recover the CD acoustic signals by eliminating the noisy portion and to reduce the dimensions of the feature input vector. The proposed model is proven to characterize the PD activity with a high degree of integrity, which is attributed to the effect of the wavelet technique. The test results show that the proposed approach is efficient and reliable.

Identification of Acoustic Signals of Internal Electric Discharges on Glass Insulator under Variable Applied Voltage

<p>A Partial Discharge (PD) is an unwanted phenomenon in electrical equipment. Therefore it is of great importance to identify different types of PD and assess their severity. This paper investigates the acoustic emissions associated with Internal Discharge (ID) from different types of sources in the time-domain. An experimental setup was arranged in the high voltage laboratory, a chamber with an electrode configuration attached to it was connected to a high voltage transformer for generating various types of PD. A laboratory experiment was done by making the models of these discharges. The test equipment including antennas as a means of detection and digital processing techniques for signal analysis were used. Wavelet signal processing was used to recover the internal discharge acoustic signal by eliminating the noises of many natures.</p>

Identification of Acoustic Signals of Internal Electric Discharges on Glass Insulator under Variable Applied Voltage

<p>A Partial Discharge (PD) is an unwanted phenomenon in electrical equipment. Therefore it is of great importance to identify different types of PD and assess their severity. This paper investigates the acoustic emissions associated with Internal Discharge (ID) from different types of sources in the time-domain. An experimental setup was arranged in the high voltage laboratory, a chamber with an electrode configuration attached to it was connected to a high voltage transformer for generating various types of PD. A laboratory experiment was done by making the models of these discharges. The test equipment including antennas as a means of detection and digital processing techniques for signal analysis were used. Wavelet signal processing was used to recover the internal discharge acoustic signal by eliminating the noises of many natures.</p>

Optical observations of meteors generating infrasound—I: Acoustic signal identification and phenomenology

We analyse infrasound signals from 71 bright meteors/fireballs simultaneously detected by video to investigate the phenomenology and characteristics of meteor-generated near-field infrasound (<300km) and shock production. A taxonomy for meteor generated infrasound signal classification has been developed using the time–pressure signal of the infrasound arrivals. Based on the location along the meteor trail where the infrasound signal originates, we find most signals are associated with cylindrical shocks, with about a quarter of events evidencing spherical shocks associated with fragmentation episodes and optical flares. The video data indicate that all events with ray launch angles >117° from the trajectory heading are most likely generated by a spherical shock, while infrasound produced by the meteors with ray launch angles ≤117° can be attributed to both a cylindrical line source and a spherical shock. We find that meteors preferentially produce infrasound toward the end of their trails with a smaller number showing a preference for mid-trail production. Meteors producing multiple infrasound arrivals show a strong infrasound source height skewness to the end of trails and are much more likely to be associated with optical flares. We find that about 1% of all our optically-recorded meteors have associated detected infrasound and estimate that regional meteor infrasound events should occur on the order of once per week and dominate in numbers over infrasound associated with more energetic (but rarer) bolides. While a significant fraction of our meteors generating infrasound (~1/4 of single arrivals) are produced by fragmentation events, we find no instances where acoustic radiation is detectable more than about 60° beyond the ballistic regime at our meteoroid sizes (grams to tens of kilograms) emphasizing the strong anisotropy in acoustic radiation for meteors which are dominated by cylindrical line source geometry, even in the presence of fragmentation.

Speaker recognition analysis using running autocorrelation function parameters

The human brain's process associated with the recognition and identification of acoustic signals is regarded as the calculation of the distances between “sound vectors;“ the ones listened in the present with the memorized vectors in previous listening. Matching vectors (minimal distance between them) would indicate sounds come from similar sound sources or same sound source. In this study, the statistical values of r-ACF (running autocorrelation function) microscopic parameters of 10 spoken words recordings (in Spanish) by the same and different speakers were calculated, and the aforementioned vector's distances were constructed by using the distances between the r-ACF parameters, being able to predict the degree of similarity between the speakers.

Application of Neural Network on Acoustic Signal Identification

The application based on Backpropagation (BP) Algorithm network is conducted on identifying the categories and numbers of mechanical equipments by acoustic signal in battlefield targets. Collected signal was pre-processed and extracted the power spectrum feature of acoustic signal as input vectors of neural networks, then classified by neural networks and pattern recognition theorem. We employ the acoustic signals of six kinds of normal equipments as training samples to train the network. The experiment shows that the ratio of recognition of the acoustic signal processing system based on neural networks proposed is better than the conventional methods.

Location and Identification of Acoustic Signals Recorded in Power Oil Transformers within the Band of 20-180 kHz

Location and Identification of Acoustic Signals Recorded in Power Oil Transformers within the Band of 20-180 kHz

Hearing aid system

The user ( 2 ) of a binaural hearing aid or communication system ( 1 A, 1 B) is to be provided with an easier assignment or identification of acoustic signals generated in the system for keeping a user ( 2 ) informed about current settings or states of the system. To this end the signal is emitted by the hearing aid or communication system ( 1 A, 1 B) in such a way that for the user ( 2 ) the signals appear to come from different signal sources ( 3 to 6; 8 to 11 ) in the space ( 7 ) surrounding the user. In this way the acoustic signals carry additional information perceived consciously or unconsciously by the user ( 2 ).

Application of dynamic synapse neural networks on gunshot recognition

Identification of acoustic signals in noisy environments remains one of the most difficult of signal processing problems, and is a major obstacle to the high degree of accuracy and speed needed to identify suspicious sounds in high-security, high-safety environments. We have previously developed an acoustic recognition capability using a novel, biologically based Dynamic Synapse Neural Network (DSNN) technology. The DSNN-based technology has been demonstrated to classify target sounds with a high degree of accuracy, even in high noise conditions. In this paper we focus on extending the acoustic recognition capability of DSNNs to the problem of gunshot recognition. In order to recognize and localize the event, an array of four microphones is used for sound input. For localization purpose, time-delay estimation algorithm (TDE) is employed for triangulation. We have developed stand-alone, portable, and cost-efficient hardware with which both recognition and localization can be performed. In field-testing, the system classifies and localizes over 90% of the trained-for sounds. Performance for firecracker, starter pistol, 9-mm, and 44-caliber, explosion/firing sounds was also tested.

Rumble IV seamount—no rumble?

Abstract The earlier identification of acoustic signals from the bearing of Rumble IV was mistaken, because there had been a then unrecognised change in the geometry of the acoustic array used to make the detection. A reassessment of the bearing, places the sources of the sounds on the same bearing as Rumble III. Consequently, there is now no evidence of recent volcanic activity from Rumble IV.

Computational noise effects on adaptive filter algorithms

Finite word length arithmetic roundoff noise in adaptive filter algorithms results in statistical variations in the filter weight vector about the infinite precision arithmetic weight vector. These roundoff errors may be modeled as a statistically non stationary driving noise affecting weight mean and covariance convergence. Mean and covariance expressions and bounds are desired for word lengths in fixed-point arithmetic by making use of multiplication roundoff error models. The adaptive filter algorithms consist of the LMS algorithm, the Widrow-Hoff LMS algorithm, pilot-vector algorithm and clipped vector algorithm. All of these algorithms can be implemented on-line and real-time. However, only the behavior of the LMS algorithm is reported here. The implementation of the adaptive filter algorithms in finite word length arithmetic is most evident in minicomputer, microprocessor, and dedicated digital signal processors for on-line real-time signal identification and parameter estimation in many disciplines. Radar signal processing, adaptive beam forming, acoustic signal identification, communication channel enhancement have a definite need for advanced filtering concepts. Our adaptive algorithms are typically employed in these filter configurations. These filters can also be employed in phase distortion equalizers. A particular advantage of these filters is that they can be trained to equalize a variety of distortions. Should a particular distortion scenario change in time, the filters can be made to easily adapt to the new problem.