Design and Simulation of an Audio Signal Alerting and Automatic Control System

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

Audio Signal Generator System Based On State Machines

A state machine can make program designing quicker,simpler and more efficient. This paper describes in detail the model for a state machine and the idea for its designing and gives the design process of the state machine through an example of audio signal generator system based on Labview. The result shows that the introduction of the state machine can make complex design processes more clear and the revision of programs easier.

An Efficient Approach for Segmentation, Feature Extraction and Classification of Audio Signals

Due to the presence of non-stationarities and discontinuities in the audio signal, segmentation and classification of audio signal is a really challenging task. Automatic music classification and annotation is still considered as a challenging task due to the difficulty of extracting and selecting the optimal audio features. Hence, this paper proposes an efficient approach for segmentation, feature extraction and classification of audio signals. Enhanced Mel Frequency Cepstral Coefficient (EMFCC)-Enhanced Power Normalized Cepstral Coefficients (EPNCC) based feature extraction is applied for the extraction of features from the audio signal. Then, multi-level classification is done to classify the audio signal as a musical or non-musical signal. The proposed approach achieves better performance in terms of precision, Normalized Mutual Information (NMI), F-score and entropy. The PNN classifier shows high False Rejection Rate (FRR), False Acceptance Rate (FAR), Genuine Acceptance rate (GAR), sensitivity, specificity and accuracy with respect to the number of classes.

A Novel Multichannel Audio Signal Compression Method Based on Tensor Representation and Decomposition

Multichannel audio signal is more difficult to be compressed than mono and stereo ones.A novel multichannel audio signal compression method based on tensor representation and decomposition is proposed in this paper.The multichannel audio is represented with 3-order tensor space and is decomposed into core tensor with three factor matrices in the way of channel,time and frequency.Only the truncated core tensor is transmitted which will be multiplied by the pre-trained factor matrices to reconstruct the original tensor space.Objective and subjective experiments have been done to show a very noticeable compression capability with an acceptable output quality.The novelty of the proposed compression method is that it enables both high compression capability and backward compatibility with limited signal distortion to the hearing.

Determined Reverberant Blind Source Separation of Audio Mixing Signals

Audio signal separation is an open and challenging issue in the classical“Cocktail Party Problem”.Especially in a reverberation environment,the separation of mixed signals is more difficult separated due to the influence of reverberation and echo.To solve the problem,we propose a determined reverberant blind source separation algorithm.The main innovation of the algorithm focuses on the estimation of the mixing matrix.A new cost function is built to obtain the accurate demixing matrix,which shows the gap between the prediction and the actual data.Then,the update rule of the demixing matrix is derived using Newton gradient descent method.The identity matrix is employed as the initial demixing matrix for avoiding local optima problem.Through the real-time iterative update of the demixing matrix,frequency-domain sources are obtained.Then,time-domain sources can be obtained using an inverse short-time Fourier transform.Experi-mental results based on a series of source separation of speech and music mixing signals demonstrate that the proposed algorithm achieves better separation performance than the state-of-the-art methods.In particular,it has much better superiority in the highly reverberant environment.

SCALABLE PERCEPTUAL AUDIO REPRESENTATION WITH AN ADAPTIVE THREE TIME-SCALE SINUSOIDAL SIGNAL MODEL

This work is concerned with the development and optimization of a signal model for scalable perceptual audio coding at low bit rates. A complementary two-part signal model consisting of Sines plus Noise (SN) is described. The paper presents essentially a fundamental enhancement to the sinusoidal modeling component. The enhancement involves an audio signal scheme based on carrying out overlap-add sinusoidal modeling at three successive time scales,large, medium, and small. The sinusoidal modeling is done in an analysis-by-synthesis overlapadd manner across the three scales by using a psychoacoustically weighted matching pursuits.The sinusoidal modeling residual at the first scale is passed to the smaller scales to allow for the modeling of various signal features at appropriate resolutions. This approach greatly helps to correct the pre-echo inherent in the sinusoidal model. This improves the perceptual audio quality upon our previous work of sinusoidal modeling while using the same number of sinusoids. The most obvious application for the SN model is in scalable, high fidelity audio coding and signal modification.

基于ICEEMDAN模糊熵与Bi-LSTM的工业设备健康状态预测

工业设备健康状态关系着工业生产能否正常进行,为此提出一种基于改进自适应噪声完备经验模态分解(ICEEMDAN)和双向长短期记忆网络(Bi-LSTM)的工业设备健康状态预测方法。ICEEMDAN用于将原始音频信号进行分解得到若干个固有模态函数(IMF)分量,通过计算相关系数选取最佳分量组进行信号重构,然后计算重构IMF分量的模糊熵值构造特征向量集合,最后再输入到Bi-LSTM网络进行模型训练和预测。实验结果表明:相较于其他模型,基于ICEEMDAN模糊熵和Bi-LSTM的工业设备健康状态预测方法,能够有效提取音频信号特征,并准确进行健康状态预测。

多路音频传感信号低功耗实时采集方法设计

由于多路音频存在信号混合噪声干扰,以单路无线传感器网络(Wireless Sensor Networks, WSN)节点为基础的采集过程,淡化了不同路线上的噪声特征,以单一阈值为基础降噪,存在采集偏差大、丢包率高等问题,提出基于WSN节点的多路音频信号低功耗实时采集方法。利用五元麦克风声源定位模型,确定音频信号声源的方位、距离和角度等信息,引入传感器节点,引入静音区检测的多路降噪法,通过调整静音区幅度值为0达到对音频信号不同线路的降噪目的;通过计算合适的采样时间点和平均采样频率,实现多路音频信号低功耗采集。在对比实验测试中,所提方法取得的音频信号值最接近实际值,并且丢包率最高仅为2.5%,最高功耗仅为0.62 W,该方法有效提高了信号的准确性。

噪声环境中多轨道数字音频信号降噪方法

为提升多轨道数字音频信号的峰值信噪比,降低噪声,使音频更加清晰,提出噪声环境中多轨道数字音频信号降噪方法。使用二进小波分解多轨道数字音频,将信号分解为不同的频率子带,使得噪声和信号在频率域上分离。通过模极大值计算分解信号的奇异性,由于信号和噪声在相同奇异性时的变化不同,因此能够确定信号中的噪声特点,利用层间相关搜索法找到分解后信号中的噪声并去除,最后使用交替投影法将去除噪声的分解信号重构,得到去除噪声的多轨道数字音频信号。实验结果表明:使用该方法进行降噪后,存在噪声的信号幅值得到了控制,一些单独突出可认定为噪声的信号基本消失;对低峰值信噪比的信号降噪,平均峰值信噪比提升了28 dB。

非平稳强噪声环境中的音频信号端点检测系统

为提高音频信号端点识别能力,设计一种非平稳强噪声环境中的音频信号端点检测系统。构建音频信号端点检测硬件单元,利用预处理单元对音频信号进行预加重、分帧以及加窗处理后,端点检测单元在提取处理音频信号的MFCC倒谱距离特征、频带方差特征的基础上,依据动态阈值估计策略确定恰当阈值;通过双特征参数双门限法来实现对音频信号起止点的确定以及语音帧和非语音帧的分离;利用包络确定延时单元,防止噪声段被错误识别为语音段,避免出现拖尾太长问题。实验结果表明,所设计系统可实现非平稳强噪声环境音频信号端点检测,检测误差满足设定要求。

Reception of infrasound and audio current in derma nerves

Determining the frequency range of derma nerve that responds to audio current is fundamental for the development of skin-hearing technology. Previous studies have shown that the range of derma nerve responding to audio current is 15-15 000 Hz, because audio amplification is not separated from the step-up transformer. Therefore, the present study used a signal generator which directly drives plane electrodes, simplified the original experimental environment for skin-hearing, measured lower limit voltage of frequency for derma nerve receiving pulse current signals, and revealed that the frequency range of human derma nerve response was as wide as 0.1-30 000 Hz. Results demonstrate that human derma nerve receives audio signals and infrasound within a wide frequency range.

基于音频动态特征重组和TCN-Attention的电力变压器故障诊断方法

电力变压器是电力系统的重要电气设备之一,对变压器进行在线故障诊断是降低电力系统运维成本、提高电力系统稳定性的关键措施.基于电力变压器运行时的音频信息,提出音频信号动态特征重组和TCN-Attention模型实现变压器典型故障的精准识别.首先,分析变压器音频信号的SRA、RMS、峭度和裕度特征;然后,根据特征与变压器故障之间的相关性、鲁棒性和时序单调性实现不同特征的加权融合,得到变压器音频信号的综合特征;最后,设计TCN-Attention模型分析变压器音频特征从而实现故障诊断,并基于注意力机制增强音频特征中的重要信息,以提升变压器故障的识别准确率.本文采集了变压器在正常运行、绕组故障和铁芯故障3种状态下的音频信号构成数据集,对所提方法进行验证.实验结果表明,本文方法根据变压器音频信号进行故障诊断的准确率可达90%以上,实现了变压器故障的智能诊断,对保障电力系统稳定运行具有重要意义.

基于CEEMDAN-BO-SVM的选煤设备故障诊断方法

针对选煤设备故障诊断准确率低且所需特征信号难以采集和提取等问题,提出一种基于自适应噪声完备经验模态分解(CEEMDAN)及黑猩猩算法(BO)优化支持向量机(SVM)的音频信号故障诊断方法.对设备的原始音频信号进行CEEMDAN分解后得到一系列本征模态分量(IMF),计算各IMF的峭度值-相关系数,依据筛选准则优选有效IMF分量.提取有效分量的能量系数及波形系数,组成故障诊断特征集,使用BO-SVM进行故障诊断.实验结果表明,本文方法的故障诊断平均准确率为96.8%,在音频信号特征提取及故障诊断领域有一定的优势,具有一定的应用价值.

强噪环境下分频段数字音频信号精细化采集研究

在强噪环境下,为了获取高质量、低失真度的数字音频信号,提出一种强噪环境下分频段数字音频信号精细化采集方法。通过麦克风设备获取音频信号,利用LM4550芯片对其作采样、编码等处理后生成数字音频信号,基于AC-97单元接收数字音频信号,利用频段分割器对其作频带分解,获得互不重叠子频段数字音频信号。采用多窗谱谱减法对其去噪,利用数据通信接口将其传输给LM4550芯片,在完成模拟信号转换后,通过耳机输出,实现数字音频信号精细化采集。实验结果表明,该方法处理后的各子频段数字音频信号有用信息得以完整保留,并提高了信号波形的规整度和规律性,强噪声环境下数字音频信号的PESQ指标达到4.08以上,最大失真度为3.74%。

基于多维度频谱图的钢琴音频信号识别算法

常用的钢琴音频信号识别算法计算代价过高,为此,提出基于多维度频谱图的钢琴音频信号识别算法。选取某一钢琴选段作为识别目标,检测音频信号获得多维度频谱图,针对频谱图简化特征向量矩阵,提取频谱序列作为输入样本,完成目标音频信号识别。实验结果表明:所设计算法的音符基频计算偏差在1%以内,音频信号特征维度压缩水平和BER指数高,整体计算代价小。

声学基础在专业音响行业中的关键作用与应用前瞻

文章通过阐述专业音频行业的背景和发展方向,分析声学基础在音频技术中的重要性,深入了解声学基础对专业音频行业的影响,并探讨其在音频设备设计、声音处理和音频工程中的应用。通过探讨声学基础在专业音频行业中的潜在发展方向,为行业的发展提供理论支持和实践指导。

人工智能在音频信号处理中的应用与挑战

人工智能可以通过智能化的算法和模型处理音频信号,从而实现音频的增强、识别及转换等功能。然而,人工智能在音频处理领域的应用也面临一些挑战。首先从自动语音识别、语音合成、音频去噪与增强、情感识别与音频分析4个方面分析人工智能在音频信号处理中的应用,其次从音频信号的复杂性和多变性、数据获取与标注问题、计算资源与效率问题以及隐私与安全问题4个方面分析人工智能在音频信号处理中面临的挑战,最后深入分析应对挑战的对策。

基于音频比对监测的中波转播台实际应用分析

随着中波转播台技术的发展,音频信号的高质量传输和监测变得至关重要。光电转换技术为信号传输带来高保真和低干扰的优势,但在传输过程中也可能遇到挑战,如介质不均匀性导致的细小干扰。音频比对监测技术能有效监控和保证广播内容的准确性和真实性,利用先进的算法如梅尔频率倒谱系数(Mel Frequency Cepstral Coefficents,MFCC)来分析信号的质量。文章主要分析了音频比对监测的功能、原理及其在中波转播台的应用,以期通过中波转播平台进行音频监测来减少人为错误,降低故障率。

4K高清全媒体转播车音频信号非稳态噪声过滤

4K音频信号噪声呈现复杂状态,在冲击响应下会出现非稳状态,对噪声信号进行识别和标记是一个难点。提出4K高清全媒体转播车音频信号非稳态噪声过滤方法。采用幅频响应特征提取方法对非稳态噪声信号的位置尺度参数进行计算,分割音频信号片段后构建冲击响应函数。在冲击响应函数的支持下,采用粒子滤波完成非稳态噪声特征的提取。根据特征提取结果,对音频信号进行时域内的分段处理。采用维格纳变换分析音频信号解析信号的时频域,构建非稳态噪声的标记函数,完成非稳态噪声的检测。对非稳态噪声进行编码处理后,获得对应的解析信号,完成非稳态噪声的过滤处理。实验结果表明,所提方法能够提高音频信号的信噪比,并且能够在提升非稳态噪声过滤效果的同时,缩短过滤时间,说明所研究的非稳态噪声过滤方法具有较高的现实应用意义。

基于音频多模态研究的信号分析系统设计

通过统计分析NSP模态文件准确定位真实数据,解析WAV模态文件结构、编码特征和影响因素,充分考虑声道数、采样频率、PCM采样位数,实现不同模态格式转换,设计基于音频多模态研究的信号分析系统。实验从文件内码、文件参数信息、信号声波波形和主观听觉感受方面进行综合对比分析,验证该系统的有效性和实用性。