Sound visualization with an acoustic camera

Nowadays, characterization of sound sources is being carried out with acoustic cameras, which allow to obtain entire sound intensity maps overlaid on digital images of the scenarios under study at once, as opposed to individual sound probes. This work explains the process of building such a device, from the design of the microphone array to the integration of the required software layers...Hoy en día, para la caracterización de fuentes de sonido se emplean cámaras acústicas, que permiten obtener mapas de intensidad sobrepuestos en imágenes digitales de escenarios analizados. Este trabajo explica el proceso de construir tal dispositivo, desde el diseño del arreglo de micrófonos hasta la integración de las capas de software requeridas..

The life of a New York City noise sensor network

Noise pollution is one of the topmost quality of life issues for urban residents in the United States. Continued exposure to high levels of noise has proven effects on health, including acute effects such as sleep disruption, and long-term effects such as hypertension, heart disease, and hearing loss. To investigate and ultimately aid in the mitigation of urban noise, a network of 55 sensor nodes has been deployed across New York City for over two years, collecting sound pressure level (SPL) and audio data. This network has cumulatively amassed over 75 years of calibrated, high-resolution SPL measurements and 35 years of audio data. In addition, high frequency telemetry data has been collected that provides an indication of a sensors' health. This telemetry data was analyzed over an 18 month period across 31 of the sensors. It has been used to develop a prototype model for pre-failure detection which has the ability to identify sensors in a prefail state 69.1% of the time. The entire network infrastructure is outlined, including the operation of the sensors, followed by an analysis of its data yield and the development of the fault detection approach and the future system integration plans for this.Comment: This article belongs to the Section Intelligent Sensors, 24 pages, 15 figures, 3 tables, 45 reference

Visualizing Interior And Exterior Jet Aircraft Noise

In today\u27s competitive aerospace industry, the quest for quiet has drawn significant attention to both the interior and exterior design of an airplane. Understanding the noise generation mechanisms of a jet aircraft is a crucial first step toward developing the most cost-effective noise and vibrations abatement methods. In this investigation, the Helmholtz Equation Least Squares (HELS) based nearfield acoustic holography will be used to understand noise transmission caused by jet engine and turbulence into the fuselage of a jet aircraft cruising at 30,000 ft. Modern propulsive jet engines produce exterior noise sources with a high amplitude noise field and complicated characteristics, which makes them very difficult to characterize. In particular, there are turbulent eddies that are moving through the jet at high speeds along the jet boundary. These turbulent eddies in the shear layer produce a directional and frequency dependent noise. The original HELS approach assumes a spherical source at the origin and computes the acoustic field based on spherical emission from this source. This assumption of one source at the origin is not sufficient to characterize a complex source like a jet. As such, a modified HELS approach is introduced that will help improve the source characterization as it is not dependent on a single source at the origin but a number of virtual sources throughout the space. Custom microphones are created to take acoustic pressure measurements around the jet engine. These measured acoustic pressures are then taken as input to the modified HELS algorithm to visualize the noise pattern of a subsonic jet engine

Development of a Vibroacoustic Noise Prediction Model for Multi-Layered Concentric Cylinders Under Electromagnetic Forced Vibration

Vibracoustic noise prediction models for electrically excited cylinders are used to predict the noise emissions for operating dry-type air-core reactors. These reactors are used to limit current and regulate voltage in electrical transmission and distribution grids. During operation, these reactors produce unwanted, electrically induced noise which is created by forced vibration due to the generated magnetic field from the electrical load being applied to the coil. The reactors designed with complex constructions having multiple winding coils will produce greater amounts of structure-borne sound. Given that these dynamically behave as multiple layers of concentric cylinders, cylindrical vibration theory can be used to predict their behaviour. The goal of this research was to construct and validate an innovative vibracoustic prediction model that accurately represents the mechanisms of the structure-borne noise generation of the reactor to accurately predict the noise emission levels during the design phase.For the Trench Limited Coil Operations, having the ability to accurately predict the noise produced by a reactor in the early design stage is critical to maintain a competitive edge in the competitive reactor market by ensuring that acoustic specifications are met. A review of the literature has shown very little work has been done to develop the science to accurately predict the noise generation for complex reactor construction with multiple winding coil packages. Also, the validation process for the current models do not consider a large frequency range and various electrical excitation frequencies. The novelty of this research is the construction of a cylindrical vibroacoustic noise prediction model for complex reactors of multiple winding packages in conjunction with the validation across a wide range of electrical excitation frequencies.In this dissertation, a detailed test and literature review is simultaneously presented in order to guide the development of an improved vibroacoustic model and to validate the noise prediction outcomes. A comprehensive literature review found various vibroacoustic models have been developed to represent the vibrational excitation of the reactor cylinder, and in turn compute the output noise emissions. Comprehensive noise and vibration testing of two prototype reactors with induced electrical excitation was conducted using CPB, FFT, directivity, noise source identification (NSI) and Modal analysis. From these analyses, the construction of the model was guided by considering the natural structural modes. In addition, a bank of noise emission data for validation of the proposed models was complied. Through the validation process of comparing the proposed vibroacoustic models with the collected reactor noise data, a recommended method for noise prediction was developed. The models coined the Cylindrical Vibroacoustic Model (both single and multiple layered models) were deemed to be the most effective and accurate method for reactor noise prediction. The methodology considers the cylindrical construction of the reactor with multiple layers of concentric cylinders and has been validated over a large electrical excitation frequency range. The outcome of this more versatile vibroacoustic model is the ability to better predict the noise emissions for complex reactor constructions having multiple winding coil packages

Exploring Dimensionality Reduction Effects in Mixed Reality for Analyzing Tinnitus Patient Data

In the context of big data analytics, gaining insights into high-dimensional data sets can be properly achieved, inter alia, by the use of visual analytics. Current developments in the field of immersive analytics, mainly driven by the improvements of smart glasses and virtual reality headsets, are one enabler to enhance user-friendly and interactive ways for data analytics. Along this trend, more and more fields in the medical domain crave for this type of technology to analyze medical data in a new way. In this work, a mixed-reality prototype is presented that shall help tinnitus researchers and clinicians to analyze patient data more efficiently. In particular, the prototype simplifies the analysis on a high-dimensional real-world tinnitus patient data set by the use of dimensionality reduction effects. The latter is represented by resulting clusters, which are identified through the density of particles, while information loss is denoted as the remaining covered variance. Technically, the graphical interface of the prototype includes a correlation coefficient graph, a plot for the information loss, and a 3D particle system. Furthermore, the prototype provides a voice recognition feature to select or deselect relevant data variables by its users. Moreover, based on a machine learning library, the prototype aims at reducing the computational resources on the used smart glasses. Finally, in practical sessions, we demonstrated the prototype to clinicians and they reported that such a tool may be very helpful to analyze patient data on one hand. On the other, such system is welcome to educate unexperienced clinicians in a better way. Altogether, the presented tool may constitute a promising direction for the medical as well as other domains

Towards a Synesthesia Laboratory: Real-time Localization and Visualization of a Sound Source for Virtual Reality Applications

In this paper, we present our findings related to the problem of localization and visualization of a sound source placed in the same room as the listener. The particular effect that we aim to investigate is called synesthesia—the act of experiencing one sense modality as another, e.g., a person may vividly experience flashes of colors when listening to a series of sounds. Towards that end, we apply a series of recently developed methods for detecting sound source in a three-dimensional space around the listener.We also apply a Kalman filter to smooth out the perceived motion. Further, we transform the audio signal into a series of visual shapes, such that the size of each shape is determined by theloudness of the sound source, and its color is determined by the dominant spectral component of the sound. The developed prototype is verified in real time. The prototype configuration is described and some initial experimental results are reported and discussed. Some ideas for further development are also presented

Towards a Synesthesia Laboratory: Real-time Localization and Visualization of a Sound Source for Virtual Reality Applications

In this paper, we present our findings related to the problem of localization and visualization of a sound source placed in the same room as the listener. The particular effect that we aim to investigate is called synesthesia—the act of experiencing one sense modality as another, e.g., a person may vividly experience flashes of colors when listening to a series of sounds. Towards that end, we apply a series of recently developed methods for detecting sound source in a three-dimensional space around the listener.We also apply a Kalman filter to smooth out the perceived motion. Further, we transform the audio signal into a series of visual shapes, such that the size of each shape is determined by theloudness of the sound source, and its color is determined by the dominant spectral component of the sound. The developed prototype is verified in real time. The prototype configuration is described and some initial experimental results are reported and discussed. Some ideas for further development are also presented

Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks

Sensors are present in various forms all around the world such as mobile phones, surveillance cameras, smart televisions, intelligent refrigerators and blood pressure monitors. Usually, most of the sensors are a part of some other system with similar sensors that compose a network. One of such networks is composed of millions of sensors connect to the Internet which is called Internet of things (IoT). With the advances in wireless communication technologies, multimedia sensors and their networks are expected to be major components in IoT. Many studies have already been done on wireless multimedia sensor networks in diverse domains like fire detection, city surveillance, early warning systems, etc. All those applications position sensor nodes and collect their data for a long time period with real-time data flow, which is considered as big data. Big data may be structured or unstructured and needs to be stored for further processing and analyzing. Analyzing multimedia big data is a challenging task requiring a high-level modeling to efficiently extract valuable information/knowledge from data. In this study, we propose a big database model based on graph database model for handling data generated by wireless multimedia sensor networks. We introduce a simulator to generate synthetic data and store and query big data using graph model as a big database. For this purpose, we evaluate the well-known graph-based NoSQL databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a number of query experiments on our implemented simulator to show that which database system(s) for surveillance in wireless multimedia sensor networks is efficient and scalable