<p>The purpose of this thesis is to develop software to stream input and output audio from a large number of computers in a network to one specific computer in the same network. This computer will save the audio to disk. The audio that is to be saved will consist mostly of spoken communication. The saved audio is to be used in a framework for modeling and visualization.</p><p>There are three major problems involved in designing a software to fill this purpose: recording both input and output audio at the same time, efficiently receiving multiple audio-streams at once and designing an interface where finding and organizing the computers to record audio from is easy.</p><p>The software developed to solve these problems consists of two parts; a server and a client. The server captures the input (microphone) and output (speaker) audio from a computer. To capture the output and input audio simultaneously an external application named Virtual Audio Cable (VAC) is used. The client connects to multiple servers and receives the captured audio. Each one of the client’s server-connections is handled by its own thread. To make it easy to find available servers an Automatic Server Discovery System has been developed. To simplify the organization of the servers they are displayed in a tree-view specifically designed for this purpose.</p>

The purpose of this thesis is to develop software to stream input and output audio from a large number of computers in a network to one specific computer in the same network. This computer will save the audio to disk. The audio that is to be saved will consist mostly of spoken communication. The saved audio is to be used in a framework for modeling and visualization. There are three major problems involved in designing a software to fill this purpose: recording both input and output audio at the same time, efficiently receiving multiple audio-streams at once and designing an interface where finding and organizing the computers to record audio from is easy. The software developed to solve these problems consists of two parts; a server and a client. The server captures the input (microphone) and output (speaker) audio from a computer. To capture the output and input audio simultaneously an external application named Virtual Audio Cable (VAC) is used. The client connects to multiple servers and receives the captured audio. Each one of the client’s server-connections is handled by its own thread. To make it easy to find available servers an Automatic Server Discovery System has been developed. To simplify the organization of the servers they are displayed in a tree-view specifically designed for this purpose.

The Internet of Things (IoT) is a concept that revolves around ordinary devices that are connected to the internet for extended control and ease of use. Altran, a company dealing in high technology and innovation consultancy, predicts a large growth in business opportunities in the IoT area in the coming years, and therefore wants to invest in knowledge about the Internet of Things. Altran wanted a report that described popular wireless IoT communication technologies along with a proposal for a general IoT communication platform or base that could be used to implement many of these technologies. Additionally, an audio streaming application were to be implemented on the proposed platform to validate its credibility. The project resulted in a report on 6 different wireless IoT technologies: Z-wave, ZigBee, Thread, Bluetooth, 802.11n, and 802.11ah. A hardware and software base was proposed that could implement 4 of 6 of these technologies. This base was the Raspberry Pi 2 along with the Raspbian Jessie operating system. Finally an audio streaming system that could stream data to a set of smart Speaker nodes over wireless links based on IEEE 802.11n was implemented on the proposed base.

This thesis deals with design and implementation of multi-channel audio signal streaming via local network. The aim was to choose suitable processor for the transmission of up to 32 channels of audio signal, which will be used for implementation. Also the low-latency ASIO driver was studied and was used to provide timestamps for synchronization. The transmission protocol was designed for signaling and for the real time stream. The actual transmission is provided by TCP protocol. In conclusion, I created the application for development board with choosen MCU. Finally whole solution was tested with real data.

Bluetooth Low Energy (BLE) is a widely used low-energy version of Bluetooth’swireless protocol. To meet increasing requirements of modern wireless audio devices,Bluetooth LE Audio was released with its new Low Complexity CommunicationsCodec (LC3) being much more data efficient than its predecessor Low Complexity SubBand Coding. Because of its increased data efficiency, LC3 opens the door of exploring usage ofvarious physical layer configurations, especially those with lower data rates. Thedifference in performance when streaming audio with the uncoded LE 2M and 1Mconfigurations, compared to using the LE coded S=2 and S=8 configurations (whichhave a lower throughput) points to a research gap which this thesis aims to fill. To be able to gather data necessary to fill the identified gap, multiple iterations of bothsoftware and hardware artefacts were made. The produced artefacts were designed torun the same Bluetooth version (LE Audio) and switch between the physical layerconfigurations. Throughput and current consumption in varied ranges was measuredthrough usage of the artefacts. The results from the experiments show that for energy optimization, an adaptive schemewould not be beneficial over only using LE 2M. However, an adaptive scheme for thephysical layer can be used for LE Audio to improve range and stability. This doeshowever, come with the cost of increased energy consumption.

Orientador: Max Henrique Machado Costa / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-10T00:12:12Z (GMT). No. of bitstreams: 1 GoncalvesNeto_Jahyr_M.pdf: 950657 bytes, checksum: f62691d16e5db013d1b8a9c4e4a32c88 (MD5) Previous issue date: 2007 / Resumo: Nesta dissertação apresentamos o desenvolvimento de uma plataforma multimídia baseada no modelo cliente-servidor voltada para aplicações de streaming de áudio e vídeo. Essa plataforma deverá evoluir para um sistema de videoconferência em um projeto futuro. A plataforma permite a comunicação de áudio, vídeo e texto a partir de um ponto (o servidor) para vários outros pontos (os clientes). Uma das inovações do projeto está no desenvolvimento em Python, que é uma linguagem interpretada, orientada a objetos e dinamicamente tipada / Abstract: This dissertation presents the development of a client-server platform designed initially for audio and video streaming applications. This platform will evolve into a videoconference system as part of a future project. The platform allows audio, video and text communication from a point (the server) to several others points (the clients). One of the project innovations is the implementation Python Language, which is an interpreted, objectoriented and dynamically typed language / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica

In today’s business climate permeated by Big Data, an opportunity to drive performance lies in analysing consumer behaviour from user data. In particular for online content providers, user data is available in abundance and logged continuously. This leads to new possibilities for design and usage of metrics, as businesses can benefit from smart and timely decision-making. However, in order to profit from user data in performance measurement (PM), it is critical to identify metrics that truly guide decisions. Thus, an effective and efficient PM process is imperative. Despite its promise, Big Data’s role in PM has been scarcely researched. Research has studied user behaviour from data, for instance in the context of video or audio streaming and web search, but primarily with a focus on technical performance. In addition, the research on online content providers’ PM is fragmented, and has mainly been conducted by practitioners. Thus, the PM field needs to be updated to reflect today’s dynamic and digital business climate. Therefore, the purpose of this research was to explore how online content providers, generating a large amount of user data, work with PM, and also practically illustrate how metrics can be designed from user data. The research was carried out as a case study at an audio streaming company, but empirics was also gathered from other online content providers with the aim to increase the generalisability. The illustration of metric design was based on quantitative analysis of commuters’ in-car audio streaming. For commuters’ audio streaming it was found that suitable metrics should capture the habitual nature. Therefore engagement metrics were found to be applicable, for instance the fraction having sessions both in the morning and afternoon, and the fraction having more than one day commuting with the streaming service per week. In regard to online content providers’ PM process, this research contributes with a proposed framework, which was developed from three existing frameworks; HEART reflected as important measurement dimensions and translation of goals to metrics, OKR which sets the focus in terms of high-level goals, and design-implement-use reflected as the process’ phases. It was found that insights from user data and explicit user feedback are complementary and can arise throughout the whole process, and that mutual communication between data scientists and product managers is crucial. Further, four types of iterations were identified in the process; modifying a metric, designing new metrics, completely changing a metric, and starting new initiatives. Moreover, metrics were found to be highly context dependent. Additionally, four important aspects were identified in metric design; data availability and proxy assessment, characteristics and form of metric, metric trade-offs, and metric movement interpretation. / I dagens affärsklimat genomsyrat av Big Data finns en möjlighet att driva resultat framåt genom analys av kundbeteenden från användardata. I synnerhet för online-tjänsteföretag samlas användardata kontinuerligt och finns tillgänglig i en oerhörd mängd. Detta skapar nya möjligheter för design och användande av mätetal då företag kan utveckla smartare och snabbare beslutsfattande. För att verkligen dra fördel av användardata i prestationsmätning (PM) är det dock kritiskt att identifiera mätetal som faktiskt bistår beslutsfattande, vilket följaktligen kräver en effektiv PM-process. Trots potentialen är forskning på Big Data inom PM begränsad. Studier har analyserat kundbeteenden från användardata, exempelvis i kontexten av strömmad video eller audio och webbsökningar, men primärt med fokus på tjänstens tekniska prestanda. Vidare är forskning på PM hos online-tjänsteföretag fragmenterad, och huvudsakligen genomförd av företag inom industrin. Följaktligen bör fältet aktualiseras för att reflektera dagens digitala och dynamiska affärsklimat. Därför var syftet med denna studie att utforska hur online-tjänsteföretag, som besitter stora mängder användardata, arbetar med PM, men även praktiskt illustrera hur mätetal kan designas från denna data. Studien genomfördes som en fallstudie på ett ljud-strömningsföretag, men empiri insamlades även från andra online-tjänsteföretag med avsikt att öka generaliserbarheten. Den praktiska illustrationen av mätetals-design baserades på en kvantitativ analys av pendlares audio-strömning i bil. För pendlares audio-strömning i bil fann denna studie att lämpliga mätetal bör fånga den vanemässiga aspekten associerad med pendling. Därmed anses mätetal som reflekterar engagemang lämpliga, exempelvis andelen som har sessioner både på förmiddagen och eftermiddagen och andelen som har mer än en dag med pendlar-sessioner i veckan. Gällande PM-processen hos online-tjänsteföretag bidrar denna studie med ett föreslaget ramverk som utvecklades från tre existerande ramverk; HEART som reflekteras i form av viktiga mätetalsdimensioner samt översättning av mål till mätetal, OKR vilket sätter fokus för processen i termer av mål på högre nivå, och designa-implementera-använda som reflekterar processens faser. I studien kom det fram att insikter från användardata och explicit användaråterkoppling kompletterar varandra, och att dessa kan uppkomma under hela processen. Vidare konstaterar denna studie att ömsesidig kommunikation mellan dataforskare och produktchefer är essentiellt. Dessutom identifierades fyra typer av iterationer som kan förekomma vid användning av mätetal; modifiera mätetal, designa nya mätetal, fullständigt förändra mätetal samt påbörja nya initiativ. Därutöver kan studien konstatera att mätetal är högst kontextberoende, och att det finns fyra viktiga aspekter att ta hänsyn till i mätetals-design; data-tillgänglighet och proxy-utvärdering, karaktäristik och form på mätetal, trade-off mellan mätetal, samt tolkning av mätetals-förändringar.