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1T6-510 - Troubleshooting with Sniffer Portable/Sniffer Distributed - Dump Information

Vendor : Network-General
Exam Code : 1T6-510
Exam Name : Troubleshooting with Sniffer Portable/Sniffer Distributed
Questions and Answers : 120 Q & A
Updated On : December 13, 2018
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1T6-510 Questions and Answers

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1T6-510 Troubleshooting with Sniffer Portable/Sniffer Distributed

Study Guide Prepared by Killexams.com Network-General Dumps Experts


Killexams.com 1T6-510 Dumps and Real Questions

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1T6-510 exam Dumps Source : Troubleshooting with Sniffer Portable/Sniffer Distributed

Test Code : 1T6-510
Test Name : Troubleshooting with Sniffer Portable/Sniffer Distributed
Vendor Name : Network-General
Q&A : 120 Real Questions

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Network-General Network-General Troubleshooting with Sniffer

network widely wide-spread Extends Its leadership in community and application performance administration With New performance Enhancements to Sniffer InfiniStream | killexams.com Real Questions and Pass4sure dumps

SAN JOSE, Calif.--(business WIRE)--community general™ corporation, the leading company of IT service Assurance options, today announced the unencumber of three new models of its business-main community analysis solution, the Sniffer® InfiniStream™ platform and the supply of 24x7 subsequent business day fix or aid for the new platform.

the brand new Sniffer InfiniStream models – i430, i530 and i1730 – join the business’s portfolio of network and utility performance administration solutions, with improved device throughput for top performance facts capture and elevated storage capability. moreover, the company introduced it'll offer onsite, next company day help for the new fashions providing a greater easy and positive formulation for troubleshooting and problem decision, minimizing equipment downtime.

The Sniffer InfiniStream product allows for community personnel to function in-depth, retrospective analysis to check network performance, study site visitors developments, isolate anomalies, function deep packet evaluation, and generate precise and customized summary studies. the new systems are architected to satisfy the ever starting to be storage requirements of traumatic enterprise high speed networks, now and sooner or later.

“network familiar continues to differentiate itself out there with the introduction of inventive new technology to its industry main Sniffer structures,” stated Rick Fitz, vice president of product management for community established. “the new platforms permit our consumers to drive down the charge per managed network section whereas concurrently embracing the challenges of providing IT functions from a enterprise-centric factor of view.”

The options present seamless integration with Administrator and Visualizer, offering users with real time health status, more advantageous trend evaluation and potential planning-based mostly reporting.

For greater counsel, please visit: www.networkgeneral.com

About network popular supplier

community prevalent™ is a leading provider of IT management options designed to combine and simplify IT management and troubleshooting throughout IT domains, assuring the start of IT services. The community ordinary portfolio carries imaginative utility options and intelligent home equipment that display screen and manipulate all aspects of the IT infrastructure including network devices, functions, and servers, whereas simultaneously delivering a correlated view of the fitness of the company carrier. community customary’s solutions provide IT authorities with an conclusion-to-conclusion correlated view of the performance and availability of crucial enterprise functions and the underlying network infrastructure. For greater assistance on community accepted, a privately-held company based in San Jose, California, dial +1-408-571-5000 or go to: www.networkgeneral.com.

community regular, company Container, networkDNA, and the community regular logo are registered logos or logos of network universal agency and/or its associates in the u.s. and/or other countries. best network established agency makes Sniffer® company products. All other registered and unregistered logos herein are the only property of their respective owners. ©2006 community accepted employer. ALL RIGHTS RESERVED.


Troubleshooting Connectivity issues | killexams.com Real Questions and Pass4sure dumps

From the writer of 

When a person stories that he or she can't join, the primary issue to do is to find out what message the person is getting. If the message says, Login Failed for person, the consumer obviously forgot his or her password. which you can reset the password with sp_password. The command looks like this:

sp_password NULL, newpassword, loginid

only the equipment administrator can challenge this command.

IMO

The sp_password command should be obtainable to contributors of the protection admin position (Chapter 9 discusses that position in aspect), however is not.

If the consumer is getting messages, reminiscent of particular SQL Server not discovered (named pipes) or conventional network Error (Sockets), you certainly have a connectivity problem. These messages can also be issued if the server is down, so you need to make sure the server is working before doing the rest.

alas, most customer connectivity complications have to be solved at the customer notebook; so until you have got a utility corresponding to pc anywhere or Remotely viable, you are going to need to talk over with the pc of the consumer with the difficulty. which you can are attempting a number of issues if you happen to are on the user's pc.

First, use the client community utility to try the community library the customer is the usage of. This became installed along with client Connectivity.

if you birth the utility, the common tab shows you what the default community library is. a shopper can be install to hook up with multiple server, the usage of distinct community libraries. you are going to see these aliases on the familiar tab as well. If the default network library or the one associated with a specific alias isn't the one which SQL Server is listening on, simply exchange the library. that you would be able to either select a special default, modify an present alias entry, or add a brand new community library and alias. The network Libraries tab indicates you the community libraries purchasable on the customer pc.

You might also should examine that the customer and server are the usage of the equal underlying community protocol. If the server is the usage of TCP/IP and the customer is the use of NetBEUI, as an example, there could be no communication even though the SQL Server netlibs are the same. that you could find the installed network protocols by way of correct-clicking on network local, and then deciding on residences. On a windows 9x laptop, highlight the community adapter and judge properties. On an NT computing device, seem on the counsel proven on the Protocols tab.

if you can't unravel the problem with the client community utility, try the methods described in right here sections.

Troubleshooting Named Pipes Connectivity

on the working device instant, on the client workstation, class here:

web VIEW\\SERVERNAME

This tells you no matter if the computer can see the server. if you get lower back counsel about the server, the computing device can see the server. in case you get an Error 5 – entry Denied, there is an issue with the NT Authentication login. If the user can't see the server at all, you get hold of an Error fifty three. in this case, or if you get some other message, a network difficulty exists and you should still contact the network administrator.

If this succeeds, however you nevertheless can't connect with the SQL Server, make sure to verify the pipe with makepipe and readpipe. regrettably, you need to do a part of this on the server and part of this on the customer. makepipe runs on NT best; readpipe runs on NT, home windows 9x, and windows 3.11. These tools install immediately in case you install SQL Server. they are in the \MSSQL7\BINN listing. There is not any icon for them, and that they don't appear within the beginning menu.

makepipe

You run makepipe on the server with here command:

> makepipe

Upon working this command, you should see output that looks anything like here:

Making PIPE:\pipe\abc study to write down lengthen (seconds):0 awaiting client to join...

At this point, that you may return to the customer workstation and problem the readpipe command.

readpipe

You run readpipe on the client with here command:

> readpipe /Sservername /Dstring

Assuming, for example, that you have run makepipe on a server named MyServer, run right here:

> readpipe /SmyServer /Dhello

If this command is a hit, you'll want to see output that seems like this:

SvrName:\\myserver PIPE :\\myserver\pipe\abc information :howdy information despatched: 1 : hello facts examine: 1 : hiya

if you don't see the facts examine message, you have got a community issue and should contact your network administrator. Press CTRL+C on the server to terminate the makepipe program.

Troubleshooting Sockets Connectivity

To look at various TCP/IP connections, use ping at the customer pc. It takes either a server name or an IP handle, as proven here:

> ping MyServer

or

> ping eleven.11.11.11

be sure to see outcomes that look like this:

Pinging myserver [1.1.1.40] with 32 bytes of facts: Reply from 1.1.1.forty: bytes=32 time<10ms TTL=128 Reply from 1.1.1.forty: bytes=32 time<10ms TTL=128 Reply from 1.1.1.40: bytes=32 time<10ms TTL=128 Reply from 1.1.1.40: bytes=32 time<10ms TTL=128 Ping facts for 1.1.1.40: Packets: sent = 4, got = 4, misplaced = 0 (0% loss), Approximate circular trip times in milliseconds: minimal = 0ms, optimum = 0ms, regular = 0ms

in case you get a message that the request timed out, you have got a network problem and should contact your network administrator.

Troubleshooting ODBC Connectivity

when you've got community connectivity however nevertheless can not hook up with SQL Server, you may also have an issue with ODBC connectivity. that you would be able to use ODBCPING to diagnose these complications. ODBCPING is in the \MSSQL7\BINN directory. There is no icon for it in your birth menu. you can use it to examine a direct ODBC connection to a server and to examine an ODBC statistics supply name (DSN).

To examine an immediate ODBC connection, use the following command:

ODBCPING –Sservername –Uusername –Ppassword

in case you do not get a response, you doubtless should install a newer edition of the ODBC driver.

If this works, you should identify which ODBC DSN the client is using (you could doubtless determine this by using looking on the ODBC applet in control Panel), and then issue here command:

ODBCPING –Ddatasourcename –Uusername –Ppassword

If the direct ODBC connection worked, however this one didn't, you need to check the DSN for correctness. as an example, is does the DSN specify a database the user doesn't have access to or give an fallacious server name? do that with the ODBC applet in control Panel.

ordinary Tip

you can also look at various connectivity to an ODBC statistics supply with the ODBC applet. spotlight the facts source and decide Configure. click on subsequent, after which conclude. On the final monitor, click the verify statistics source button.

If none of these hints help, you could try the use of SQL Profiler (described in Chapter 10) to see what login message is truly attending to SQL Server. equipment such because the NT network display screen and the community widespread Sniffer can also help you diagnose and correct connectivity problems. Ask your network administrator for assist with these last two tools.


Sniffer updates nothing to sneeze at | killexams.com Real Questions and Pass4sure dumps

Andrew R. Hickey, news author

nonetheless reeling from its messy breakup with application maker McAfee Inc., community general Corp. hopes new developments to its Sniffer products proves as soon as and for all that the San Jose, Calif.-primarily based seller is back.

network commonplace introduced a couple of new updates to its commercial enterprise network management and performance evaluation platform this week on the Sniffer user Summit in Miami.

leading the can charge is a brand new incarnation of the business's information catch device, the InfiniStream. edition 2.5 offers precise-time, circulation-to-disk trap of Gigabit and 10/a hundred Ethernet, WAN and Asynchronous switch Mode (ATM) statistics. It additionally makes use of Sniffer decodes and knowledgeable analysis. mixed, the capabilities give what network normal calls "Retrospective evaluation," again-in-time and true-time network and utility evaluation performed by recording and examining traffic over a number of hours, days or weeks. The InfiniStream 2.5 outlets as much as four terabytes of statistics.

The InfiniStream finds the basis reasons of network performance issues and allows for troubleshooting on each topology, including VoIP, spoke of Tom Bienkowski, community time-honored's director of product management. It helps administrators clear up efficiency problems extra instantly devoid of interruption, Bienkowski talked about. InfiniStream 2.5 also integrates with different Sniffer tools, which boosts vogue analysis and capability planning-based mostly reporting.

along with the InfiniStream update, network ordinary the day before today also introduced the Sniffer commercial enterprise Administrator 4.0, which allows for centralized administration, administration and protection of Sniffer elements. It automates basic tactics and permits IT directors to configure a whole bunch of alternatives on one Sniffer agent after which clone the configuration throughout different brokers all over the business. When combined with the tool's ability to immediately download the working equipment and Sniffer updates and patches, the tool ensures security and policy compliance, Bienkowski stated.

The Sniffer enterprise Administrator offers a single-console view throughout dissimilar appliances, enabling single signal-on and administration of all Sniffer instruments from one console, slicing down on time and putting off the need for directors to bodily access the Sniffer instruments.

other announcements made the day gone by by community regularly occurring include:

  • The Sniffer MultiSegment Intelligence 4.0, a device that detects inefficiencies on as much as eight community segments as traffic flows between community aspects. It lets administrators locate where an issue is and determine whether it's a community or utility flaw.
  • The Sniffer business Visualizer four.0, which offers visibility into the fitness and availability of functions, LAN and WAN traffic, and allows rapid tuning of functions. It presents vogue reports with confidence ratings and gives in-depth, end-to-end visibility into clients, purposes and subnets.
  • The Sniffer InfiniStream i120, a line rate, circulate-to-disk device that screens, measures, manages and resolves community problems on the network's edge or in small deployments. It retailers as much as 300 GB of information that community managers can use to analyze network performance and developments.
  • Robert Whiteley, analyst with Cambridge, Mass.-based mostly Forrester research Inc., stated network standard's flow to encompass utility efficiency analysis with its network and protocol analysis line will help for more suitable end-to-end administration of the entire community.

    "It eventually makes it possible for the finger-pointing to stop," he observed. "Now they could definitively say even if or not it's an utility or network difficulty. here's a pretty good function when all is said and achieved."

    Whiteley spoke of bringing network analysis capabilities into the software house is a "natural extension" for community common.

    "it be a administration interface that plugs into a sort of supervisor of managers very seamlessly," he stated.

    Whiteley added: "Kudos to network prevalent for waking up … they are coming back round and asserting, 'Let's exit and make sure not one of the small guys delivery to snip away at us.'"


    1T6-510 Troubleshooting with Sniffer Portable/Sniffer Distributed

    Study Guide Prepared by Killexams.com Network-General Dumps Experts


    Killexams.com 1T6-510 Dumps and Real Questions

    100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



    1T6-510 exam Dumps Source : Troubleshooting with Sniffer Portable/Sniffer Distributed

    Test Code : 1T6-510
    Test Name : Troubleshooting with Sniffer Portable/Sniffer Distributed
    Vendor Name : Network-General
    Q&A : 120 Real Questions

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    Troubleshooting with Sniffer Portable/Sniffer Distributed

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    Netscout Covers Network Monitoring and Analysis | killexams.com real questions and Pass4sure dumps

    WEBINAR:On-Demand

    EUC with HCI: Why It Matters

    NetScout Systems, Inc., headquartered in Westford, Massachusetts has extensive experience in network performance monitoring and analysis, dating back to its founding in 1984. The firm was one of the earliest innovators in remote monitoring (RMON) technologies, developing probes for a number of LAN and WAN technologies, including Fast Ethernet, FDDI, T3 and ATM.

    In early 2000, the company introduced the first end-to-end performance management system for e-business, the nGenius Performance Management System, and also extended its technology into application monitoring, including the first real-time monitoring solution providing simultaneous visibility into converged voice, video and data traffic. In 2002, the company was the first to integrate real-time monitoring, troubleshooting, protocol analysis and historical reporting tasks and data within a single tool that managed performance of all standard, proprietary, web and voice applications. In 2007, the company doubled its business by acquiring Network General, and its well known Sniffer, Infinistream and Network Intelligence product lines, considered by many to be the industry standard for protocol analysis. By adding the analysis power of the Sniffer technologies to the real-time monitoring of the nGenius, NetScout has positioned itself as a company that can provide both network analysis and application performance solutions. NetScout (NASDAQ: NTCT) has over 800 employees and offices around the globe, serving the Global 5000 enterprise community as well as government agencies and telecommunications service providers.

    The NetScout Product Line

    NetScout Systems' product line is divided into two main areas: Instrumentation, with solutions that monitor and record the packet/flow data from strategic locations providing network visibility; and Analysis, which includes software products for intelligent management, analysis and reporting of network and application performance across complex networks.

    The flagship analysis solution is the Sniffer Global, which supports both wired and wireless enterprise network links including 10/100/1000 Ethernet and 802.11 a/b/g/n networks. The Sniffer Global system runs under Windows XP or Vista, and is typically loaded onto a high-end laptop that can be easily attached to a network location where enhanced network analysis and management functions are required. Evolved from the industry-leading Sniffer Portable and incorporating Sniffer Expert analysis capabilities and advanced protocol decodes, this system is able to help users pinpoint and analyze the toughest performance problems with great deployment flexibility. The system is especially suited to wireless environments, as it integrates with the Cisco Systems 3000 Series Mobility Services Engine. This unique combination allows the net manager to address the challenge of effectively troubleshooting performance issues in a wireless network, in which components and problems are constantly in motion and difficult to pinpoint quickly without multiple analyzers and technicians involved.

    Sniffer Global was developed taking into account that tools implemented in today's network environment need to adhere to certain security and compliance policies to ensure that critical corporate data is protected. The system uses a central management and administration server which controls deployment and distribution of the product, as well as controls upgrade activities and license management. The product provides user authentication and controlled access to application functionality such as capture access, capture slice sizing and other features on a per user basis. In addition, it includes activity and audit log capabilities, providing a strong tool for monitoring and controlling usage. It also features industry-leading packet-level expert analysis and decodes, including those for a wide array of databases, VoIP, and mobile technologies. Available in multi-user and enterprise-wide licenses, these server-based controls safeguard internal corporate compliance and governance requirements by restricting use to authorized staff only and providing visibility into users of the technology, when they are using it and how it is being used.

    Another analysis product is the nGenius system, which uses data from strategically deployed probes and collectors to provide visibility into complex networks for application monitoring, packet/flow analysis and troubleshooting, response time analysis, capacity planning and convergence management of voice, video and data traffic. Through the combination of Sniffer and nGenius technologies, NetScout enables customers to maximize the performance and efficiency of applications and content delivered across globally distributed networks.

    The nGenius system is based upon distributed monitoring devices, called the Intelligent Data Sources, which collect key network metrics. The data sources include the nGenius InfiniStream Appliances, deep packet capture appliance that are strategically placed throughout the network to collect packet-flow statistics; the nGenius Probes which are distributed monitoring devices that collect key performance metrics; the nGenius Virtual Agent, a software-based intelligent data source designed to extend the reach of network management inside virtual computing environments; and the nGenius Collector, which delivers NetFlow and IP Service Level Agreement (SLA)-based data to the system. These data sources are placed at strategic aggregation points within the network, to collect key performance metrics, support the monitoring of load-balancing or redundant links, and provide application recognition by tracking complex, Web-based and peer-to-peer applications.

    The nGenius analysis and reporting solution includes four modules. The Performance Manager is a comprehensive solution for application and network performance management, including service and policy validation, plus planning and optimization tools. The K2 Service Delivery Manager leverages application-level data from the Performance Manager to provide a quick status of the health of critical business services, including early warnings of changes in application and network conditions. The Performance Manager for Flows analyzes and maps the NetFlow data and IP SLA results that are gathered from the nGenius Collectors and displays that information on real-time screens and historical reports, including VoIP quality metrics and system utilization statistics. The Analytics for Flows system automatically records information from the nGenius Collectors, looking for utilization anomalies, providing managers with an early warning of potential system or application issues.

    Further details on the NetScout network management solutions can be found at http://netscout.com/. Our next tutorial will continue our examination of vendors' network management architectures.

    Copyright Acknowledgement: ©2009 DigiNet Corporation, All Rights Reserved

    Author's Biography

    Mark A. Miller, P.E. is President of DigiNet Corporation, a Denver-based consulting engineering firm. He is the author of many books on networking technologies, including Voice over IP Technologies, and Internet Technologies Handbook, both published by John Wiley & Sons.

    Mark A. Miller, P.E. is President of DigiNetB. Corporation, a Denver-based consulting engineering firm. He is the author of many books on networking technologies, including Voice over IP Technologies, and Internet Technologies Handbook, both published by John Wiley & Sons.


    A detector you can wear | killexams.com real questions and Pass4sure dumps

    The new MRI detector is much like an elastic bandage and moulds itself to the shape of the body. The little black boxes contain special electronics designed to compensate for changes in detector geometry during the scan. Photo: Institute for Biomedical Engineering, Klaas Prüssmann’s group / ETH Zurich

    Swiss scientists from ETH Zurich have developed the first elastic detector for magnetic resonance imaging (MRI). The detector in the form of an elastic bandage moulds itself to the shape of the patient's body, which also enables body parts to be examined in motion. The novel detector provides better images and greater patient comfort during the scan.

    Ever since the early days of magnetic resonance imaging (MRI) around thirty years ago, all technical conditions have had to be as perfect and stable as possible for a successful scan: "You need a strong magnetic field that, until now, has had to be as homogeneous and constant as possible and the scans have to proceed exactly as planned," says Klaas Prüssmann, a professor at the joint Institute for Biomedical Engineering of ETH Zurich and the University of Zurich. "Nowadays, we want to shake off these constraints."

    Not more powerful—more intelligent

    The aim is not only to keep making these devices more powerful, but also more intelligent. In their latest prototypes, Prüssmann says, the magnetic field is allowed to wobble thanks to measurement and control technology that handles even substantial field fluctuations. It should also be possible to examine body parts in motion, which is important to discern what the kneecap does while bending or whether the meniscus is torn partially or fully, for instance.

    Present-day detectors might be robust and well-engineered, but they have two major shortfalls: they do not enable body parts to be scanned in motion and are built in such a way as to fit any patient. This means that the distance from the detector is vast for a small knee, which affects the detectors' sensitivity.

    "It is as if a doctor didn't place his stethoscope directly on the body and tried to listen to the patient from ten centimetres away," says Prüssmann. For the first time, the ETH-Zurich professor and his team have now developed a detector that is both stretchable and elastic and can mould itself to the individual body.

    MRI Images of a knee. In order to obtain these images, the newly designed detector was used. Photos: Institute for Biomedical Engineering, Klaas Prüssmann’s group / ETH Zurich

    An MRI scan of the knee, for instance, basically requires two things: a strong magnet and an antenna that emits radio waves and can serve as a detector. The latter surrounds the patient's knee while in the tube that builds up the magnetic field.

    Detector like an elastic bandage

    The detectors that record the signals are based on a resonant circuit. The largest module is a conductor loop with high conductivity. Making this both stretchable and elastic so that it returns to its original shape after stretching was a challenge for the scientists. Due to the high conductivity required, besides copper only gold and silver came into question, but they were too soft and not elastic enough.

    Jurek Massner, one of Prüssmann's doctoral students, solved the problem as part of his dissertation. He sewed—as a conductor loop—fine copper braids onto a fabric blend of cotton and polyamide, which helped provide the necessary elasticity and stretchability. His detector resembles an elastic bandage.

    The scientists had to make several prototypes until they found the ideal combination of conductor loop and support material. The electronic properties had to be distributed optimally in the conductor-loop braiding to provide the right resistance, inductance and capacitance there. In doing so, it became apparent that especially thin braiding had the best properties.

    Eliminating interference with sophisticated electronics

    The elastic detector's electronics presented a further challenge. When the detector is stretched out, the electrical conditions change and therefore so does the kind of signal. In order to be tolerant of such changes, Massner devised a special adjustment network and preamplifier to offset this "interference".

    Until ten years ago, people mostly only worked with one detector in MRIs. Nowadays, as many receivers as possible are used in unison. Because every detector is in a slightly different position, they provide different perspectives of the same object. By exploiting these in the coding and signal processing, the imaging can be rendered considerably more rapid.

    As a result, for instance, three-dimensional datasets with resolutions of up to 200 micrometres can be obtained. In the latest MRI machines with up to thirty-two detectors, the measuring time can be slashed to an eighth of what it used to be. Without speeding it up, such a scan would take over an hour.

    Anyone who would like to find out more about the research conducted by Klaas Prüssmann and his team and even take a peek inside the MRI lab will have the opportunity to do so at Scientifica.

    Explore further: MRI/PET scanner combo

    More information: Nordmeyer-Massner JA, De Zanche N, Pruessmann KP: Stretchable coil arrays: application to knee imaging under varying flexion angles. Magnetic Resonance in Medicine (2012), 67, 872-879, doi: 10.1002/mrm.23240


    Federated Learning for Wake Word Detection | killexams.com real questions and Pass4sure dumps

    by David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrech and Joseph Dureau

    We propose a practical approach based on Federated Learning to solve out-of-domain issues encountered with continuously running embedded speech-based models such as wake word detectors. We conduct an extensive empirical study of the federated averaging algorithm for the “Hey Snips’’ wake word based on a crowdsourced dataset that mimics a federation of wake word users. We empirically demonstrate that using an adaptive averaging strategy inspired from Adam in place of standard weighted model averaging highly reduces the number of communication rounds required to reach our target performance. The associated upstream communication costs per user are estimated at 8 MB, which is reasonable in the context of smart home voice assistants. Additionally, the dataset used for these experiments is open sourced with the aim of fostering further transparent research in the application of federated learning to speech data.

    This blog post is based on our original paper, that we slightly modified for the purpose of readability. If you are interested in getting the “Hey Snips” dataset, here are the instructions !

    Introduction

    Wake word detection is used to start an interaction with a voice assistant. The challenge at stake is a specific case of keyword spotting (KWS): it involves continuously listening to an audio stream to detect a predefined keyword or set of keywords. Well-known examples of wake words include Apple’s “Hey Siri’’ or Google’s “OK Google’’. Once the wake word is detected, voice input processed by a spoken language understanding engine, powering the perception abilities of the voice assistant [2].

    Wake word detectors usually run on device in an always-on fashion, which brings two major difficulties.

  • First, it should run with minimal memory footprint and computational cost. The resource constraints for our wake word detector are 200k parameters (based on the medium-sized model proposed in [3]), and 20 MFLOPS.
  • Secondly, the wake word detector should behave consistently in any usage setting, and show robustness to background noise. The audio signal is highly sensitive to recording proximity (close or far field), recording hardware, but also to the room configuration. Robustness also implies a strong speaker variability coverage (genders, accents, etc.). While the use of digital signal processing front-ends can help mitigate issues related to bad recording conditions, speaker variability remains a major challenge. High accuracy is all the more important since the model can be triggered at any time: it is therefore expected to capture most of the commands (high recall, or low false rejection rate) while not triggering unintentionally (low false alarm rate).
  • Today, wake word detectors are typically trained on datasets collected in real usage setting e.g. users homes in the case of voice assistants. Speech data being by nature very sensitive, centralized collection raises major privacy concerns. In this work, we investigate the use of federated learning (FL) [4] in the context of an embedded wake word detector. FL is a decentralized optimization procedure that enables to train a central model on the local data of many users without the need to ever upload this data to a central server. The training workload is moved towards the user’s devices which perform training steps on the local data. Local updates from users are then averaged by a parameter server in order to create a global model.

    Related work

    Most research around decentralized learning has historically been done in the context of a highly controlled cluster/data center setting, e.g. with a dataset evenly partitioned in an i.i.d fashion. The multi-core and multi-gpu distributed training setting has been specifically studied in the context of speech recognition in [5]. Efforts on decentralized training with highly distributed, unbalanced and non-i.i.d data is relatively recent, as the foundations were laid down in [4] with the introduction of the federated averaging (FedAvg) algorithm and its application to a set of computer vision (MNIST, CIFAR-10) and language modeling tasks (applied to the Shakespeare and Google Plus posts datasets). There are for now very few real life experiments that we know of — except for Google’s keyboard Gboard for Android [6] and more recently Mozilla’s URL suggestion bar [7]. To our knowledge, the present work is the first experiment of its kind on user-specific speech data.

    The federated optimization problem in the context of convex objective functions has been studied in [8]. The authors proposed a stochastic variance-reduced gradient descent optimization procedure SVRG with both local and global per-coordinate gradient scaling to improve convergence. Their global per-coordinate gradient averaging strategy relies on a sparsity measure of the given coordinate in users local datasets and is only applicable in the context of sparse linear-in-the-features models. The latter assumption does not hold in the context of neural networks for speech-based applications.

    Several improvements to the initial FedAvg algorithm have been suggested with a focus on client selection [9], budget-constrained optimization [10] and upload cost reduction for clients [11]. A dynamic model averaging strategy robust to concept drift based on a local model divergence criterion was recently introduced in [12]. While these contributions present efficient strategies to reduce the communication costs inherent to federated optimization, the present work is as far as we know the first one introducing a dynamic per-coordinate gradient update in place of the global averaging step.

    The next section describes the federated optimization procedure, and how its global averaging can be substituted by an adaptive averaging rule inspired from Adam. It is followed by the experiments section, where both the open-sourced crowdsourced data and model used to train our wake word detector are introduced. Results come next, and a communication cost analysis is provided. Finally, the next steps towards training a wake word detector on user data that is really decentralized are described.

    Federated Optimization

    We consider the standard supervised learning objective function f(w) = l(x_i, y_i, w) that is the loss function for the prediction on example (x_i, y_i) when using a model described by a real-valued parameter vector w of dimension d. In a federated setting, we assume that the datapoints i are partitioned across K users, each user being assigned their own partition P_k, |P_k|= n_k. The optimization objective is therefore the following:

    The FedAvg algorithm introduced in [4] aims at minimizing the objective function (1) assuming a synchronous update scheme and a generic non-convex neural network loss function. The model is initialized with a given architecture on a central parameter server with weights w_0. Once initialized, the parameter server and the user’s devices interact synchronously with each other during communication rounds. A communication round at time t in [1, ..,T] is described below:

  • The central model w_{t-1} is shared with a subset of users S_t that are randomly selected from the pool of K users given a participation ratio C.
  • Each user k in S_t performs one or several training steps on their local data based on the minimization of their local objective F_k using mini-batch stochastic gradient descent (SGD) with a local learning rate η_{local}. The number of steps performed locally is E*max(ceil(n_k/B),1), n_k being the number of datapoints available locally, E the number of local epochs and B the local batch size.
  • Users from S_t send back their model updates w_{t,k}, k in S_t to the parameter server once local training is finished.
  • The server computes an average model w_t based on the user’s individual updates w_{t,k} , k in S_t, each user’s update being weighted by n_k/n_r, where n_r is the average number of datapoints used at each round and will be considered as a constant n.
  • Algorithm 1: The Federated averaging algorithm [4]

    When B= ∞ (i.e the batch size is equal to the local dataset size) and E=1, then a single gradient update is performed on each user’s data. It is strictly equivalent to doing a single gradient computation on a batch including all of selected user data points. This specific case is called FedSGD, e.g. stochastic gradient descent with each batch being the data of the federation of selected users at a given round. FedAvg (Federated averaging) is the generic case when more than one update is performed locally for each user.

    The global averaging step can be written as follows, using a global update rate η_{global}:

    Setting the global update rate η_{global} to 1 is equivalent to a weighted averaging case without moving average. Equation (2) highlights the parallel between global averaging and a gradient update G_t . This parallel motivates the use of adaptive per-coordinate updates for G_t that have proven successful for centralized deep neural networks optimization such as Adam [13]. Moment-based averaging allows to smooth the averaged model by taking into account the previous rounds updates that were computed on different user subsets. We conjecture that the exponentially-decayed first and second order moments perform the same kind of regularization that occurs in the mini-batch gradient descent setting, where Adam has proven to be successful on a wide range of tasks with various neural-network based architectures. In this work, we set the exponential decay rates for the moment estimates to β_{1}=0.9 and β_{2}=0.999 and ε= 10^-8 as initially suggested by the authors of [13].

    Experiments Datasets

    Unlike generic speech recognition tasks, there is no reference dataset for wake word detection. The reference dataset for multi-class keyword spotting is the speech command dataset [14], but the speech command task is generally preceded by a wake word detector and is focused on minimizing the confusion across classes, not robustness to false alarms. We constituted a crowdsourced dataset for the Hey Snips wake word. We are releasing publicly this dataset [1] in the hope it can be useful to the keyword spotting community.

    Table 1: Dataset statistics for the Hey Snips wake word- 18%of utterances are positive, with strong per user imbalance inthe number of utterances (mean: 39, standard dev: 32)

    The data used here was collected from 1.8k contributors that recorded themselves on their device with their own microphone while saying several occurrences of the Hey Snips wake word along with randomly chosen negative sentences. Each recorded audio sample has gone through a validation process, where at least two of three distinct contributors have validated that the pronounced utterance matches the transcript.

    This crowdsourcing-induced data distribution mimicks a real-world non-i.i.d, unbalanced and highly distributed setting, and a parallel is therefore drawn in the following work between a crowdsourcing contributor and a voice assistant user. The statistics about the dataset comforting this analogy are summarized in Table 1. The train, dev and test splits are built purposely using distinct users, 77% of users being used solely for training while the 23% remaining are used for parameter tuning and final evaluation, measuring the generalization power of the model to new users.

    Model

    Acoustic features are generated based on 40-dimensional mel-frequency cepstrum coefficients (MFCC) computed every 10ms over a window of 25ms. The input window consists in 32 stacked frames, symmetrically distributed in left and right contexts. The architecture is a CNN with 5 stacked dilated convolutional layers of increasing dilation rate, followed by two fully-connected layers and a softmax inspired from [15]. The total number of parameters is 190,852. The model is trained using cross-entropy loss on frames prediction. The neural network has 4 output labels, assigned via a custom aligner specialized on the target utterance “Hey Snips’’: “Hey’’, “sni’’, “ps’’, and “filler’’ that accounts for all other cases (silence, noise and other words). A posterior handling [16] generates a confidence score for every frame by combining the smoothed label posteriors. The model triggers if the confidence score reaches a certain threshold 𝛕, defining the operating point that maximizes recall for a certain amount of False Alarms per Hour (FAH). We set the number of false alarms per hour to 5 as a stopping criterion on the dev set. The dev set is a “hard’’ dataset when it comes to false alarms since it belongs to the same domain as data used for training. The model recall is finally evaluated on the test set positive data, while false alarms are computed on both the test set negative data and various background negative audio sets. See results section for further details about evaluation.

    Results

    We conduct an extensive empirical study of the federated averaging algorithm for the Hey Snips wake word based on crowdsourced data from Table 1. Federated optimization results are compared with a standard setting e.g. centralized mini-batch SGD with data from train set users being randomly shuffled. Our aim is to evaluate the number of communication rounds that are required in order to reach our stopping criterion on the dev set. For the purpose of this experiment early stopping is evaluated in a centralized fashion, and we assume that the dev set users agreed to share their data with the parameter server. In an actual product setting, early stopping estimation would be run locally on the devices of the dev users, they would download the latest version of the central model at the end of each round and evaluate the early stopping criterion based on prediction scores for their own utterances. These individual metrics would then be averaged by the parameter server to obtain the global model criterion estimation. Final evaluation on test users would be done in the same distributed fashion once training is finished.

    Standard baseline: Our baseline e.g. a standard centralized data setting with a single training server and the Adam optimizer reaches the early stopping target in 400 steps (~2 epochs), which is a strong convergence speedup in comparison with standard SGD that remains under 87% after 28 epochs despite learning rate and gradient clipping tuning on the dev set.

    User parallelism: The higher the ratio of users selected at each round C, the more data is used for distributed local training, and the faster the convergence is expected, assuming that local training does not diverge too much. Figure 1shows the impact of C on convergence: the gain of using half of users is limited with comparison with using 10%, specifically in the later stages of convergence. A fraction of 10% of users per round is also more realistic in a practical setup as selected users have to be online. With lower participation ratios (C=1%), the gradients are much more sensitive and might require the use of learning rate smoothing strategy. C is therefore set to 10%.

    Figure 1: Effect of the share of users involved in each round Con the dev set recall / 5 FAH, FedSGD, Adam global averaging,ηglobal = 0.001, ηlocal = 0.01

    Global averaging: Global adaptive learning rates based on Adam accelerates convergence when compared with standard averaging strategies with or without moving averages. Table 2 summarizes experimental results in the FedSGD setting with optimized local learning rates. Applying standard global averaging yields poor performances even after 400 communication rounds when compared with adaptive per-parameter averaging.

    Table 2: Dev set recall / 5 FAH for various averaging strategiesFedSGD, C = 10%

    Local training: Our results show consistency across local training configurations, with limited improvements coming from increasing the load of local training. The number of communication rounds required to reach the stopping criterion on the dev set ranges between 63 and 112 communication rounds for E in [1,3] and B in [20, 50, ∞], using C=10%, Adam global averaging with η_{global}= 0.001, and a local learning rate of 0.01. In our experiments, the best performances are obtained for E=1 and B=20 for an average of 2.4 local updates per worker taking part in a round, yielding a 80% speedup with comparison to FedSGD. Nevertheless we observed variability across experiments with regard to weight initialization and early stage behaviour. Unlike some experiments presented in [4], the speedup coming from increasing the amount of local training steps does not lead to order of magnitude improvements on convergence speed, while local learning rate and global averaging tuning proved to be crucial in our work. We conjecture that this difference is related to the input semantic variability across users. In the MNIST and CIFAR experiments from [4] the input semantics are the same across emulated users. For instance, images of the 9 digit that are attributed to various users are all very similar. In the wake word setting, each user has their own vocalization of the same wake word utterance with significant differences in pitch and accent than can lead to diverging lower stage representations that might perform poorly when averaged.

    Evaluation: We evaluate the false alarm rates of the best model (E=1 and B=20) for a fixed recall of 95% on the test set. We observe 3.2 FAH on the negative test data, 3.9 FAH on Librispeech [17] , and respectively 0.2 and 0.6 FAH on our internal news and collected TV datasets. Unsurprisingly, false alarms are more common on close-field continuous datasets than they are on background negative audio sets.

    Communication cost analysis

    Communication cost is a strong constraint when learning from decentralized data, especially when user’s devices have limited connectivity and bandwidth. Considering the asymmetrical nature of broadband speeds, the communication bottleneck for federated learning is the updated weights transfer from clients to the parameter server once local training is completed [11]. We assume that the upstream communication cost associated with users involved in model evaluation at each communication round is marginal, as they would only be uploading a few floating point metrics per round that is much smaller than the model size. The total client upload bandwidth requirement is provided in the equation (3):

    Based on our results, this would yield a cost of 8MB per client when the stopping criterion is reached within 100 communication rounds. On its end, the server receives 137 updates per round when C=10%, amounting for 110GB over the course of the whole optimization process with 1.4k users involved during training. This cost is of course directly related to the early stopping criterion. Further experiments with latter convergence stages (400 rounds) yielded 98% recall / 0.5 FAH on the test set for an upload budget of 32 MB per user.

    Conclusion and future work

    In this work, we investigate the use of federated learning on crowdsourced speech data to learn a resource-constrained wake word detector. We show that a revisited Federated Averaging algorithm with per-coordinate averaging based on Adam in place of standard global averaging allows the training to reach a target stopping criterion of 95% recall per 5 FAH within 100 communication rounds on our crowdsourced dataset for an associated upstream communication costs per client of 8MB. We also open source the Hey Snips wake word dataset.

    The next step towards a real-life implementation is to design a system for local data collection and labeling as the wake word task requires data supervision. The frame labeling strategy used in this work relies on an aligner, which cannot be easily embedded. The use of of memory-efficient end-to-end models [1] in place of the presented class-based model could ease labeling as it would only rely on voice activity detection.

    If you liked this article and want to support Snips, please share it!

    Follow us on Twitter: David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, Joseph Dureau and Snips.

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    References

    [1] Alice Coucke, Mohammed Chlieh, Thibault Gisselbrecht, David Leroy, and Thibaut Lavril, “Efficient keyword spotting using dilated convolutions and gating”, 2018.

    [2] Alice Coucke, Alaa Saade, Adrien Ball, Theodore Bluche, Alexandre Caulier, David Leroy, Clement Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Mael Primet, and Joseph Dureau, “Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces”, CoRR, vol. abs/1805.10190, 2018.

    [3] Yundong Zhang, Naveen Suda, Liangzhen Lai, and Vikas Chandra, “Hello edge: Keyword spotting on microcontrollers”, CoRR, vol. abs/1711.07128, 2017.

    [4] H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Aguera y Arcas, “Federated learning of deep networks using model averaging”, CoRR, vol. abs/1602.05629, 2016.

    [5] Daniel Povey, Xiaohui Zhang, and Sanjeev Khudanpur, “Parallel training of deep neural networks with natural gradient and parameter averaging”, CoRR, vol. abs/1410.7455, 2014.

    [6] Brendan McMahan and Daniel Ramage “Federated learning: Collaborative machine learning without centralized training data”, https://ai.googleblog.com/2017/04/ federated-learning-collaborative. html, 2017.

    [7] Florian Hartmann, “Federated learning”, https://florian.github.io/federated-learning/, 2018.

    [8] Jakub Konecny, H. Brendan McMahan, Daniel Ramage, and Peter Richtarik, “Federated optimization: Distributed machine learning for on-device intelligence”, CoRR, vol. abs/1610.02527, 2016.

    [9] Takayuki Nishio and Ryo Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge”, CoRR, vol. abs/1804.08333, 2018.

    [10] Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, and Kevin Chan, “When edge meets learning: Adaptive control for resource-constrained distributed machine learning”, CoRR, vol. abs/1804.05271, 2018.

    [11] Jakub Konecny, H.BrendanMcMahan, FelixX.Yu, Peter Richtarik, Ananda Theertha Suresh, and Dave Bacon, “Federated learning: Strategies for improving communication efficiency”, CoRR, vol. abs/1610.05492, 2016.

    [12] Joachim Sicking, Fabian Hger, Peter Schlicht, Tim Wirtz, Michael Kamp, Linara Adilova and Stefan Wrobel, “Efficient decentralized deep learning by dynamic model averaging”, CoRR, vol. abs/1807.03210, 2018.

    [13] Diederik P. Kingma and Jimmy Ba, “Adam: A method for stochastic optimization”, CoRR, vol. abs/1412.6980, 2014.

    [14] Pete Warden, “Speech commands: a dataset for limited-vocabulary speech recognition”, CoRR, vol. abs/1804.03209, 2018.

    [15] Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur, “A time delay neural network architecture for efficient modeling of long temporal contexts”, in Sixteenth Annual Conference of the International Speech Communication Association, 2015.

    [16] Guoguo Chen, Carolina Parada, and Georg Heigold, “Small-footprint keyword spotting using deep neural networks” in Acoustics, speech and signal processing (icassp), 2014 ieee international conference on. IEEE, 2014, pp. 4087–4091.

    [17] Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur, “Librispeech: An ASR corpus based on public domain audio books” ,in ICASSP. 2015, pp. 5206–5210, IEEE



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