Signal processor improves Echo’s bass response, loudness, and speech recognition accuracy

Multiband dynamics processing, which separately modifies volume in different frequency bands of an audio signal, is known to improve listeners’ audio experiences. But in the context of voice-controlled systems like the Amazon Echo family of products, it can also improve automatic speech recognition by making echo cancellation easier.

Traditional multiband dynamics processors (MBDPs) have a few drawbacks, however. When splitting a signal into its component frequencies, they don’t always achieve clean separation; and they tend to use fixed frequency bands, which can’t be adjusted to the characteristics of specific audio devices.

Both of these drawbacks can affect the listener’s perception of both the loudness and bass response of an audio signal. They can also cause distortions that make echo cancellation more difficult.

At this year’s International Conference on Acoustics, Speech and Signal Processing, my colleagues and I present a novel MBDP design that addresses both these drawbacks. The technology began shipping in Alexa-enabled devices in 2017, and extensive user testing indicates that it improves listener perception of loudness and bass. In tests, it significantly improved performance on a fundamental speech recognition task. Moreover, the computational complexity of our MBDP system is small.

scrollingwaveformsV2.gif._CB467417779_.gif
Three waveforms: an original audio signal (top); the signal after processing by a conventional MBDP system, with spiky deformations throughout (middle); and the signal after processing by our novel system, which limits the distortion but better preserves shape (bottom).

An MBDP has two main functions: one is compression, or keeping the ratio of a signal’s maximum and minimum volumes within a prescribed range; and the other is peak limiting, or cutting off sudden volume spikes that can cause distortion or even cause the signal from cutting out momentarily, a condition called brownout.

Applying different compressors and limiters to different frequency bands provides greater signal control. But it also depends on filters that can provide clean frequency separation. So the key to our system’s performance is its configurable filter-bank design.

Our filter bank consists of a cascade of filters, all of which or only a few of which may be used at a time. An incoming signal is split in two; half of it passes to two sequential high-pass filters, which filter out frequencies below a cutoff frequency, and the other half passes to two sequential low-pass filters, which filter out frequencies above the same cutoff frequency.

The signal from the high-pass filter may be split again, and again passed to separate banks of high-pass and low-pass filters. This process may repeat an arbitrary number of times, and at each stage, the output of the low-pass filter passes to an “all-pass” filter, which leaves the signal unchanged but enables the synchronization of all the bands. The high-pass and low-pass frequencies may be set to arbitrary values, so that the filtration frequency bands can be tailored to specific applications.

Filterbank_architecture.png._CB467153366_.png
Our proposed reconfigurable filter bank

The signal in each frequency band passes to its own dedicated compressor and then to a limiter. At that point, the frequency-specific signals are recombined and passed to full-band limiter, which ensures that the frequency-specific modifications don’t cause the signal as a whole to distort.

Echo cancellation systems like the one found in Amazon Echo devices subtract a known audio signal — the electrical signal sent to the device’s loudspeaker — from the signal received by the device’s microphones. The more distortion the audio signal suffers, the less it will resemble the reference signal, and the less successful the subtraction will be.

Our MBDP system reduces distortion in three ways. First, the greater precision of the filter bank enables better control of the compression ratios in different frequencies. That means that the system can reduce a loudspeaker’s total harmonic distortion without compromising the overall loudness and bass response of the audio signal.

Similarly, the frequency-specific and full-band peak limiters ensure that the loudspeaker stays in its “linear dynamic range,” meaning that the sound pressure level doesn’t exceed the threshold at which it will begin to cause distortion.

The linear dynamic range is a mechanical property of the loudspeaker. But the electrical signal can become distorted before it even reaches the loudspeaker, if the amplifier attempts to output too high a voltage. This is known as clipping, and the full-band limiter can prevent that, as well.

We conducted extensive listening tests, in which study participants reported that audio processed using our reconfigurable MBDP scheme sounds much better and louder than audio processed using the traditional MBDP scheme. Spectral analyses also demonstrated that our system increases bass response by about five decibels.

FRR_graph.png._CB467153364_.png
Our system (blue line) significantly reduced the rate at which an Echo device falsely rejected Alexa’s wake word (false reject rate, or FRR), as a function of device audio volume.

To evaluate our system’s effect on speech recognition, we tested Echo devices’ responses to Alexa’s wake word — usually “Alexa” — when they were broadcasting audio at a range of volumes. We found that using our MBDP scheme instead of the traditional scheme significantly reduced the number of false rejects, or instances in which the Echo failed to recognize the wake word. We also found that the higher the Echo’s output volume, the greater the advantage offered by our approach.

Acknowledgments: Amit S. Chhetri, Carlo Murgia, Philip Hilmes

About the Author
Jun Yang is a senior research scientist in Amazon Devices' Hardware Technology and Architecture group.

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Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!As a Data Scientist on this team, you will:· Solve real-world problems by getting and analyzing large amounts of data, diving deep to identify business insights and opportunities, design simulations and experiments, developing statistical and ML models by tailoring to business needs, and collaborating with Scientists, Engineers, BIE's, and Product Managers.· Write code (Python, R, Scala, etc.) to analyze data and build statistical models to solve specific business problems.· Apply statistical and machine learning knowledge to specific business problems and data.· Build decision-making models and propose solution for the business problem you define.· Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance.· Analyze historical data to identify trends and support optimal decision making.· Formalize assumptions about how our systems are expected to work, create statistical definition of the outlier, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed.· Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.· Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication.Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Job summaryAmazon is investing heavily in building a customer centric, world class advertising business across its many unique audio, video, and display surfaces. We are looking for an Applied Scientist who has a deep passion for building machine-learning solutions in our advertising decision system. In this role, you will be on the cutting edge of developing monetization solutions for Live TV, Connected TV and streaming Audio. These are nascent, high growth areas, where advertising monetization is an important, fully integrated part of the core strategy for each business.Key job responsibilities· Rapidly design, prototype and test machine learning algorithms for optimizing advertising reach, frequency and return on advertising spend· Build systems that extract and process volumes of disparate data using a variety of econometric and machine learning approaches. These systems should be designed to scale with exponential growth in data and run continuously.· Leverage knowledge of advanced software system and algorithm development to build our measurement and optimization engine.· Contribute intellectual property through patent generation.· Functionally decompose complex problems into simple, straight-forward solutions.· Understand system inter-dependencies and limitations as well as analytic inter-dependencies to build efficient solutions.A day in the lifeAs an Applied Scientist, you will be tasked with leading innovations in machine learning algorithms to deliver ads across platforms influencing product features and architectural choices for decision making systems. You will need to work with data scientists to invent elegant metrics and associated measurement models, and develop algorithms that help advertisers test and learn the impact of advertising strategies across channels on these metrics while ensuring a great customer experience.
US, CA, San Francisco
Job summaryHelp us deliver meaningful recommendations, personalized for each of the millions of customer engaging with Amazon Music.The Music Personalization team is responsible for the machine learning models that underly Amazon Music’s recommendations, playlists and stations. We own the development, training and serving of these models. We create personalized recommendation experiences such as You Might Like recommender, New Releases for You recommender and My Discovery Mix playlist.As an Applied Scientist, your work will have a real world impact on the millions of customers using Amazon Music. You will start with formulating product requirements into a scientific problem. You will work closely with engineering to realize your scientific vision.About Amazon MusicImagine being a part of an agile team where your ideas have the potential to reach millions. Picture working on cutting-edge consumer-facing products, where every single team member is a critical voice in the decision-making process. Envision being able to leverage the resources of a Fortune-500 company within the atmosphere of a start-up. Welcome to Amazon Music, where ideas are born and come to life as Amazon Music Unlimited, Prime Music, and so much more.Everyone on our team has a meaningful impact on product features, new directions in music streaming, and customer engagement. We are looking for new team members across a variety of job functions including software engineering/development, marketing, design, ops and more. Come join us as we make history by launching exciting new projects in the coming year.Our team is focused on building a personalized, curated, and seamless music experience. We want to help our customers discover up-and-coming artists, while also having access to their favorite established musicians. We build systems that are distributed on a large scale, spanning our music apps, web player, and voice-forward audio engagement on mobile and Amazon Echo devices, powered by Alexa to support our customer base. Amazon Music offerings are available in countries around the world, and our applications support our mission of delivering music to customers in new and exciting ways that enhance their day-to-day lives.Come innovate with the Amazon Music team!
US, WA, Seattle
Job summaryJoin us in the evolution of Amazon’s consumer business!The Brand Program and Selling Partner Development organization is the growth and development engine for our Store. Our business grows when our Selling Partners (SPs) businesses’ grow. We aspire to make Amazon the best place for Selling Partners to sell and measure our progress towards this goal through a combination of metrics. We make it easy and predictable for Selling Partners to achieve a great Customer Experience. We believe all motivated Selling Partners that provide a great Customer Experience should be able to grow their business on Amazon either guided by unambiguous self-service tools or by a named Account Manager (AM) working from the same set of recommendations and insights. By ensuring that the same set of recommendations and insights are used, we democratize growth, increase predictability and build trust with our SPs. Our front line AMs drive a virtuous flywheel of learning, experimentation, development and scale that accelerates our store and improves the Customer Experience.We have numerous, durable, deep, technical and interesting problems for Sr. Data Scientist to own. We are looking for a Sr. Data Scientist to design and implement deep tools, insights, metrics and models (inclusive of AI) to lead our systems and tools. You will work with a range of strategic functions within the consumer organization. Our broader org. certainly embodies a start-up mentality. You will work closely with data engineers, software developers, product managers to come up with solution end-to-end with cutting edge machine learning and AI technologies.
US, WA, Seattle
Job summaryAmazon AI is looking for world class scientists to join its AI Lab. This group is entrusted with developing core machine learning algorithms for AWS. As a part of the AI Lab you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and explore novel solutions to new problems at scale. You will interact closely with our customers and with the academic community. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.Key job responsibilitiesThe main focus of a scientist on the team is to use and develop cutting edge science to build new features that will delight our customers. Scientists own their features from research to implementation and production.A day in the lifeScientists works closely with our partners in engineering and product as well as directly with customers. They write production quality code to implement new features. Scientists produce customer-obsessed research to drive the development of new features which we publish in top-tier conferences.About the teamWe are a science team focusing on anomaly detection and related fields. We work closely with other science teams in forecasting, recommender systems, and causality. We deliver state-of-the-art ML solutions for our customers and publish our research in top-tier conferences and journals.
US, CA, Santa Monica
Job summaryAre you passionate about building systems that process massive amounts of data? Are you excited by developing and productizing machine learning, deep learning and computer vision algorithms on the edge and/or on the cloud? Do you enjoy working with a huge diversity of engineers, scientists, and user-experience researchers? If so, you have found the right match!The Fashion Innovation tech team at Amazon is working to redefine and build new, exciting experiences for customers shopping for apparel and related fashion products. We own services that enable personalized customer experiences for our customers while pushing the envelope through novel scientific research, advancing the state of art in the field of Computer Vision and Machine Learning. We’re looking for a thought leader and manager to lead the charge with cutting edge research and application of machine learning to address the unique and ambiguous problems in our space. You will manage a team of talented individuals, innovate in a start-up mode, and partner with business teams to deliver customer experiences that disrupt status quo.The ideal candidate is a strong, creative and highly-motivated individual with hands-on experience in leading multiple research and engineering initiatives. You balance technical leadership with strong business judgment to make the right decisions about technology, tools, and methodologies. You excel in translating broader business objective into Machine Learning science formulations, research for potential solutions or invent new solutions for the objective. You strive for simplicity, demonstrate high judgment backed by sound statistical reasoning and robust machine learning models to deliver creative solutions. You manage a team doing independent research to develop non-trivial Machine learning solutions. You will serve as a technical lead on the team and mentor scientists and engineers. You will also actively contribute to Amazon's Intellectual Property through patents and/or external publications.Key job responsibilities· Lead complex projects that design and build computer vision and deep neural network models to solve a customer facing usecase in fashion space.· Collaborate with product managers, program managers, UX researchers/designers, and engineering teams to transform the apparel shopping experience for Amazon customers.· Perform hands-on data analysis, build machine-learning models and communicate the impact to senior management.· Drive continued scientific innovation as a thought leader in the team.· Lead and mentor Applied Scientists and Engineers in the team.A day in the lifeYou will build and lead a diverse team of Applied Scientists and Software development engineers to solve a variety of technical challenges. You will own the technology roadmap that will drive future products and features that directly affect our growth in this critical space. You will partner with Product Managers, UX designers, and Engineering leaders on the development and execution of a technical roadmap that includes Machine Learning (ML), Computer Vision (CV), 3D visualization, and other emerging technologies. You'll interact regularly with senior leadership as you bring your vision to life.About the teamThe Fashion Innovation tech team at Amazon is working to redefine and build new, exciting experiences for customers shopping for apparel and related fashion products. We own services that enable personalized customer experiences for our customers while pushing the envelope through novel scientific research, advancing the state of art in the field of Computer Vision and Machine Learning.
US, WA, Seattle
Job summaryAre you passionate about making real-world impact on the lives of over one million Amazon employees? Do you want to use science and data-driven methods to build Earth’s best employer and safest place to work? If your answers to these questions are “yes”, then come join the Employee Experience (EX) science team within People Experience and Technology (PXT) at Amazon. Through science, research, and technology, the team’s mission is to improve the employee experience for all Amazonians.As a Data Scientist on the EX science team, you will lead key science initiatives and deliver insights that enable Amazon to create a great employee experience. In this role, you will apply advanced analysis techniques and statistical concepts to draw insights from datasets, create intuitive data visualizations, and build scalable machine learning models. You will work closely with other scientists (data, research, and applied scientists), business intelligence engineers, and product managers to obtain relevant datasets and prototype predictive analytic models. You will team up with data engineers and software development engineers to implement data pipeline to productionize your models and review key results with business leaders and stakeholders.We are looking for someone who can work with ambiguous business problems and navigate complex and dynamic business environments. In this role, you will develop, execute, and deliver on detailed technical roadmap that influences across organizations. The successful candidate is able to make complex trade-offs between technical and business requirements and has experience striking a balance between scientific validity and business practicality in their work. They also have the ability to effectively communicate technical results to a business audience in order to influence key leadership decisions.Key job responsibilities• Create innovative, sophisticated analytic models to address critical issues but also meet key business criteria (cost/risk/business impact) and key technical criteria (reliability/validity/predictability).• Apply machine learning techniques and statistical methods to automatically identify trends, patterns and frictions in the employee experience.• Interview stakeholders to incorporate business requirements and inputs from research, science, product, and engineering partners and translate them into concrete requirements for data science projects.• Define and conduct experiments and communicate insights and recommendations to product, engineering, and business teams.• Work with data engineers and software development engineers to deploy models and experiments to production.• A clear passion for making a positive impact on employees' lives through your work.• Identify and advocate for technical options related to machine learning, data mining, and other statistical approaches.• Identify and recommend opportunities to automate systems, tools, and processes.• Ability to work in a highly collaborative environment with peers that have a range of technical aptitudes.
GB, London
The EU Economics team is a central science team working across a variety of topics in the EU Retail business and beyond. We work closely EU business leaders to drive change at Amazon.We focus on solving long-term, ambiguous and challenging problems, while providing advisory support to help solve short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making. Our work is highly collaborative and we regularly partner with EU- and US-based interdisciplinary teams.We are looking for a Senior Economist who is able to provide structure around complex business problems, hone those complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with various science, engineering, operations and analytics teams to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale.If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you.Key job responsibilities· Provide data-driven guidance and recommendations on strategic questions facing the EU Retail leadership· Scope, design and implement version-zero (V0) models and experiments to kickstart new initiatives, thinking, and drive system-level changes across Amazon· Build a long-term research agenda to understand, break down, and tackle the most stubborn and ambiguous business challenges· Influence business leaders and work closely with other scientists at Amazon to deliver measurable progress and change