AmazonScience_EchoBuds_01.jpg
In the second-generation Echo Buds, Amazon engineers were able to produce a device that is 21 percent smaller than the first version, while maintaining costs, through a multitude of innovations and integration of components.

How the second-gen Echo Buds got smaller and better

Take a behind-the-scenes look at the unique challenges the engineering teams faced, and how they used scientific research to drive fundamental innovation.

Notebook computers, tablets, and smartphones get the tech headlines, but these are largely mature products at this point. Smaller, more personal devices are going through a torrent of iteration and innovation.

Bluetooth wireless headphones are a highly competitive category, with products with bare-bones features available for less than $50, and feature-packed devices available at prices ranging all the way up to $400.

The first Amazon Echo Buds appeared in 2019, and, the follow-up second-gen Echo Buds in April 2021. The team at Amazon improved the second-gen earbuds in almost every way. This is a behind-the-scenes look at the unique challenges the engineering teams faced in creating the latest generation Echo Buds, and how they used scientific research to drive fundamental innovation to overcome those challenges.

Ultimately, Amazon’s team was able to a deliver feature-rich product that competes with products at the high end of the price range for $120.

Atif Noori was the principal product manager for both generations of Echo Buds. Reflecting Amazon’s customer focus, he said the process of designing the latest Echo Buds began with understanding the desires of the customer.

“We work backwards from the customers and build out a set of product requirements. From there we work across multiple talented teams to deliver a lovable product,” he said.

What customers want is great audio, a comfortable fit, long battery life, and excellent connectivity with their smartphones. Of course, many of these are in tension with one another. At the high end of the hearables category, customers also want advanced features like noise cancellation and cloud-based voice services like Alexa.

Echo Buds, Glacier White, Outside.jpg
For the second-generation Echo Buds, engineers worked to redesign the main Bluetooth chip and the audio co-processor in such a way that those two components could perform the tasks of five different components in the first-generation device.

Given this catalog of customer expectations, the nugget-sized wireless earbuds are giants of engineering challenges.

Reducing size to improve comfort and fit, while still maintaining connectivity performance, staying under comfortable temperatures limits, and meeting the customer’s battery life expectations with more features, was a challenge, but one the engineering team said they were excited to tackle.   

Milos Jorgovanovic, principal system architect at Amazon Lab 126, says size and cost are the constant constraints. The Amazon engineers were able to produce a device that is 21 percent smaller than the first version Echo Buds, while maintaining costs, through a multitude of innovations and integration of components.

This began with the processors, or, to use the engineers’ lingo, the silicon, which are the heart of the device. To make the device smaller, the engineers needed to reduce the size of the battery. Easily enough done on its own, except the team also needed to do this without reducing the device's battery life.

"And really for that, the key piece is the power consumption of the silicon platform itself," Jorgovanovic said. "At the same time, we are basically trying to offer high-end features at a much lower power consumption and lower cost."

For the second-generation Echo Buds, team worked with manufacturers to redesign the main Bluetooth chip and the audio co-processor in such a way that those two components could perform the tasks of five different components found in the first-generation device.

"We basically cut the power consumption for audio and Alexa processing by at least a factor of two from what it was before," Jorgovanovic said.

We basically cut the power consumption for audio and Alexa processing by at least a factor of two from what it was before.
Milos Jorgovanovic

This was done while simultaneously improving the Alexa’s ability to hear customers speak.

Amazon started the voice category with the original Echo and Alexa launch in November 2014, so it makes sense that the latest Echo Buds would offer seamless Alexa functionality. With Alexa, a user can not only play music and make phone calls, but also set reminders, request information, and in certain cities, plan public transportation routes and get information on the train or bus they're hoping to catch, all while leaving their phone in their pocket.

"If a customer wants to take Alexa on the go, they can do that and have the same experience as they do with an Echo in their home," Noori said. "It's even more than that though. The responses are tailored for when you're on the go. For example, you can ask Alexa to remind you to buy tahini when you arrive at Whole Foods. And in some stores, you can then ask Alexa if tahini is in stock, or ask which aisle the tahini is on, which is pretty awesome.”

Achieving all of that requires not only integration with the cloud, but also a good bit of on-device processing. Jorgovanovic said improvements in the new processor allowed this to be done with less power consumption.

"We put a better digital signal processor in there, but the second, and more important piece, is that this chip was designed so that it allows very aggressive frequency and voltage scaling," he said. "What it means is that if the device is basically sitting in the air, doing very little processing, we are able to lower both the frequency and the voltage on that chip and have the chip consume much less energy."

If the user speaks and, for example, asks a friend, "Hey, Jason, how are you doing?" the device will run a small amount of processing to determine if the user said "Alexa." If the user did say "Alexa," the digital signal processor (DSP) is boosted even further, increasing the voltage, boosting the frequency, and engaging in more complex compute. At that point the device is processing the Alexa event — the information is sent to the cloud and then the response is played when it is received.

"We basically have these levels of processing, and we set the frequency and the voltage on the processor to the adequate level for the amount of processing we need. This is one of the two main things that we've done in gen two to scale down the power consumption,” Jorgovanovic explained. “The second big thing was integrating more functionality into the main Bluetooth SoC [system on a chip] by innovating on the Bluetooth protocol between the two earbuds, which reduced the number of components and power spent on interconnect. Overall, we reduced total device power by more than 35 percent relative to the first Echo Buds, and specific to that DSP processing for audio and Alexa, by at least a factor of two, if not more. And that's just, wow."

Reducing the power of the processors brought another benefit: reduced heat. "Because we pack in so much, we have to factor in heat dissipation," Noori said.  "It was not like you can add on cooling fins or a big heat sink. It required careful simulation and design."

Beyond the silicon, another major constraint in size and cost are the antennas.

Connectivity is a hurdle in wireless Bluetooth headphones because they are partially hidden in the ear. And while ears can block frequencies, the human body is also effective at blocking signals. The user's smartphone needs to connect with one of the earbuds, and then the two earbuds need to send packets to each other, with as little latency as possible.

"That's really important — the synchronization of the two ear buds — because our hearing is very sensitive to this," Jorgovanovic said. "Something like a hundred microseconds of delta between left and right can easily be felt. And, the effect is the user will sense that the audio is not coming from straight ahead, but instead coming from one side or the other."

Balamurugan Shanmugam, senior antenna design engineer, says the connectivity issues are a challenge for all wearable devices.

"This is an inherent physics problem, right? I mean, this is not unique to Amazon. Anyone working on body-worn devices or even looking just at medical devices such as wireless-enabled pacemakers will encounter the same problem," he said.

Shanmugam's challenge was to improve connectivity in a smaller package. His team's first go at the problem developed a solution, but the manufacturing costs were too high. It was time to develop a novel solution.

Just as the engineers were able to reduce the number of processors in the device, they also were able to integrate functions to accommodate a new antenna. The best location for the antenna is in the front center of the device, but that is also where a user expects to tap or use gestures. On the first-generation Echo Bud, the touch sensor and electrostatic discharge (ESD) circuits were utilizing the location an antenna needs to maximize wireless performance. To address that, the engineers invented an integrated antenna design that combines the antenna, touch, and ESD subsystems.

"The newest Echo Bud has integrated antenna, touch, and electrostatic discharge to optimize wireless performance," Shanmugam said.

Noori said that connectivity is among the features that stand out in the latest Echo Buds. "Connectivity is very solid on these devices; I'm definitely proud of the connectivity performance. I think we nailed that."

And there’s more to come.

"I think there's a lot of interesting things that can be done with earbuds that are outside of basic music playback," Noori said. "We’re continuing to innovate on behalf of our customers, and pushing out software updates. Echo Buds will continue to get better and smarter over time."

Get them in black or white with a wired charing case for $119.99 or with a wireless charging case for $139.99.

Related content

US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists 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. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Python (or R, Matlab, or equivalent) is necessary, and experience with SQL would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Virtual Contact Center-WA
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. About the team The Selling Partner Fees team owns the end-to-end fees experience for two million active third party sellers. We own the fee strategy, fee seller experience, fee accuracy and integrity, fee science and analytics, and we provide scalable technology to monetize all services available to third-party sellers. Within the Science team, our goal is to understand the impact of changing fees on Seller (supply) and Customers (demand) behavior (e.g. price changes, advertising strategy changes, introducing new selection etc.) as well as using this information to optimize our fee structure and maximizing our long term profitability.
US, WA, Seattle
This is a unique opportunity to build technology and science that millions of people will use every day. Are you excited about working on large scale Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL)? We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that makes shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products. As Amazon expands to new geographies, we are faced with the unique challenge of maintaining the bar on Search Quality due to the diversity in user preferences, multilingual search and data scarcity in new locales. We are looking for an applied researcher to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching (e.g. is a drone the same as quadcopter?), relevance ranking (what is a "funny halloween costume"?), language identification (did the customer just switch to their mother tongue?), machine translation (猫の餌を注文する). This is a highly visible role with a huge impact on Amazon customers and business. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. You will work with leaders to develop a strategic vision and long term plans to improve search globally. We are growing our collaborative group of engineers and applied scientists by expanding into new areas. This is a position on Global Search Quality team in Seattle Washington. We are moving fast to change the way Amazon search works. Together with a multi-disciplinary team you will work on building solutions with NLP/ML/DL at its core. Along the way, you’ll learn a ton, have fun and make a positive impact on millions of people. Come and join us as we invent new ways to delight Amazon customers.
US, WA, Seattle
This is a unique opportunity to build technology and science that millions of people will use every day. Are you excited about working on large scale Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL)? We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that makes shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products. As Amazon expands to new geographies, we are faced with the unique challenge of maintaining the bar on Search Quality due to the diversity in user preferences, multilingual search and data scarcity in new locales. We are looking for an applied researcher to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching (e.g. is a drone the same as quadcopter?), relevance ranking (what is a "funny halloween costume"?), language identification (did the customer just switch to their mother tongue?), machine translation (猫の餌を注文する). This is a highly visible role with a huge impact on Amazon customers and business. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. You will work with leaders to develop a strategic vision and long term plans to improve search globally. We are growing our collaborative group of engineers and applied scientists by expanding into new areas. This is a position on Global Search Quality team in Seattle Washington. We are moving fast to change the way Amazon search works. Together with a multi-disciplinary team you will work on building solutions with NLP/ML/DL at its core. Along the way, you’ll learn a ton, have fun and make a positive impact on millions of people. Come and join us as we invent new ways to delight Amazon customers.
US, WA, Seattle
The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon’s on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon’s goods and services are aligned with Amazon’s corporate goals. We are seeking an experienced high-energy Economist to help envision, design and build the next generation of retail pricing capabilities. You will work at the intersection of economic theory, statistical inference, and machine learning to design new methods and pricing strategies to deliver game changing value to our customers. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities Amazon’s Pricing Science and Research team is seeking an Economist to help envision, design and build the next generation of pricing capabilities behind Amazon’s on-line retail business. As an economist on our team, you will work at the intersection of economic theory, statistical inference, and machine learning to design new methods and pricing strategies with the potential to deliver game changing value to our customers. This is an opportunity for a high-energy individual to work with our unprecedented retail data to bring cutting edge research into real world applications, and communicate the insights we produce to our leadership. This position is perfect for someone who has a deep and broad analytic background and is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis. We are particularly interested in candidates with research background in applied microeconomics, econometrics, statistical inference and/or finance. A day in the life Discussions with business partners, as well as product managers and tech leaders to understand the business problem. Brainstorming with other scientists and economists to design the right model for the problem in hand. Present the results and new ideas for existing or forward looking problems to leadership. Deep dive into the data. Modeling and creating working prototypes. Analyze the results and review with partners. Partnering with other scientists for research problems. About the team The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon’s on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon’s goods and services are aligned with Amazon’s corporate goals.
US, CA, San Francisco
The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon's on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon's goods and services are aligned with Amazon's corporate goals. We are seeking an experienced high-energy Economist to help envision, design and build the next generation of retail pricing capabilities. You will work at the intersection of statistical inference, experimentation design, economic theory and machine learning to design new methods and pricing strategies for assessing pricing innovations. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities Amazon's Pricing Science and Research team is seeking an Economist to help envision, design and build the next generation of pricing capabilities behind Amazon's on-line retail business. As an economist on our team, you will will have the opportunity to work with our unprecedented retail data to bring cutting edge research into real world applications, and communicate the insights we produce to our leadership. This position is perfect for someone who has a deep and broad analytic background and is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis. We are particularly interested in candidates with research background in experimentation design, applied microeconomics, econometrics, statistical inference and/or finance. A day in the life Discussions with business partners, as well as product managers and tech leaders to understand the business problem. Brainstorming with other scientists and economists to design the right model for the problem in hand. Present the results and new ideas for existing or forward looking problems to leadership. Deep dive into the data. Modeling and creating working prototypes. Analyze the results and review with partners. Partnering with other scientists for research problems. About the team The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon's on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon's goods and services are aligned with Amazon's corporate goals.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US
The Amazon Supply Chain Optimization Technology (SCOT) organization is looking for an Intern in Economics to work on exciting and challenging problems related to Amazon's worldwide inventory planning. SCOT provides unique opportunities to both create and see the direct impact of your work on billions of dollars’ worth of inventory, in one of the world’s most advanced supply chains, and at massive scale. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. We are looking for a PhD candidate with exposure to Program Evaluation/Causal Inference. Knowledge of econometrics and Stata/R/or Python is necessary, and experience with SQL, Hadoop, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
The Selling Partner Fees team owns the end-to-end fees experience for two million active third party sellers. We own the fee strategy, fee seller experience, fee accuracy and integrity, fee science and analytics, and we provide scalable technology to monetize all services available to third-party sellers. We are looking for an Intern Economist with excellent coding skills to design and develop rigorous models to assess the causal impact of fees on third party sellers’ behavior and business performance. As a Science Intern, you will have access to large datasets with billions of transactions and will translate ambiguous fee related business problems into rigorous scientific models. You will work on real world problems which will help to inform strategic direction and have the opportunity to make an impact for both Amazon and our Selling Partners.