alexaguardimage.jpeg
Jonathan and Kathy, an Orlando-based couple, were able to save their French bulldog, Cooper, and prevent fire damage to their house thanks to a smart mobile alert from Alexa Guard.

The science behind an Amazon Echo feature that helped save a puppy

How acoustic event detection helps Alexa Guard understand what might warrant an alert — and what just might be a microwave beeping.

Jonathan and Kathy, an Orlando-based couple, were out visiting a neighbor a few days before Christmas 2020 when Jonathan got an unusual alert from their Amazon Echo. Using the Alexa App, they dropped in on their Echo device, which allowed them to hear what was happening in their home in real time.

"You could hear things crackling and popping, and the smoke alarm was going off like crazy," Jonathan told About Amazon. He then rushed home. "Upon rolling into the neighborhood, it was very smoky," Jonathan said. "I pulled up into the driveway, opened the garage, and smoke just started billowing out. I went into our house, and more black smoke poured out. It was so thick you couldn't see six inches in front of your face. The only thing I could think of was Cooper."

How an Amazon Echo alert helped save Cooper the dog

Jonathan managed to get Cooper, the couple's French bulldog, from his pen as smoke billowed from the house. The fire department was also able to extinguish the fire and minimize damage. However, neither outcome may have occurred if it weren’t for a Smart Alert mobile notification from Alexa.

The feature that alerted Jonathan is called Alexa Guard, a smart-home capability that relies on acoustic event detection (AED). AED is an emerging field that focuses on training models to detect and process sounds.

“The technology behind Alexa Guard was developed in an effort to augment the utility of Echo devices,” said Angel Calvo, director of software for Alexa Smart Home team.

How Guard works

When set to away mode, Guard is trained to identify sounds related to home security and safety events, like a smoke alarm sounding, and to distinguish those sounds from something more prosaic, like a microwave beeping.

The detection service relies on two models applied in a two-step system, one on the device, another in the cloud.

The first step utilizes a recurrent neural network — a type of deep learning model that uses sequential data or time series data to learn — on the Echo device itself. The on-device detection works by converting the audio input into features that feed into a recurrent neural network (RNN).

The device uses long short-term memory (LSTM) — a type of recurrent neural network that has shown a significant improvement in speech recognition and has high accuracy, “particularly when it’s applied to sequential data,” said Ming Sun, applied science manager for AED. This is particularly important for determining when a specific sound occurred.

The Echo must also occasionally be able to distinguish between multiple sounds at once. Layered over the RNN is a multi-task learning framework that is trained to detect multiple events. These multiple output layers work like branches off the base neural network, each trained to recognize a different event in the captured audio.

This helps Echo devices detect multiple concurrent incidents (those which customers have selected for detection) such as footsteps and glass breaking, for example.

Layering multiple output layers over a single neural network also makes the detection system in Echo devices very scalable; the device can be trained to recognize new sounds with minimal additions.

“Without this design, we would need to update the whole model every time we update one existing sound event or add a new sound event,” Sun said. “Now, we only have to update the output layer for a target existing event, or add a new output layer for a new event.”

When one of the sounds a customer has selected for detection triggers Guard on the Echo device, that audio is then sent to the cloud for the second verification step to confirm the on-device detection. The cloud runs a much more powerful recognition system to filter out false triggers that might be linked to ambient noise around the home, Sun said.

If the validation process confirms the sound is the one that the device is actively monitoring for, the customer gets a notification in their Alexa app along with an audio clip of the detection.

Getting creative to teach Guard sounds

Because home security events are relatively rare — and the data sets for these audio events are quite meager — semi-supervised learning and self-supervised learning have been critical as Sun’s team expands and refines Guard’s capabilities.

“Semi-supervised learning relies on small sets of annotated training data to leverage larger sets of unannotated data,” Sun said. “While self-supervised learning utilizes larger sets of unannotated data with training targets derived from data itself in an unsupervised way — no human annotations.

“Another technique is to detect for a longer time and aggregate events to be more accurate,” Sun said. To improve the accuracy of sounds with repeating patterns, the detectors look for shorter repeating patterns, such as an appliance beeping. This allows Guard to distinguish between that type of repetitive beeping and an alarm, which can run for 30 seconds or longer. Guard can also detect the difference between a smoke alarm and a carbon monoxide alarm, and notify customers of a specific risk.

Since the very beginning, it’s been critical to build accurate models that consume less resources. We apply lots of optimization so that this system can be as small and efficient as possible.
Ming Sun

Guard Plus, a subscription service launched in January, detects sounds that could be an intruder — like footsteps, a door closing, or glass breaking — and can send a Smart Alert mobile notification or plays a siren on the Echo device. Alexa can also notify customers about the sound of smoke alarms or carbon monoxide alarms. Because the ambient sounds in places like dense urban environments or apartment complexes can make this tricky, the team added a feature allowing customers to adjust the sensitivity to accommodate the noise in their home environments.

The limited annotated data the Guard team had access to has also required them to get creative. Glass breaking, for example, is a rare sound, it’s over in two to three seconds, and it varies based on the type of glass. To bolster their data set, the Guard team rented a warehouse and contracted a construction crew to break hundreds of windows: single pane, double pane, different compositions. This allowed the team to build an authentic data set to build the initial model — also called a seed model — before deploying Guard to beta testers.     

All of the strategies Sun’s team employed to optimize the recognition system on Echo devices have minimized the error rate.

This is where the powerful AED models in the cloud — Guard’s second validation step — are so essential. The chances of false alarm are much smaller when audio is processed through both local and cloud systems, Sun said. And, he emphasized, audio is sent to the cloud only after running it through a device-side model to protect privacy.

“Since the very beginning, it’s been critical to build accurate models that consume less resources,” Sun said. “We apply lots of optimization so that this system can be as small and efficient as possible.”

Edge devices like Echo only send data to the cloud when it’s essential. In the case of Guard, that means the majority of the audio data is processed and discarded by the neural network on the device. Only potential triggers make it to the cloud. For those events, customers are able to view, listen, and delete the audio that Guard detects directly from their Guard History in the Alexa app, or from the Alexa Privacy Settings page.

How a team of designers, scientists, developers, and engineers worked together to create a truly unique device in Echo Show 10.

Research areas

Related content

US, WA, Seattle
The Global Media Entertainment Science team uses state of the art economics and machine learning models to provide Amazon’s entertainment businesses guidance on strategically important questions. 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. Key job responsibilities
US, CA, Palo Alto
The Amazon Search team creates powerful, customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Product Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide. The Search Relevance team focuses on several technical areas for improving search quality. In this role, you will invent universally applicable signals and algorithms for training machine-learned ranking models. The relevance improvements you make will help millions of customers discover the products they want from a catalog containing millions of products. You will work on problems such as predicting the popularity of new products, developing new ranking features and algorithms that capture unique characteristics, and analyzing the differences in behavior of different categories of customers. The work will span the whole development pipeline, including data analysis, prototyping, A/B testing, and creating production-level components. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world’s leading Internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California. Please visit https://www.amazon.science for more information
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Do you have a strong machine learning background and want to help build new speech and language technology? Amazon is looking for PhD students who are ready to tackle some of the most interesting research problems on the leading edge of natural language processing. We are hiring in all areas of spoken language understanding: NLP, NLU, ASR, text-to-speech (TTS), and more! A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will develop and implement novel scalable algorithms and modeling techniques to advance the state-of-the-art in technology areas at the intersection of ML, NLP, search, and deep learning. You will work side-by-side with global experts in speech and language to solve challenging groundbreaking research problems on production scale data. The ideal candidate must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon has positions available for Natural Language Processing & Speech Intern positions in multiple locations across the United States. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. Please visit our website to stay updated with the research our teams are working on: https://www.amazon.science/research-areas/conversational-ai-natural-language-processing
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. The Research team at Amazon works passionately to apply cutting-edge advances in technology to solve real-world problems. Do you have a strong machine learning background and want to help build new speech and language technology? Do you welcome the challenge to apply optimization theory into practice through experimentation and invention? Would you love to help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, etc? At Amazon we hire research science interns to work in a number of domains including Operations Research, Optimization, Speech Technologies, Computer Vision, Robotics, and more! As an intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using mathematical programming techniques for complex problems, implement prototypes and work with massive datasets. Amazon has a culture of data-driven decision-making, and the expectation is that analytics are timely, accurate, innovative and actionable. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. The Research team at Amazon works passionately to apply cutting-edge advances in technology to solve real-world problems. Do you have a strong machine learning background and want to help build new speech and language technology? Do you welcome the challenge to apply optimization theory into practice through experimentation and invention? Would you love to help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, etc? At Amazon we hire research science interns to work in a number of domains including Operations Research, Optimization, Speech Technologies, Computer Vision, Robotics, and more! As an intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using mathematical programming techniques for complex problems, implement prototypes and work with massive datasets. Amazon has a culture of data-driven decision-making, and the expectation is that analytics are timely, accurate, innovative and actionable. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
CA, ON, Toronto
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Are you a Masters student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
CA, ON, Toronto
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Are you a PhD student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. We are looking for Masters or PhD students excited about working on Automated Reasoning or Storage System problems at the intersection of theory and practice to drive innovation and provide value for our customers. AWS Automated Reasoning teams deliver tools that are called billions of times daily. Amazon development teams are integrating automated-reasoning tools such as Dafny, P, and SAW into their development processes, raising the bar on the security, durability, availability, and quality of our products. AWS Automated Reasoning teams are changing how computer systems built on top of the cloud are developed and operated. AWS Automated Reasoning teams work in areas including: Distributed proof search, SAT and SMT solvers, Reasoning about distributed systems, Automating regulatory compliance, Program analysis and synthesis, Security and privacy, Cryptography, Static analysis, Property-based testing, Model-checking, Deductive verification, compilation into mainstream programming languages, Automatic test generation, and Static and dynamic methods for concurrent systems. AWS Storage Systems teams manage trillions of objects in storage, retrieving them with predictable low latency, building software that deploys to thousands of hosts, achieving 99.999999999% (you didn’t read that wrong, that’s 11 nines!) durability. AWS storage services grapple with exciting problems at enormous scale. Amazon S3 powers businesses across the globe that make the lives of customers better every day, and forms the backbone for applications at all scales and in all industries ranging from multimedia to genomics. This scale and data diversity requires constant innovation in algorithms, systems and modeling. AWS Storage Systems teams work in areas including: Error-correcting coding and durability modeling, system and distributed system performance optimization and modeling, designing and implementing distributed, multi-tenant systems, formal verification and strong, practical assurances of correctness, bits-IOPS-Watts: the interplay between computation, performance, and energy, data compression - both general-purpose and domain specific, research challenges with storage media, both existing and emerging, and exploring the intersection between storage and quantum technologies. As an Applied Science Intern, you will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment who is comfortable with ambiguity. Amazon believes that scientific innovation is essential to being the world’s most customer-centric company. Our ability to have impact at scale allows us to attract some of the brightest minds in Automated Reasoning and related fields. Our scientists work backwards to produce innovative solutions that delight our customers. Please visit https://www.amazon.science (https://www.amazon.science/) for more information.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. We are looking for PhD students excited about working on Automated Reasoning or Storage System problems at the intersection of theory and practice to drive innovation and provide value for our customers. AWS Automated Reasoning teams deliver tools that are called billions of times daily. Amazon development teams are integrating automated-reasoning tools such as Dafny, P, and SAW into their development processes, raising the bar on the security, durability, availability, and quality of our products. AWS Automated Reasoning teams are changing how computer systems built on top of the cloud are developed and operated. AWS Automated Reasoning teams work in areas including: Distributed proof search, SAT and SMT solvers, Reasoning about distributed systems, Automating regulatory compliance, Program analysis and synthesis, Security and privacy, Cryptography, Static analysis, Property-based testing, Model-checking, Deductive verification, compilation into mainstream programming languages, Automatic test generation, and Static and dynamic methods for concurrent systems. AWS Storage Systems teams manage trillions of objects in storage, retrieving them with predictable low latency, building software that deploys to thousands of hosts, achieving 99.999999999% (you didn’t read that wrong, that’s 11 nines!) durability. AWS storage services grapple with exciting problems at enormous scale. Amazon S3 powers businesses across the globe that make the lives of customers better every day, and forms the backbone for applications at all scales and in all industries ranging from multimedia to genomics. This scale and data diversity requires constant innovation in algorithms, systems and modeling. AWS Storage Systems teams work in areas including: Error-correcting coding and durability modeling, system and distributed system performance optimization and modeling, designing and implementing distributed, multi-tenant systems, formal verification and strong, practical assurances of correctness, bits-IOPS-Watts: the interplay between computation, performance, and energy, data compression - both general-purpose and domain specific, research challenges with storage media, both existing and emerging, and exploring the intersection between storage and quantum technologies. As an Applied Science Intern, you will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment who is comfortable with ambiguity. Amazon believes that scientific innovation is essential to being the world’s most customer-centric company. Our ability to have impact at scale allows us to attract some of the brightest minds in Automated Reasoning and related fields. Our scientists work backwards to produce innovative solutions that delight our customers. Please visit https://www.amazon.science (https://www.amazon.science/) for more information.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, and more! We are combining computer vision, mobile robots, advanced end-of-arm tooling and high-degree of freedom movement to solve real-world problems at huge scale. As an intern, you will help build solutions where visual input helps the customers shop, anticipate technological advances, work with leading edge technology, focus on highly targeted customer use-cases, and launch products that solve problems for Amazon customers. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. You will own the design and development of end-to-end systems and have the opportunity to write technical white papers, create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science