Promotional image for 'AI lullaby,' featuring Endel Grimes
Endel, a Berlin, Germany-based provider of personalized sound environments, recently released an updated and streamlined skill for Alexa that includes "AI Lullaby", a soundscape with vocals, music, and voiceovers by Grimes (whose name is now c).
Credit: Endel

The science behind Endel's AI-powered soundscapes

Alexa Fund company releases updated and streamlined skill for Alexa that includes "AI Lullaby" soundscape with vocals, music, and voiceovers by Grimes.

(Editor’s Note: This is the first of a series of articles we’ll be doing related to the science behind products and services from companies in which Amazon has invested. In this instance, we’re focusing on Endel, a Berlin, Germany-based provider of personalized sound environments to help users focus and relax. The Amazon Alexa Fund first invested in Endel in 2018 and earlier this year participated in their $5 million Series A led by True Ventures.)   

Recently, Endel launched an updated and streamlined Endel skill for Alexa that includes the “molecular mechanisms” soundscape with original vocals, music, and voiceovers by Grimes

The company made majorheadlines earlier this fall when c (the artist’s new lower-case, italicized name, inspired the symbol for the speed of light) released “AI Lullaby”, a scientifically engineered sleep soundscape that’s now available on Alexa. c actually initiated the collaboration with Endel after using the app, and because of her search for sleeping aids for her young son (whose father is her boyfriend, Elon Musk). 

Endel co-founders Oleg Stavitsky  and Dmitry Evgrafov
From the very beginning of the company, Endel co-founders Oleg Stavitsky, CEO, (left), and Dmitry Evgrafov, sound designer, say it has been important for the company to be "rooted in science".
Credit: Vika Bogorodskaya

Endel was founded in 2018 by a team of six. It is now a 30-person operation focused on creating personal artificial intelligence-powered soundscapes that take into account an individual’s immediate conditions. It does this by assessing a person’s current state and generating an appropriate soundscape from components of its sound engine. This process was born out of scientific principles about sound’s effect on the human body and mind.

In time for the release of the updated skill for Alexa, Amazon Science contributor Tyler Hayes spoke with Endel co-founders Oleg Stavitsky (CEO) and Dmitry Evgrafov (sound designer) about how Endel uses a variety of contextual data points to play the right sounds at the right time. 

Q. What are some of the contextual signals you use to provide personalized sounds? 

Stavitsky: Circadian rhythms is one. Each person’s body has a natural, daily rhythm — an internal clock. Even if you can’t explain it exactly, you’ve likely felt the physical or mental changes happening on a daily cycle. Circadian rhythm is a sleep-wake cycle that regulates the secretion of a sleep hormone called melatonin. It repeats every 24 hours and is constantly fine-tuned through natural light levels. Scientists have been observing circadian rhythm for some time now and in2017, the Nobel Prize was awarded to three Americans for their discovery of molecular mechanisms that control the circadian rhythm.

We use these universal rhythms as a baseline for our sound personalization. Everyone’s circadian graph will look different depending on where they live and their sleep habits. We also use signals such as user location and time to estimate natural light levels for further personalization. In addition to the circadian rhythm, we use the ultradian rhythm, a rest-activity cycle that regulates cognitive state, mood, and energy level. It consists of roughly 110-minute energy level loops.

Evgrafov: Curated playlists full of piano or classical guitar may feel relaxing to some people at certain points throughout the day, but those ways of relaxing with music can’t adjust depending on individual factors. If one wants to effectively use these curated playlists for specific tasks, the onus falls on the listener to know the specifics of their circadian and ultradian rhythms. Instead, our app or skill creates a personalized circadian rhythm chart for each listener to target the user’s desired mood through sound. Are you in a natural energy entry slump, but still trying to focus? We adjust accordingly. 

In the case of Alexa, we use local information such as time of day, weather, and the amount of natural light exposure through which we know the circadian rhythm phase. Alexa customers must first create an account with us to utilize the skill, and can learn about our privacy policy. With our iOS app, health data also is a key signal for creating personalized sound. Using a person’s heart rate as a real-time input indicator is one essential tool for soundscape personalization. 

We can use real-time heart rate data from people wearing fitness trackers or smartwatches like Apple Watch, if they’ve agreed to allow access. With access to heart rate data, we can recognize prolonged spikes and adapt the BPM to try to bring the heart rate back to a resting level. If possible, in the future, we would be very interested in providing this kind of personalization with the new Amazon Halo

BPM isn’t the only tool we use to adjust human physiology. One study by Luciano Bernardi looked at how swelling crescendos and deflating decrescendos can affect our physiology. Bernardi found that music with a series of crescendos generally led to increased blood pressure, heart rate, and respiration; while selections with decrescendos typically had the opposite effect.

Another study looking at effects on heart rate variability when exposed to different styles of "relaxing" music found that "new age" music induced a shift in heart rate variability from higher to lower frequencies, independent of a listener’s music preference. These and other studies suggest that music can go beyond evoking emotion to impacting cardiovascular function.

Q: How has music theory informed the types of sound your Alexa skill produces? 

Evgrafov: For music composition, we first used the pentatonic scale, a set of notes ordered by pitch or frequency, because of its popularity across modern music.

Listeners may also notice that the AI-powered soundscapes are often very simple. Using less complex tones, melodies, and movement helps ease the burden on our minds. We started with simple ratios of two tonal frequencies like octaves, 2:1, or a perfect fifth, 3:2, because those are pleasing to the brain. A new model suggests music is found to be pleasing when it triggers a rhythmically consistent pattern in certain auditory neurons.

We try to reduce brain fatigue in other ways, too. While complex song structures and unique melodies may sound nice, they force our brains to work a little harder to make sense of them. This auditory experience creates alertness in listeners. Sometimes that’s the goal of the listener, but not always. It can be difficult to determine if a song uses complex or simple elements, especially without musical training. That’s why one piece of classical music might not lull listeners into a state of relaxation in the way others do.

We employ models to determine which sounds are best suited for relaxation and which are best suited for alertness and focus. Relaxation is best facilitated with mellow tones, slow chord changes, and simple structures. Our brains are constantly analyzing sound and the less detail there is, the less attention is dedicated to that task. This helps facilitate relaxation quicker and for longer periods.

The sounds that we find most calming are also linked to our biology. Research by Dr. Lee Salk dating back to the 1960s showed how infants exposed to a heart rate of 72 bpm at 85db overwhelmingly appeared happier. They cry less and put on weight easier. Studies continue to show how lower frequencies and bass can be calming.

Q. What are your plans for evolving your soundscapes, and how will science play a role in the evolution of Endel? 

Stavitsky: To effectively personalize sound through time and tone, we have based our soundscapes on the scientific principles that Dmitry has described above. To validate and take our research-based soundscapes further, we have consulted many experts. 

For example, in the initial stages of figuring out how helpful Endel could be for people, we contacted Mihaly Csikszentmihalyi, author of the bookFlow. Csikszentmihalyi designed his own survey methodology while writing the book to figure out whether people were “in flow” — a focused mental state conducive to productivity. We adapted Csikszentmihalyi’s survey to be interactive inside the app. Listeners were continually asked about their feelings, state of being, and mood to improve the effectiveness of the sounds.

Sleep scientist Dr. Roy Raymann ofSleepScore Labs has been instrumental in helping us create soundscapes to naturally facilitate sleep. The latest advancement includes incorporating a sleep onset period. To do this, the same jingle or sounds are played around the same time each night to trigger the body into a restful phase.

We use broadband noises, those from a wide range of frequencies, because broadband sound administration has also shown to reduce sleep onset latency. Further into the sleep cycle, Endel incorporates nature sounds such as waves to resemble human breathing because hearing breathing-like sounds can help lull people into sleep.

We also have partnered with Germany’s largest scientific institution to study the effect of colored noises on concentration in a workspace environment, and we’re working with a brain wave analysis company for a validation experiment. The study will monitor brain activity of participants listening to Endel, popular streaming music playlists, and silence, to compare the effectiveness at achieving the state of flow.

As a team, we’re rapidly evolving to incorporate the latest data to help listeners with their goals. One example: we’re currently exploring sound masking, which will lead to new ways of listening across varied environments. But other types of sounds and scenarios informed by real-time listener data are in the works, too.

Our unique ability to adapt to every individual and creative, multidisciplinary approach are our magic potion. The scientific principles and research incorporated into the platform are what make Endel so powerful.

View from space of a connected network around planet Earth representing the Internet of Things.
Sign up for our newsletter

Research areas

US, Virtual
Job summaryHow do you manage inventory when you don’t own it? How do you design and provide right incentives for millions of sellers that inbound and ship billions of customer orders? How do you optimize Amazon’s third-party supply chain using new ideas never implemented at this scale to benefit millions of customers worldwide? If these type of questions get your mind racing, we want to hear from you.Supply Chain Optimization Technologies (SCOT) optimizes Amazon’s global supply chain end to end and build systems to deliver billions of products to our customers’ doorsteps faster every year while saving hundreds of millions of dollars using science, machine learning, and scalable distributed software on the Cloud. FBA is an Amazon service for our marketplace third party sellers, where our sellers leverage our world-class facilities and provide customers Prime delivery promise on all their goods. SCOT has launched a new team called Fulfillment by Amazon (FBA) Automation & Optimization to focus on optimizing our third-party supply chain, and is in search to hire a Principal Economist.Key job responsibilities· Design and develop rigorous models to understand and assess third party sellers’ behaviors and experience, including causal impact of various Amazon inventory policies on their short-term and long-term performance.· Design and conduct experiments to validate theories and improve understanding of Amazon’s third party ecosystem.· Collaborate with product managers, scientists, and software developers to incorporate models into production processes and influence senior leaders.· Own the scientific vision and direction related to FBA Sellers.· Own all development phases of economic modeling, including defining key research questions, recommending measures, working with multiple data sources, evaluating methodology and design, executing analysis plans, and interpreting and communicating results· Effectively communicate econometric models to business teams and incorporate feedback into project analysis/modeling.About the teamSellers are a critical part of Amazon’s ecosystem to deliver on our vision of offering the Earth’s largest selection and lowest prices. Fulfillment By Amazon (FBA) enables Sellers to provide fast and efficient deliver to their customers using Amazon fulfillment services. In 2020, Sellers enjoyed strong growth using FBA shipping more than half of all products offered on Amazon. To our consumers, FBA provides a broad and diverse inventory of products from Books, Electronics and Apparel to Consumables and beyond with many of them available with 1-Day shipping. The FBA Inventory team within the Amazon Supply Chain Optimization Technology (SCOT) organization is in charge of defining and delivering fulfillment services to our Sellers by leveraging Amazon’s expertise in machine learning, inventory optimization, big data, and distributed systems to deliver the best inventory management experiences for our FBA Sellers. We work full stack, from foundational backend systems to future-forward user interfaces. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making.
US, CA, Palo Alto
Job summaryAmazon is the 4th most popular site in the US (http://www.alexa.com/topsites/countries/US). Our product search engine is one of the most heavily used services in the world, indexes billions of products, and serves hundreds of millions of customers world-wide. We are working on a new AI-first initiative to re-architect and reinvent the way we do search through the use of extremely large scale next-generation deep learning techniques. Our goal is to make step function improvements in the use of advanced Machine Learning (ML) on very large scale datasets, specifically through the use of aggressive systems engineering and hardware accelerators. This is a rare opportunity to develop cutting edge ML solutions and apply them to a problem of this magnitude. Some exciting questions that we expect to answer over the next few years include:· Can a focus on compilers and custom hardware help us accelerate model training and reduce hardware costs?· Can combining supervised multi-task training with unsupervised training help us to improve model accuracy?· Can we transfer our knowledge of the customer to every language and every locale ?This is a unique opportunity to get in on the ground floor, shape, and build the next-generation of Amazon Search. We are looking for exceptional scientists and ML engineers who are passionate about innovation and impact, and want to work in a team with a startup culture within a larger organization.Please visit https://www.amazon.science for more information
US, CA, Palo Alto
Job summaryAmazon is the 4th most popular site in the US (http://www.alexa.com/topsites/countries/US). Our product search engine is one of the most heavily used services in the world, indexes billions of products, and serves hundreds of millions of customers world-wide. We are working on a new AI-first initiative to re-architect and reinvent the way we do search through the use of extremely large scale next-generation deep learning techniques. Our goal is to make step function improvements in the use of advanced Machine Learning (ML) on very large scale datasets, specifically through the use of aggressive systems engineering and hardware accelerators. This is a rare opportunity to develop cutting edge ML solutions and apply them to a problem of this magnitude. Some exciting questions that we expect to answer over the next few years include:· Can a focus on compilers and custom hardware help us accelerate model training and reduce hardware costs?· Can combining supervised multi-task training with unsupervised training help us to improve model accuracy?· Can we transfer our knowledge of the customer to every language and every locale?· Can we compress an extremely large model to a small model with minimal accuracy loss?This is a unique opportunity to get in on the ground floor, shape, and build the next-generation of Amazon Search. We are looking for exceptional scientists and ML engineers who are passionate about innovation and impact, and want to work in a team with a startup culture within a larger organization.Please visit https://www.amazon.science for more information
US, CA, Sunnyvale
Job summaryAre you seeking an environment where you can drive innovation? Do you want to apply learning techniques and advanced mathematical modeling to solve real world problems? Do you want to play a key role in the future of Amazon's Retail business? Come and join us!Amazon’s Customer Analytics team is looking for Research Scientists, who can work at the intersection of machine learning, statistics and economics; and leverage the power of big data to solve complex problems like long-term causal effect estimation.As a research scientist, you will bring statistical modeling and machine learning advancements to analyze data and develop customer-facing solutions in complex industrial settings. You will be working in a fast-paced, cross-disciplinary team of researchers who are leaders in the field. You will take on challenging problems, distill real requirements, and then deliver solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking.Key job responsibilitiesUnderstand and mine the large amount of data, prototype and implement new learning algorithms and prediction techniques to improve long-term causal estimation approaches.Collaborate with product managers and engineering teams to design and implement solutions for Amazon problems
US, Virtual
Job summaryAlexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team builds services and tools through Machine Learning techniques to implement our policies to detect and mitigate sensitive content in across Alexa.We are looking for an experienced Principal Applied Science to build industry-leading technologies in attribute extraction, annotation, and sensitive content detection and interpretation across all languages, modal, and countries. A Principal Applied Scientist will be a tech lead for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP and Computer Vision related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon.Key job responsibilitiesA Principal Applied Scientist should have good understanding of NLP models (e.g. Bi-LSTM, BERT, and other transformer based models) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies.You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you.A day in the lifeYou will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation for worldwide coverage. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, model development, and solution implementation. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership.About the teamThe mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics.The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video.Job responsibilities
US, WA, Virtual Location - Washington
Job summaryVoice-driven AI experiences are finally becoming a reality and Amazon’s Alexa voice cloud service and Echo devices are at the forefront of this latest technology wave. We deliver world-class products on aggressive schedules that are used every day, by people you know, in and about their homes. At the same time, we obsess about customer trust and ensure that we build products in a manner that maintains our high bar for customer privacy. We are looking for a passionate and talented Applied Scientist with experience in delivering production systems based on innovative research. This is a unique opportunity to play a key role in an exciting, fast growing business. You will be working on one of the world's most cutting edge customer experience and technology. You'll design and run experiments, research new algorithms, and find new ways of optimizing customer experience. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally.You should thrive in ambiguous environments that require to find solutions to problems that have not been solved before. You enjoy and succeed in fast paced environments where learning new concepts quickly is a must. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience building large-scale distributed systems to creating reliable, scalable, and high performance products. Your strong communication skills enable you to work effectively with both business and technical partners.You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history. Candidates can work in Arlington, VA OR Seattle, WA.
US, WA, Seattle
Job summaryAre you inspired by building new technologies to benefit customers? Do you dream of being at the forefront of robotics and autonomous system technology? Would you enjoy working in a fast paced, highly collaborative, start-up like environment? If you answered yes to any of these then you've got to check out the Amazon Scout team.We’ve been hard at work developing a new, fully-electric delivery system – Amazon Scout – designed to get packages to customers using autonomous delivery devices. These devices were created by Amazon, are the size of a small cooler, and roll along sidewalks at a walking pace.We developed Amazon Scout at our research and development lab in Seattle, ensuring the devices can safely and efficiently navigate around pets, pedestrians and anything else in their path.The Amazon Scout team shares a passion for innovation using advanced technologies, a love of solving complex challenges, and a desire to impact customers in a meaningful way. We're looking for individuals who like dealing with ambiguity, solving hard, large scale problems, and working in a startup like environment. To learn more about Amazon Scout, check out our Amazon Day One Blog post here: http://amazon.com/scoutAs a Sr. Applied Scientist specializing in Computer Vision, you will combine cutting-edge Deep Learning techniques with classical Computer Vision to create intelligent systems.In this job you will: - Collaborate closely with Robotics scientists and Hardware teams to develop perception systems for Robots.· Take responsibility for technical problem solving, including creatively meeting product objectives and developing best practices.· Interact with teammates in variety of roles to accomplish your goals.· Identify and initiate investigations of new technologies, prototype and test solutions for product features, and design and validate designs that deliver an exceptional user experience.· Recruit, hire and develop other Applied Scientists.You are a person with a commitment to team work, who enjoys working on complex systems, is customer centric, and thrives on the challenge of prototyping new systems that will eventually operate at world-wide scale.
SE, Stockholm
Job summaryCome build the future of entertainment with us.Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching?Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on.We strive to be a fast-moving, creative, and high-impact organization, but we think it is equally important to be collaborative, supporting, and high-trust in the way we work. We want to come to work every day loving not only what we do, but who we have the privilege of working with. Come help us make all of this a reality.Key job responsibilitiesAs part of the Automated Excellence organization, the Automated Reasoning team applies deep and cutting-edge automated reasoning techniques to detect defects automatically in Prime Video’s core systems and device-level code. The tools we build are mission-critical to the software development and release cycle of many Prime Video engineering organizations, and will represent a huge step forward in the sophistication of our approach to automated Quality Assurance. Your work on this team will help us address a new dimension of scale our business faces as we deliver our applications on an ever-expanding set of client devices.A day in the lifeYou will have the opportunity to apply your deep knowledge of automated reasoning techniques, such as static analysis, formal verification, symbolic execution, etc., to concrete problems our product and engineering teams face on a daily basis. You will collaborate with team members to design and deliver enterprise-scale systems that will be used by both internal and external customers. You will have the opportunity to analyse and verify code to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept. You will help set and continuously evolve a culture of innovation and curiosity that helps us find and solve our customers’ biggest problems.About the teamTo help a growing organization quickly deliver more features to Prime Video customers, Prime Video’s Automated Excellence organization is innovating on behalf of our global software development team consisting of thousands of engineers. We build services and utilities that make developer’s lives easier and more productive, and that help them deliver at higher levels of quality.
IE, D, Dublin
Job summaryAre you a MS or PhD student interested in a 2022 Applied Science Internship in the fields of Speech, Robotics, Computer Vision, or Machine Learning/Deep Learning?Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products that improve the lives of people in a meaningful way?If this describes you, come join our research teams at Amazon. As an Applied Science Intern, you will have access to large datasets with billions of images and video to build large-scale machine learning systems. Additionally, you will analyze and model terabytes of text, images, and other types of data to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept.We are looking for smart scientists capable of using a variety of domain expertise combined with machine learning and statistical techniques to invent, design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.
US, VA, Arlington
Job summaryThe AWS Human Resources Operations and Analytics organization is a critical piece of the AWS flywheel. We are the curators of people data for the industry leader in Cloud Computing. As pioneers in this space, we get to answer new and interesting problems in the People Analytics space, always at scale, and across a variety of business and technical leaders. Our data is sourced from a variety of internal and external sources. The work we do enables leaders to continue to make industry shaking decisions with the knowledge that they are doing so based on reliably sourced and responsibly secured data. We own systems and database environments which are built with reliability and security as the foundation on which balances accessibility, speed, scale, and insight generation. Our systems of self-service data today will quickly evolve into self-service insights in 2022 and beyond.Research Scientists on this team have end-to-end range and capabilities. They work closely with stakeholders to define key business needs and deliver on commitments, retrieve and aggregate data from multiple sources, and compile it into a digestible and actionable format. They also gather and use complex data sets across domains, work closely with product managers, and lead the development of key machine learning features from development to deployment in a cross-functional team.The successful candidate will create documents and share findings in line with scientific best practices for both technical and nontechnical audiences and occasionally present research result at internal and external conferences. They will also work closely with Amazon worldwide operations and the People, Experience, Technologies team to define key business objectives, metrics, and data science deliverables, as well as lead the development of key machine learning features from inception to production in an agile development environment.