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 article is the latest installment in a series by Amazon Science delving into the science behind products and services of companies in which Amazon has invested. 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 major headlines 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. 

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 in 2017, the Nobel Prize was awarded to three Americans for their discovery of molecular mechanisms that control the circadian rhythm.

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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. 

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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 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 book Flow. 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 Roy Raymann of SleepScore 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.

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Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for passionate, hard-working, and talented individuals to help us push the envelope of content localization. We are seeking scientists with experience in audio processing, speech/voice AI and machine learning. We work on a broad array of research areas and applications, including but not limited to multimodal machine translation, speech synthesis, speech analysis, and asset quality assessment. Candidates should be prepared to help drive innovation in one or more areas of machine learning, audio processing, and natural language understanding. If you have experience with speech synthesis and foundational models, then that's a huge plus! Key job responsibilities As an Applied Scientist, you should be a strong communicator, able to describe scientifically rigorous work to business stakeholders of varying levels of technical sophistication. You will closely partner with the solution development teams, and should be intensely curious about how the research is moving the needle for business. Strong inter-personal and mentoring skills to develop applied science talent in the team is another important requirement. - Lead research and development of speech and audio generation technology and end-to-end speech-to-speech architecture - Develop audio processing solutions for production environments, including source separation, enhancement, and mixing - Define the research roadmap for your area, identify high-impact problems, and communicate technical direction to senior leadership - Publish research, contribute to the broader scientific community, and bring external advances into production systems A day in the life You might start your morning reviewing experimental results and refining a model architecture before syncing with your engineering partners on integration plans. After lunch, you could be whiteboarding a new approach to a problem your team recently identified, then writing up findings for an internal science review. You will regularly present your work to peers and stakeholders, participate in code and design reviews, and explore emerging research that could unlock new possibilities for your team. About the team Our team is driven by a shared commitment to applying science in ways that create meaningful impact for customers. We value rigorous research, collaborative problem-solving, and a willingness to experiment with new ideas. You will work alongside talented scientists and engineers in an inclusive environment where your contributions shape the direction of our work. We are focused on building solutions that matter at scale, and we are looking for teammates who are energized by that challenge.
US, WA, Seattle
AI assistants are getting genuinely good at remembering individuals: your preferences, your projects, the thread you left open last week. But that memory stops at the edge of one person's usage. It doesn't reach the level at which real work happens, where the knowledge that matters is spread across many people, where one person's decision changes what everyone else should do next, and where nobody has the full picture. We're building AI that operates at that level: a durable, accurate understanding of how a team works, used to make that team measurably faster. We are looking for a Principal Applied Scientist to own the scientific direction of that work. This is a broad, ambiguous, high-leverage charter. The problems span knowledge representation, temporal reasoning, retrieval, agentic behavior, and the measurement science needed to know whether any of it is working. You will not be handed a well-posed problem. You will decide which problems are worth posing. This is a science leadership role, not a solo research role. You will set direction and raise the scientific bar across a team of applied scientists and MLEs, while staying deep enough in the work to prototype an idea yourself and prove it on real data. Key job responsibilities Own the scientific strategy for how organizational knowledge is represented, kept current, and retrieved: extraction, entity resolution, deduplication, graph structure, and retrieval that unifies graph, semantic, keyword, and temporal search. Advance temporal reasoning. Knowledge changes: facts are revised, decisions are reversed, priorities move. Representing what superseded what and when, and preserving the provenance to distinguish confirmed information from inferred information, is among the hardest open problems in this space. Define the science of proactive behavior. When is it right for an AI system to interrupt a human? These are precision-critical problems where a false positive costs far more than a miss, and where the right threshold varies by team and by individual. Lead our measurement science. Build evaluation for completeness and correctness across a multi-component agentic system, converging on a small number of trustworthy primary metrics rather than a sprawl of component scores. Judge honestly when an offline gain is real and when it is an artifact of a sparse dataset. Build the data that doesn't exist. The most valuable phenomena in this domain are also the rarest, which makes naturally occurring examples too scarce to learn from. Design synthetic and simulated data pipelines that generate controlled, realistic scenarios so these capabilities can be developed and tested at all. Own the learning loop. Turn human interaction into usable training signal, and set the direction for how the system improves from explicit feedback in the near term and from passive observation over the longer term. Make the efficiency calls. Decide where frontier models are required and where a smaller domain-tuned model is sufficient, and build the cost and capacity measurement that makes it a data-driven decision rather than an opinion. Raise the bar across the team. Mentor scientists, review designs, publish where the work merits it, and represent the science externally to customers and to the research community. A day in the life You might spend the morning in a design review arguing that a proposed approach won't survive contact with real data, the afternoon writing a prototype yourself to demonstrate the alternative, and the end of the day convincing an engineer that the capability is worth a sprint. Our sequencing is deliberate: try the idea on intuition, validate it on real data by inspection, then measure it, then operationalize it. Scientists here are expected to identify a problem, justify it, recruit others to it, and drive it into production, across whatever parts of the system that requires. Ownership follows the problem, not the org chart. About the team We are a combined science, product, and engineering team building one product together. Scientists own capabilities end to end rather than individual components, because these problems don't decompose cleanly: a single improvement typically touches extraction, storage, and retrieval at once. We invest in the tooling that makes that practical: local full-stack environments and sandboxed realistic data, so a scientist can go from idea to result in seconds rather than waiting on a deployment or on engineering support. The work is grounded in real usage rather than benchmarks alone, which is a rare combination for science this early: real users, real data, real feedback, and a genuinely unsolved research agenda.
US, WA, Seattle
This role sits within Amazon's Automated Reasoning and Formal Verification research horizon. Shape the Future of Cloud Computing. Are you a graduate student passionate about Automated Reasoning and its real-world applications? Join our team of innovators and embark on a journey to revolutionize cloud computing through innovative automated reasoning techniques. Our tools are called billions of times daily, powering the backbone of Amazon's products and services. We are changing the way computer systems are developed and operated, raising the bar for security, durability, availability, and quality. Applied Scientists in Automated Reasoning develop and apply formal methods, automated reasoning techniques, and neurosymbolic approaches to ensure the security, reliability, and correctness of Amazon and AWS services and customer applications. Application areas span cloud infrastructure verification, cryptographic assurance, AI safety, and formal guarantees for generative AI systems. Methods range from interactive theorem proving and constraint solving to neuro-inspired proof search. As an Applied Science Intern, you will have the opportunity to work alongside our scientists and contribute to projects. From distributed proof search and SAT/SMT solvers to program analysis, synthesis, and verification, you will tackle complex challenges at the intersection of theory and practice. Amazon has positions available for Automated Reasoning Applied Science Internships in, but not limited to, Arlington, VA; Boston, MA; New York, NY; Portland, OR; Santa Clara, CA; Seattle, WA; Austin, TX; Cambridge, UK. Key job responsibilities We are particularly interested in candidates with expertise in: Theorem Proving, Boolean Satisfiability Solvers, Bounded Model Checking, Deductive Verification, Programming/Scripting Languages, Abstract Interpretation, Automated Reasoning, Static/Program Analysis, Program Synthesis. Contribute to the design and implementation of algorithms and formal methods for automated reasoning, including constraint solving, model checking, static analysis, theorem proving, and program synthesis, within a guided research framework. Explore and apply generative AI and machine learning techniques to enhance automated reasoning, including learning-based heuristics for search, neural approaches to symbolic reasoning, and methods for verifying the correctness of AI-generated code. Contribute to automated reasoning techniques for generative AI and agentic coding systems, including methods that apply formal guarantees to large language model outputs. Contribute to the scientific community through publications at peer-reviewed conferences and journals. Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. Critically review and validate outputs from AI tools and automated systems. The ideal intern must have the ability to communicate research findings clearly to diverse audiences.