The science behind Amazon’s spatial audio-processing technology

Combining psychoacoustics, signal processing, and speaker beamforming enhances stereo audio and delivers an immersive sound experience for customers.

With every new Echo device and upgrade, we challenge ourselves to bring the best audio experience to our customers at an affordable price. This year, we’re introducing Amazon’s own custom-built spatial audio-processing technology, designed to enhance stereo sound on compatible Echo devices.

The version of the technology on Echo Studio, for instance, is customized to the specific acoustic design of the speakers and employs digital-processing methods — such as upmixing and virtualization — so stereo audio, television shows, and movie soundtracks feel closer to the listener, with greater width, clarity, and presence. It turns the Echo Studio into a hi-fi audio system that mirrors that of a stereo reference arrangement. Vocal performances are more present in the center soundstage, and stereo panned instruments are better defined on the sides, thereby creating a more immersive sound experience that reproduces the artist's intent.

In this blog post, we break down how we built this spatial audio-processing technology with an emphasis on the way humans perceive sound — or psychoacoustics — by using a combination of crosstalk cancellation, speaker beamforming, and upmixing to create a room-filling, spatial audio experience.

Psychoacoustics: Width, depth, and listening zones

Throughout development, we characterize the stereo image by its psychoacoustic qualities, including width, depth, and listening zones. We then investigate how sound waves interact with listeners in various room shapes and sizes and how signal-processing methods affect the listener’s experience.

Stereo angle.png
Echo Studio virtualizes the stereo sound field at the listener’s location in the far field.

Width

Width: The angular extent (wide vs. narrow) of localizable elements in the stereo image along the horizontal — or azimuth — plane.

When determining the width of a sound field, we first consider localizable elements such as a point-source that would induce time and level differences in the acoustic responses at the listener’s two ears. To model this phenomenon, it is helpful to compare the listening experiences on headphones vs. a loudspeaker in terms of the separation of left and right ear responses.

Unlike loudspeaker listening, headphone listening lacks a crosstalk path, as illustrated in the image below. In order to make headphone listening realistic, we can model crosstalk from the point-source to the two ears using an all-pass signal-processing filter for one ear and a delayed low-pass filter for the other ear. The two filters approximate and parameterize the listener’s ear responses with respect to their relative head-related transfer functions (HRTFs), which contain important cues that the human ear uses to localize sound. Moreover, the filter design ensures that there’s minimal modification to the signal spectra — or tonal balance — and therefore preserves the original playback content.

Crosstalk simulation.png
All-pass and delayed low-pass filters approximate the angle-dependent relative ipsilateral (same side of the body) and contralateral (opposite side of the body) head-related transfer functions (HRTFs).

However, unlike headphones, an external speaker can create its own crosstalk for the listener, depending on its placement. For example, the left and right speaker transducers, or drivers, on the Echo Studio are narrowly spaced within the device, whereas the speakers in a standard stereo pair are 60 degrees apart relative to the listener.

With the spatial audio-processing technology on Echo Studio, we decouple the crosstalk of the driver pair by modeling and then inverting the system of equations between each driver and the listener’s ears, via crosstalk cancellation (CTC) methods. If we have more than two drivers, then the more general formulation is called null-steering, where filters are designed for all the drivers so that their acoustic responses cancel at one ear.

In both cases, we can normalize the filter design to satisfy a target cancellation gain curve defined by the power ratio of the acoustic energy at the ipsilateral (same side of the body) and contralateral (opposite side of the body) ears across frequencies. This prevents overfitting the cancellation to an exact location, since a listener may be at varying distances or not perfectly centered to the device.

Once the driver’s CTC filters are designed for stereo inputs, they can be combined with the approximated HRTF filters that introduce the amount of crosstalk consistent with a stereo reference system.

CTC filters.png
Stereo virtualization for external speaker playback specifies an additional pair of crosstalk cancellation (CTC) filters for nulling the contralateral acoustic response. The relative transfer function (RTF) filter realizes the ratio of the two CTC filter responses.

Depth

Depth: The distance (frontal vs. recessed) of the perceived sound field from the listener.

The distance at which sound elements in an audio track localize correlates with the relationship — or coherence — of the two signals between the sound source and the listener’s ears. For example, a simple left or right signal from a speaker is easy to understand, but if the audio mixes with the room’s reverberation, the audio clarity decreases, and the audio sounds recessed.

In speaker playback, however, we contend with the speaker directivity and its interaction with the room environment. For example, a direct acoustic path between a speaker and a listener preserves the desired clarity of the original content. But when the acoustic signal reflects off of walls, the loss in coherence recesses the perceived sound field and causes elements to smear spatially. This is why tracks heard anechoically or on headphones appear closer — or even inside the listener’s head — and clearer than tracks heard over external speakers in a reverberant room. In the first case, the acoustic response is direct from the driver to the listener’s ears, while external speakers must contend with the effects of the room environment.

Beamformer impact.png
Strong room reflections and reverberation mask the binaural cues and reduce the perceived distance of the soundstage. Speaker beamforming pushes the soundstage forward by attenuating the indirect sound energy, increasing the critical distance and coherence.

As part of our custom-built spatial audio technology, we can control the speaker directivity via careful beamforming. The speaker drivers can be filtered to produce a sound field with a directivity that sums coherently on-axis and cancels off-axis. That is, the acoustic response is greatest when the listener is lined up in front of the speaker and, conversely, weakest when the listener is to the side at +/- 90 degrees.

Therefore, one way to design with such directivity is to place two nulls at +/- 90-degree angles and either control for the cancellation gain between on-/off-axis power responses or the shape of the nulls as a function of azimuth. The resulting beam pattern is one with a main lobe that is wide enough for the direct path to be strong, at up to a +/- 45-degree azimuth listening window, before quickly tapering off to minimize the acoustic energy further off-axis, which would reflect off the walls.

This has the intended effect of making stereo audio feel closer to the listener, with greater clarity than is typical in an acoustically untreated listening environment like a living room. The effect is similar to how theaters reproduce a frontal soundstage over different seating areas, despite the speakers’ being far away.

Beamforming.png
The speaker beamformer increases directivity after placing two off-axis nulls in the midrange frequencies. The acoustic responses over frequency and azimuth contrast that of simple matrix mixing with the beamformer realized in relative-transfer-function (RTF) form.

Listening zones

Listening zone: The mapping between the listening area and the stereo soundstage.

A listening “sweet spot” — the stereo image in a hi-fi audio system reference stereo pair — is best reproduced when the listener’s location forms an equilateral triangle with the stereo speaker pair. If the listener angle exceeds +/- 30 degrees, then a hole is created in the listener’s phantom center due to the loss of inter-speaker-to-ear coherence as room reflections grow stronger. Important elements of the audio mix, such as vocals, lose their presence. If the listener angle falls below +/- 30 degrees, then the stereo image narrows, as audio elements collapse toward the center. If the listener’s location is off-axis, then the stereo image biases towards one side or the other.

Phantom center.png
The stereo field relies on a “phantom center”, where important lead vocals and instruments are mixed. The center content can be separated from the original stereo left and right input after the mid-/side decomposition.

To combat this, our spatial audio technology aims to reproduce the stereo image over the largest listening area. In practice, the intended listening area of CTC-filtered playback conflicts with that of beamforming designs that control for speaker directivity. We can achieve a compromise by performing stereo upmixing and then applying different beamforming filters to each channel. For example, we can upmix into left, right, and center (LRC), where the center is minimally correlated with left-minus-right in the mid-/side decomposition.

The upmixed left channel is processed through the CTC filter that nulls the right ear after virtualization, the upmixed right channel nulls the left ear, and the center channel is beamformed with a wide main lobe. This means that vocal performances are more present in the center, while the stereo panned instruments are better defined on the side, creating a more immersive sound experience for the listener.

Signal flow.png
After upmixing, the virtualization and the crosstalk cancellation (CTC) widens the left and right channels, and the midrange beamformer pushes the center content forward. Subsequent delay blocks phase-align the faster of the two paths.

We’re continuing to iterate and refine technology across the Echo portfolio to bring the best audio experience to our customers. If you’d like to learn more about beamforming and speaker directivity in room acoustics, read papers published by our engineering team: “Fast source-room-receiver modeling”, in EUSIPCO 2020, and “Spherical harmonic beamformer designs", in EURASIP 2021.

Research areas

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Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists
US, CA, San Francisco
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities As an Applied Scientist in the Foundations Model team, you will: - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. About the team We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities
US, CA, San Francisco
Amazon is seeking an exceptional Sr. Applied Scientist to lead the development of perception systems that harness the power of radar and thermal imaging — enabling robots to perceive and operate reliably in conditions where conventional vision alone falls short. In this role, you will develop ML-driven perception pipelines for non-traditional sensing modalities, pushing the boundaries of what robots can see, understand, and act upon in challenging real-world environments. At Amazon, we leverage advanced robotics, machine learning, and artificial intelligence to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence. As a Sr. Applied Scientist in Multi-Modal Perception, you will apply deep computer vision expertise alongside classical signal processing techniques for radar and thermal imaging — modalities that provide robustness in adverse conditions and sensing capability beyond the visible spectrum. You will develop ML-based methods to extract semantic and geometric information from radar point clouds, radar tensors, and thermal imagery, and fuse these with camera and depth data to build perception systems that are reliable, comprehensive, and ready for deployment at scale. Your work will unlock new capabilities for our robots — enabling reliable detection, classification, and scene understanding in low-visibility conditions, cluttered environments, and scenarios where traditional RGB-based perception is insufficient. You will lead research that translates cutting-edge advances in deep learning and computer vision to these underexplored but high-impact sensing modalities. Join us in building the next generation of multi-modal perception systems that will define the future of autonomous robotics at scale. Key job responsibilities - Lead the research, design, and development of ML-based perception pipelines for radar and thermal/infrared imaging modalities - Develop deep learning models for object detection, classification, segmentation, and tracking using radar data (point clouds, range-Doppler maps, radar tensors) and thermal imagery - Design and implement multi-modal fusion architectures that combine radar, thermal, camera, and depth data for robust, all-condition perception - Develop novel representations and feature extraction methods tailored to the unique characteristics of radar and thermal sensors (sparsity, noise profiles, spectral properties) - Build end-to-end perception systems — from raw sensor data processing and calibration to model training, evaluation, and real-time deployment - Collaborate closely with Hardware, Navigation, Planning, and Controls teams to define sensor configurations and deliver integrated autonomy solutions - Establish benchmarks, datasets, and evaluation frameworks for radar and thermal perception - Mentor scientists and engineers; foster a culture of scientific rigor, innovation, and high-impact delivery - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.