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.

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Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (audio, images, videos, text) by building AI-driven solutions for advertisers. To accomplish this, we are investing in understanding how best users can leverage Generative AI methods such as latent-diffusion models, large language models (LLM), generative audio (music and speech synthesis), computer vision (CV), reinforced learning (RL) and related. As an Applied Scientist you will be part of a close-knit team of other applied scientists and product managers, UX and engineers who are highly collaborative and at the top of their respective fields. We are looking for talented Applied Scientists who are adept at a variety of skills, especially at the development and use of multi-modal Generative AI and can use state-of-the-art generative music and audio, computer vision, latent diffusion or related foundational models that will accelerate our plans to generate high-quality creatives on behalf of advertisers. Every member of the team is expected to build customer (advertiser) facing features, contribute to the collaborative spirit within the team, publish, patent, and bring SOTA research to raise the bar within the team. As an Applied Scientist on this team, you will: - Drive the invention and development of novel multi-modal agentic architectures and models for the use of Generative AI methods in advertising. - Work closely and integrate end-to-end proof-of-concept Machine Learning projects that have a high degree of ambiguity, scale and complexity. - Build interface-oriented systems that use Machine Learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Curate relevant multi-modal datasets. - Perform hands-on analysis and modeling of experiments with human-in-the-loop that eg increase traffic monetization and merchandise sales, without compromising the shopper experience. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Mentor and help recruit Applied Scientists to the team. - Present results and explain methods to senior leadership. - Willingness to publish research at internal and external top scientific venues. - Write and pursue IP submissions. Key job responsibilities This role is focused on developing new multi-modal Generative AI methods to augment generative imagery and videos. You will develop new multi-modal paradigms, models, datasets and agentic architectures that will be at the core of advertising-facing tools that we are launching. You may also work on development of ML and GenAI models suitable for advertising. You will conduct literature reviews to stay on the SOTA of the field. You will regularly engage with product managers, UX designers and engineers who will partner with you to productize your work. For reference see our products: Enhanced Video Generator, Creative Agent and Creative Studio. A day in the life On a day-to-day basis, you will be doing your independent research and work to develop models, you will participate in sprint planning, collaborative sessions with your peers, and demo new models and share results with peers, other partner teams and leadership. About the team The team is a dynamic team of applied scientists, UX researchers, engineers and product leaders. We reside in the Creative X organization, which focuses on creating products for advertisers that will improve the quality of the creatives within Amazon Ads. We are open to hiring candidates to work out of one of the following locations: UK (London), USA (Seattle).
US, WA, Bellevue
The Amazon Fulfillment Technologies (AFT) Science team is seeking an exceptional Applied Scientist with strong operations research and optimization expertise to develop production solutions for one of the most complex systems in the world: Amazon's Fulfillment Network. At AFT Science, we design, build, and deploy optimization, statistics, machine learning, and GenAI/LLM solutions that power production systems running across Amazon Fulfillment Centers worldwide. We tackle a wide range of challenges throughout the network, including labor planning and staffing, pick scheduling, stow guidance, and capacity risk management. Our mission is to develop innovative, scalable, and reliable science-driven production solutions that exceed the published state of the art, enabling systems to run optimally and continuously (from every few minutes to every few hours) across our large-scale network. Key job responsibilities As an Applied Scientist, you will collaborate with scientists, software engineers, product managers, and operations leaders to develop optimization-driven solutions that directly impact process efficiency and associate experience in the fulfillment network. Your key responsibilities include: - Develop deep understanding and domain knowledge of operational processes, system architecture, and business requirements - Dive deep into data and code to identify opportunities for continuous improvement and disruptive new approaches - Design and develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and emerging challenges - Create prototypes and simulations for agile experimentation of proposed solutions - Advocate for technical solutions with business stakeholders, engineering teams, and senior leadership - Partner with software engineers to integrate prototypes into production systems - Design and execute experiments to test new or incremental solutions launched in production - Build and monitor metrics to track solution performance and business impact About the team Amazon Fulfillment Technology (AFT) designs, develops, and operates end-to-end fulfillment technology solutions for all Amazon Fulfillment Centers (FCs). We harmonize the physical and virtual worlds so Amazon customers can get what they want, when they want it. The AFT Science team brings expertise in operations research, optimization, statistics, machine learning, and GenAI/LLM, combined with deep domain knowledge of operational processes within FCs and their unique challenges. We prioritize advancements that support AFT tech teams and focus areas rather than specific fields of research or individual business partners. We influence each stage of innovation from inception to deployment, which includes both developing novel solutions and improving existing approaches. Our production systems rely on a diverse set of technologies, and our teams invest in multiple specialties as the needs of each focus area evolve.