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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Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. 다양성 AWS는 다양한 경험을 중요하게 생각합니다. JD에 나와 있는 자격 및 기술을 모두 충족하지 못하더라도 지원자가 지원하도록 권장합니다. 경력을 이제 막 시작하였거나, 전통적인 경력을 쌓지 않았거나, 조금 다른 경험을 쌓았다고, 지원을 중단하실 필요는 없습니다. AWS를 선택해야 하는 이유 아마존 웹 서비스 (AWS) 는 세계에서 가장 포괄적이고 널리 채택된 클라우드 플랫폼입니다.우리는 클라우드 컴퓨팅 시장을 개척했으며 혁신을 멈추지 않았습니다. 이것이 바로 가장 성공적인 스타트업부터 Global 500 기업에 이르는 고객이 AWS의 제품 및 서비스 제품군을 신뢰하는 이유입니다. 일과 삶의 균형 우리는 일과 삶의 조화를 중요하게 생각합니다.직장에서의 성공을 위해 가정에서의 희생을 감수해서는 안 됩니다. 그렇기 때문에 유연한 근무 시간과 근무 방식이 우리 문화의 일부입니다.직장과 가정에서 지지받는다고 느낄 때 클라우드로는 달성할 수 없는 것이 없습니다. 포용적인 팀 문화 AWS에서는 배우고 호기심을 갖는 것이 우리의 본능입니다.직원이 주도하는 어피니티 그룹은 서로 다른 점을 자랑스럽게 여길 수 있는 포용의 문화를 조성합니다.인종 및 민족에 관한 대화 (CORE) 및 AmazeCon (성별 다양성) 컨퍼런스를 포함하여 진행 중인 이벤트와 학습 경험은 우리가 우리의 독창성을 받아들일 수 있도록 영감을 줍니다. 멘토십 및 경력 개발 우리는 세계 최고의 고용주가 되기 위해 노력하면서 지속적으로 성과 기준을 높이고 있습니다.그렇기 때문에 더 다재다능한 전문가로 발전하는 데 도움이 되는 지식 공유, 멘토십 및 기타 경력 개발 리소스를 찾을 수 있습니다. 일과 삶의 균형 우리는 일과 삶의 조화를 중요하게 생각합니다.직장에서의 성공을 위해 가정에서의 희생을 감수해서는 절대 안 됩니다. 이것이 바로 우리가 근무 문화의 일환으로 유연성을 추구하기 위해 노력하는 이유입니다.직장과 가정에서 지지받는다고 느낄 때 클라우드로는 달성할 수 없는 것이 없습니다. #aws-korea-proserv-ap #AWSKOREA
DE, Aachen
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
CA, BC, Vancouver
We are open to candidates located in: Seattle and Bellevue, Washington Atlanta, GA As a Senior Data Scientist, you will be on the ground floor with your team, shaping the way performance is measured, defining what questions should be asked, and scaling analytics methods and tools to support our growing business. You will work closely with Data Engineers, Product Managers, Business Intelligence Engineers, and Software Engineers to develop statistical models, design and run experiments, and find new ways to to optimize the customer experience. A successful candidate is highly analytical, able to work effectively in a matrix organization, and adept at synthesizing a variety of technologies and capabilities into products that enhances the PXF experience across multiple products. You must engage with customers to deeply understand their current and emerging needs. PXF applications are rapidly evolving and our user base is rapidly expanding, as a DS on the team you will own diving into the different users personas and inventing on behalf of the app users to meet their needs. We are looking for someone who's customer obsessed and technology savvy - with a passion for app development work. The ideal candidate will have a well-rounded technical background as well as a history of leading complex, ambiguous projects end-to-end. Key job responsibilities - Partner with business stakeholders in formulating the business problem and providing recommendations on the approach - Understanding customer behavior to personalize customer experience, build recommendation engines to provide relevant results to customers, customer lifecycle analysis and usage behavior - Conduct large scale A/B testing and offline/online experiments to evaluate performance of various programs and drive product improvements across partner teams - Process large scale datasets using distributed computing platform to build models, mining insights from data and prototyping models that optimize towards various business goals and metrics - Interact with cross-functional teams and make business recommendations i.e cost-benefit, forecasting, experiment analysis and present findings to leadership team - Driving product roadmap and prioritizations of science projects with the PMs to improve customer experience About the team PXF builds the employee experiences that connect Amazonians, support them through their employment journey, and make Amazon Earth's Best Employer. Our products include A to Z mobile application directly impacts the lives of associates by helping them identify the best shifts for their schedule, opportunities to pick up additional work, and choose when they get paid. We enable Amazon employees to easily find and access high-quality and authoritative content throughout their employment lifecycle through content management and Search capabilities. We also provide employees with a dynamic and ever-evolving learning experience to protect, prepare, and advance their careers.
US, Virtual
Amazon is deeply invested in R&D with hundreds of researchers and applied scientists committed to innovation across every part of the company. The Amazon Scholars and Visiting Academic programs have broadened opportunities for academics to join Amazon in a flexible capacity, in particular part-time arrangements and sabbaticals. The program is designed for academics from universities around the globe who want to apply research methods in practice and help us solve hard technical challenges without leaving their academic institutions. We believe that Amazon is a unique place to measure the impact of new scientific ideas, given our scale and our ownership of both an information infrastructure and physical infrastructure. You will have a chance to have a ground-up impact on our systems, our business, and most importantly, our customers, through your expertise. Applications are accepted from academic experts in research areas including, but not limited to, the following: Artificial Intelligence, Avionics, Computer Vision, Data Science, Economics, Machine Learning, Optimization, Natural Language Processing, Quantum Computing, Robotics and Sustainability. Key job responsibilities As an Amazon Scholar or Visiting Academic, your responsibilities may include: * Advising business leaders on strategic plans * Diving deep to solve a specific technical problem in an organization’s roadmap * Advising junior researchers on methods.
IN, KA, Bengaluru
Advertising at Amazon is a fast-growing multi-billion dollar business that spans across desktop, mobile and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third party publishers; and extends across US, EU and an increasing number of international geographies. One of the key focus areas is Traffic Quality where we endeavour to identify non-human and invalid traffic within programmatic ad sources, and weed them out to ensure a high quality advertising marketplace. We do this by building machine learning and optimization algorithms that operate at scale, and leverage nuanced features about user, context, and creative engagement to determine the validity of traffic. The challenge is to stay one step ahead by investing in deep analytics and developing new algorithms that address emergent attack vectors in a structured and scalable fashion. We are committed to building a long-term traffic quality solution that encompasses all Amazon advertising channels and provides state-of-the-art traffic filtering that preserves advertiser trust and saves them hundreds of millions of dollars of wasted spend. We are looking for talented applied scientists who enjoy working on creative machine learning algorithms and thrive in a fast-paced, fun environment. An Applied Scientist is responsible for solving inherently hard problems in advertising fraud detection using deep learning, self-supervised techniques, representation learning and advanced clustering. An ideal candidate should have strong depth and breadth knowledge in machine learning, data mining and statistics. Traffic quality systems process billions of ad-impressions and clicks per day, by leveraging cutting-edge open source technologies like Hadoop, Spark, Redis and Amazon's cloud services like EC2, S3, EMR, DynamoDB and RedShift. The candidate should have reasonable programming and design skills to manipulate unstructured and big data and build prototypes that work on massive datasets. The candidate should be able to apply business knowledge to perform broad data analysis as a precursor to modeling and to provide valuable business intelligence.