Signal Processor Improves Echo’s Bass Response, Loudness, and Speech Recognition Accuracy

Multiband dynamics processing, which separately modifies volume in different frequency bands of an audio signal, is known to improve listeners’ audio experiences. But in the context of voice-controlled systems like the Amazon Echo family of products, it can also improve automatic speech recognition by making echo cancellation easier.

Traditional multiband dynamics processors (MBDPs) have a few drawbacks, however. When splitting a signal into its component frequencies, they don’t always achieve clean separation; and they tend to use fixed frequency bands, which can’t be adjusted to the characteristics of specific audio devices.

Both of these drawbacks can affect the listener’s perception of both the loudness and bass response of an audio signal. They can also cause distortions that make echo cancellation more difficult.

At this year’s International Conference on Acoustics, Speech and Signal Processing, my colleagues and I present a novel MBDP design that addresses both these drawbacks. The technology began shipping in Alexa-enabled devices in 2017, and extensive user testing indicates that it improves listener perception of loudness and bass. In tests, it significantly improved performance on a fundamental speech recognition task. Moreover, the computational complexity of our MBDP system is small.

scrollingwaveformsV2.gif._CB467417779_.gif
Three waveforms: an original audio signal (top); the signal after processing by a conventional MBDP system, with spiky deformations throughout (middle); and the signal after processing by our novel system, which limits the distortion but better preserves shape (bottom).

An MBDP has two main functions: one is compression, or keeping the ratio of a signal’s maximum and minimum volumes within a prescribed range; and the other is peak limiting, or cutting off sudden volume spikes that can cause distortion or even cause the signal from cutting out momentarily, a condition called brownout.

Applying different compressors and limiters to different frequency bands provides greater signal control. But it also depends on filters that can provide clean frequency separation. So the key to our system’s performance is its configurable filter-bank design.

Our filter bank consists of a cascade of filters, all of which or only a few of which may be used at a time. An incoming signal is split in two; half of it passes to two sequential high-pass filters, which filter out frequencies below a cutoff frequency, and the other half passes to two sequential low-pass filters, which filter out frequencies above the same cutoff frequency.

The signal from the high-pass filter may be split again, and again passed to separate banks of high-pass and low-pass filters. This process may repeat an arbitrary number of times, and at each stage, the output of the low-pass filter passes to an “all-pass” filter, which leaves the signal unchanged but enables the synchronization of all the bands. The high-pass and low-pass frequencies may be set to arbitrary values, so that the filtration frequency bands can be tailored to specific applications.

Filterbank_architecture.png._CB467153366_.png
Our proposed reconfigurable filter bank

The signal in each frequency band passes to its own dedicated compressor and then to a limiter. At that point, the frequency-specific signals are recombined and passed to full-band limiter, which ensures that the frequency-specific modifications don’t cause the signal as a whole to distort.

Echo cancellation systems like the one found in Amazon Echo devices subtract a known audio signal — the electrical signal sent to the device’s loudspeaker — from the signal received by the device’s microphones. The more distortion the audio signal suffers, the less it will resemble the reference signal, and the less successful the subtraction will be.

Our MBDP system reduces distortion in three ways. First, the greater precision of the filter bank enables better control of the compression ratios in different frequencies. That means that the system can reduce a loudspeaker’s total harmonic distortion without compromising the overall loudness and bass response of the audio signal.

Similarly, the frequency-specific and full-band peak limiters ensure that the loudspeaker stays in its “linear dynamic range,” meaning that the sound pressure level doesn’t exceed the threshold at which it will begin to cause distortion.

The linear dynamic range is a mechanical property of the loudspeaker. But the electrical signal can become distorted before it even reaches the loudspeaker, if the amplifier attempts to output too high a voltage. This is known as clipping, and the full-band limiter can prevent that, as well.

We conducted extensive listening tests, in which study participants reported that audio processed using our reconfigurable MBDP scheme sounds much better and louder than audio processed using the traditional MBDP scheme. Spectral analyses also demonstrated that our system increases bass response by about five decibels.

FRR_graph.png._CB467153364_.png
Our system (blue line) significantly reduced the rate at which an Echo device falsely rejected Alexa’s wake word (false reject rate, or FRR), as a function of device audio volume.

To evaluate our system’s effect on speech recognition, we tested Echo devices’ responses to Alexa’s wake word — usually “Alexa” — when they were broadcasting audio at a range of volumes. We found that using our MBDP scheme instead of the traditional scheme significantly reduced the number of false rejects, or instances in which the Echo failed to recognize the wake word. We also found that the higher the Echo’s output volume, the greater the advantage offered by our approach.

Acknowledgments: Amit S. Chhetri, Carlo Murgia, Philip Hilmes

About the Author
Jun Yang is a senior research scientist in Amazon Devices' Hardware Technology and Architecture group.

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Machine Learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The ML team within AWS provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and the cloud. As a Sr. ML Data Scientist in the AWS ML Solutions Lab team, you'll partner with technology and business teams to build new services that surprise and delight our customers. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will help by developing new ML models, pipelines and architectures to help customers solve their critical business cases, such as autonomous driving, supply chain optimization, predictive maintenance, fraud detection and more. You will support our customers on their ML journey by helping to develop Proof of Concepts, and at the same time helping them understand the technology behind the scientific choices you make.We’re looking for Senior ML Data Scientists capable of using ML and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.The primary responsibilities of this role are to:· Use deep learning, machine learning and analytical techniques to create scalable solutions for business problems· Design, development and evaluation of highly innovative models for predictive learning, content ranking, and anomaly detection· Interact with customers directly to understand the business problem, help and aid them in implementation of DL/ML algorithms to solve problems· Analyze and extract relevant information from large amounts of historical data to help automate and optimize key processes· Work closely with account team, research scientist teams and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 40%.We at AWS value individual expression, respect different opinions, and work together to create a culture where each of us is able to contribute fully. Our unique backgrounds and perspectives strengthen our ability to achieve Amazon's mission of being Earth's most customer-centric company.This team will be comprised of Deep Learning Architects and Data Scientists to create cutting edge solutions for clients across EMEA. We are currently recruiting for talented individuals in the following cities: London and Berlin. Discover more at https://www.amazon.jobs/en/teams/amazonai.
DE, BE, Berlin
Machine Learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The ML team within AWS provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and the cloud. As an ML Data Scientist in the AWS ML team, you'll partner with technology and business teams to build new services that surprise and delight our customers. You will be working with terabytes of text, images, and other types of data to solve real-world problems.You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will help by developing new ML models, pipelines and architectures to help customers solve their critical business cases, such as autonomous driving, supply chain optimization, predictive maintenance, fraud detection and more. You will support our customers on their ML journey by helping to develop Proof of Concepts, and at the same time helping them understand the technology behind the scientific choices you make.We’re looking for top ML data scientists capable of using ML and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.The primary responsibilities of this role are to:· Use deep learning, machine learning and analytical techniques to create scalable solutions for business problems· Design, development and evaluation of highly innovative models for predictive learning, content ranking, and anomaly detection· Interact with customers directly to understand the business problem, help and aid them in implementation of DL/ML algorithms to solve problems· Analyze and extract relevant information from large amounts of historical data to help automate and optimize key processes· Work closely with account team, research scientist teams and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 40%.We at AWS value individual expression, respect different opinions, and work together to create a culture where each of us is able to contribute fully. Our unique backgrounds and perspectives strengthen our ability to achieve Amazon's mission of being Earth's most customer-centric company.This team will be comprised of Deep Learning Architects and Data Scientists to create cutting edge solutions for clients across EMEA. We are currently recruiting for talented individuals in the following cities: London and Berlin. Discover more at https://www.amazon.jobs/en/teams/amazonai.
IN, TS, Hyderabad
Are you interested in building the next-generation services that will redefine large scale transportation?We are looking for data scientists to be based in Hyderabad/Bangalore, India with 5+ years of experience in problem-solving using statistical modelling and data science.As part of transportation business, work on creating opportunities to improve network operations for speed and efficiency. As data scientist, you will build forecasting, optimization, outlier detection models to improve predictability and reliability in network operations. Responsibilities include:· Collaborate with operators, program managers, analysts to define right analytical framework and identify improvement opportunitie· Collaborate with engineers, product managers, and analysts to design and implement analytical products· Design, experiment, and scale data driver solutions
US, NY, New York
How can Amazon improve the advertising experience for customers around the world? How can we help advertisers and customers find each other in a meaningful way? Amazon Advertising creates and transforms the connection between retailers, service providers and customers. Join the Analytics & Insights Team to contribute to product and service solutions that allow us to solve this with data science. If you are passionate about developing analytics and testing solutions to solve business problems, and are looking for a team that drives results to help influence Amazon business decisions, this is the right place for you.The Analytics & Insights Professional Services Team is looking for a Head of Advertising Experimentation Team to lead a team of Data Scientists and Business Analysts who analyze big data to build models and algorithms that power our advertising experimentation services and products. We work with advertisers on recommendations for testing in order to improve their advertising effectiveness and strive to better understand the advertising and non-advertising features that best influence and predict advertising campaign performance and incrementality. In this role, you will set the vision and direction for the team and collaborate with internal stakeholders across product and services to scale and advance our experimentation and incrementality testing offerings. The ideal candidate must be willing to effectively lead the team, project-manage and prioritize across multiple stakeholders and tasks, exhibit strong problem-solving skills and be ready to jump into a fast-paced, dynamic and fun environment.Responsibilities:· Lead and provide coaching to the Advertising Experimentation Team including Data Scientists and Business Analysts.· Partner with advertisers and experimentation teams to generate A/B and incrementality test recommendations to improve marketing effectiveness and inform their future marketing investments.· Work with product, data science, experimentation and analytics teams to share knowledge from performance tests, design packaged experimentation insights and inform future product roadmaps.· Use Amazon’s unique data, analyze huge and complex data sets, design and implement solutions using a range of data science methodologies to solve complex business problems.· Demonstrate deep analytical ability, and develop great expertise in Amazon’s proprietary metrics, working to constantly evolve how we analyze and communicate data driven insights to our advertisers.· Build consensus with business stakeholders on how your models and algorithms will drive the optimal results for Amazon customers.· Educate advertisers and internal teams on performance and incrementality testing by writing whitepapers and knowledge documentation and delivering learning sessions.