Amazon takes top three spots in Audio Anomaly Detection Challenge

Team from Amazon Web Services also wins the best-paper award at the Workshop on Detection and Classification of Acoustic Scenes and Events.

This week at Amazon Web Services’ re:Invent 2020 conference, Amazon announced Amazon Monitron, an end-to-end machine-monitoring system composed of sensors, a gateway, and a machine learning model that detects anomalies in vibration (structure-borne sound) or temperature and predicts when equipment may require maintenance. 

Machine condition monitoring was also the topic of a challenge at the Workshop on the Detection and Classification of Acoustic Scenes and Events (DCASE 2020), in November, in which Amazon took the top three spots, out of 117 submissions.

The challenge was to determine whether the sounds emitted by a machine — such as a fan, pump, or valve — were normal or anomalous. Forty academic and industry teams submitted entries, an average of almost three submissions per team.

In a pair of papers (paper 1|paper 2) we presented at the workshop, we describe the two different neural-network-based approaches we took in our submissions to the challenge. The first of those papers won the workshop’s best-paper award.

Anomaly spectrograms.png
Spectrograms of audio clips recorded from a normal valve (top) and a faulty valve (bottom). The magnified details show the difference between the normal signal and the anomalous signal.

Auditory machine condition monitoring has been common in industrial settings for several decades. Seasoned maintenance experts can identify problems in the machines they monitor just by listening to them and realizing that “something doesn’t sound right.” But by the time anomalies are audible to the human ear, the underlying problems may already be well advanced.

With the advent of machine learning and big data, there has been a lot of interest in teaching machines to detect anomalies sooner, to help predict when preventative maintenance might be necessary.

Data, labels, and rare failures

In general, anomaly detection is the problem of identifying abnormal inputs in a stream of inputs. Depending on the available data, there are three different ways to train anomaly detection systems: (i) fully supervised, in which labeled examples of normal and abnormal data are presented; (ii) semi-supervised, in which only normal data is presented; and (iii) unsupervised, in which there are no labels in the data set, and outliers have to be classified automatically. 

Anomalies can manifest themselves in different ways. For instance, you can have slow concept drift or sudden, instantaneous outliers. Typically, the data is also highly imbalanced — a lot more “normal” examples than “abnormal.”

Machines worth monitoring carefully — especially those that are critical or expensive — are usually also well maintained. This means that they rarely fail, and gathering anomalous data from them is challenging and may take many years and lots of effort.

Additionally, machines operate in different modes and under variable load or performance conditions, and their characteristics can change over time as they age and approach steady state. Some industries’ operational profiles have seasonal variations as well. 

All of these factors make anomaly detection challenging in the industrial setting. When implementing an anomaly detection system, one has to depend mostly on “normal” data, gathering additional data over time and eliciting user feedback. 

If accurate physical models of machines are available, it may be possible to simulate failures and generate “abnormal” data that way. One can also generate anomalous data by inducing hardware failures in the lab. But one has to be prepared to work with minimal data when a machine is instrumented for the first time (the so-called cold-start problem).

Anomaly detection and our two neural approaches

The papers we presented at DCASE (paper1|paper2) describe two different neural-network-based approaches to anomaly detection.

The first approach builds on recent advances in autoregressive neural-density estimation, or calculating a data distribution for streaming data by trying to predict each new data item on the basis of those that preceded it. As might be expected, such models are very sensitive to the order in which data arrives.

An earlier model, called the masked autoencoder for density estimation (MADE), makes a separate prediction for each feature — each dimension — of the input. With audio signals, however, the dimensions of the input are the energies in different frequency bands, which produce a composite picture of the signal that individual frequencies won’t capture. 

We introduce a variation of MADE that bases its predictions on groups of input features — in this case, groups of frequency bands — and which we accordingly call Group MADE.

In the second paper, we use a self-supervised approach for representation learning, which has been successful recently in solving problems in vision and speech. We believe that we are the first to apply it to audio anomaly detection. 

In the absence of anomalies in the training data, we trained a network to instead learn to distinguish multiple instances of machines within a given machine type. We found that the features learned by such a network were sensitive enough to detect delicate, previously unseen anomalies in the evaluation set. We used spectral warping and random mixing to simulate new machine instances in addition to the ones provided in the dataset. 

Industrial-monitoring embeddings.stitched.png
Two-dimensional visualizations of two different representations of the Toy Car sounds in the DCASE data set: the raw spectrograms (left) and the features learned by our self-supervised model (right). The blue samples represent data from a normal machine, the red samples data from an anomalous one. In the raw spectrograms, there is little separation between the normal and anomalous samples, while the learned features can much more clearly separate out the anomalous samples.

The DCASE challenge provided data from six different machines: fan, pump, slide rail, valve, toy car, and toy conveyor. DCASE also provided a development data set and a separate evaluation data set. Scoring was calculated using area under the ROC curve (AUC) and partial area under the ROC curve. The ROC curve maps false-positive rate against false-negative rate, so the area under the curve indicates how well a given system manages that trade-off; partial AUC is the AUC over a small false-positive-rate range, in this case [0, 0.1]. 

The table below shows the accuracies we were able to obtain, both for the challenge and since the challenge. We have developed a third approach that helped improve some of these numbers, which we will detail in a future publication. 

The challenge ranking method involved two steps, to account for the the disparate difficulty levels across various machine types. First, machine-specific rankings were assigned to all submissions, based on AUC and pAUC. The submissions were then ranked by the average of their machine-specific ranks. Please see the full leaderboard here.

While our models won the challenge using the across-all-machine-types scoring described above, fine-tuning them for specific machine types yielded the results in the last row.

DCASE results table.png

We believe that as more industrial machine data is accumulated and curated over the next few years, machine learning and neural-network-based approaches will start making a huge difference in the monitoring and maintenance of machines, and AWS and its services will be at the forefront of this revolution.

Research areas
About the Author
Arvindh Krishnaswamy is a principal scientist with Amazon Web Services.

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About us:Amazon is a company of builders. A philosophy of ownership carries through everything we do — from the proprietary technologies we create to the new businesses we launch and grow. You’ll find it in every team across our company; from providing Earth’s biggest selection of products to developing ground-breaking software and devices that change entire industries, Amazon embraces invention and progressive thinking. Amazon is continually evolving; it’s a place where motivated employees thrive, and ownership and accountability lead to meaningful results. It’s as simple as this: we pioneer.With every order made and parcel delivered, customer demand at Amazon is growing. And to meet this demand, and keep our world-class service running smoothly, we're growing our teams across Europe. Delivering hundreds of thousands of products to hundreds of countries worldwide, our Operations teams possess a wide range of skills and experience and this include software developers, data engineers, operations research scientists, and more.About these internships:Whatever your background, if you are excited about modeling huge amounts of data and creating state of the art algorithms to solve real world problems, if you have a passion for using mathematical optimization, including linear programming, combinatorial optimization, integer programming, dynamic programming, network flows and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software, if you enjoy solving operational challenges by using computer simulations, and if you’re motivated by results and driven enough to achieve them, Amazon is a great place to be. Because it’s only by coming up with new ideas and challenging the status quo that we can continue to be the most customer-centric company on Earth, we’re all about flexibility: we expect you to adapt to changes quickly and we encourage you to try new things.Amazon is looking for ambitious and enthusiastic students to join our unique world as interns. An Amazon EU internship will provide you with an unforgettable experience in a fast-paced, dynamic and international environment; it will boost your resume and will provide a superb introduction to our activities.As an intern in Ops Research and modelling, you could join one of the following teams: Supply Chain, Amazon Logistics, Transportation, Prime Now, Inventory Placement and more.You will put your analytical and technical skills to the test and roll up your sleeves to complete a project that will contribute to improve the functionality and level of service that teams provides to our customers. This could include:· Analyze and solve business problems at their root, stepping back to understand the broader context· Apply advanced statistics and data mining techniques to analyze and make insights from big data (data sets could include: historical production data, volumes, transportation and logistics metrics, simulation/experiment results etc.) in order to forecast, across multiple geographies.· Closely collaborate with operations research scientists, business analysts, BI teams, developers, economists and more on various models’ (including predictive models) development.· Perform quantitative, economic, and/or numerical analysis of the performance of supply chain systems under uncertainty using statistical and optimization tools to find both exact and heuristic solution strategies for optimization problems.· Create computer simulations to support operational decision-making. Identify areas with potential for improvement and work with internal teams to generate requirements that can realize these improvements.· Create software prototypes to verify and validate the devised solutions methodologies; integrate the prototypes into production systems using standard software development tools and methodologies.· Convert statistical output into detailed documents which influence business actions
IN, KA, Bangalore
Are you interested in shaping the future of movies, television, and digital video? Do you want to define what type and quality of X-Ray experiences should be delivered to Amazon customers? Prime Video X-Ray is a service/platform that enables creation and delivery of deep X-Ray experience for any video from any studio for millions of Amazon customers globally. Prime Video X-Ray is an experience that is growing and delighting customers globally on VoD content, Live Sports and Channels. We are looking for a Senior Applied Scientist who can work on different aspects of the video content, like text metadata, video, audio and images to apply from variety of techniques in computer vision, deep learning, machine learning and image processing algorithms to build visual understanding, metadata extraction and curation systems.You will be contributing to a platform from the very early stages which will process terabytes of video content data. You will collaborate with other research scientists across Amazon to define the scope of the product, identify and initiate investigations of new technologies, prototype, test solutions and deliver an exceptional customer experience.You will work closely with the software development teams to build robust vision-based solutions for customer-facing applications. You should be comfortable with a large degree of ambiguity and relish the idea of solving problems that, frankly, haven’t been solved at scale before. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact on millions of people.
MX, DIF, Mexico City
At Amazon Web Services (AWS), we’re hiring highly technical Data and Machine Learning engineers to collaborate with our customers and partners on key engagements. Our consultants will develop and deliver proof-of-concept projects, technical workshops, and support implementation projects. These professional services engagements will focus on customer solutions such as Machine Learning, Data and Analytics, HPC and more.In this role, you will work with our partners, customers and focus on our AWS offerings such Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena, Amazon SageMaker and more. You will help our customers and partners to remove the constraints that prevent them from leveraging their data to develop business insights.AWS Professional Services engage in a wide variety of projects for customers and partners, providing collective experience from across the AWS customer base and are obsessed about customer success. Our team collaborates across the entire AWS organization to bring access to product and service teams, to get the right solution delivered and drive feature innovation based upon customer needs.You will also have the opportunity to create white papers, writing blogs, build demos and other reusable collateral that can be used by our customers. Most importantly, you will work closely with our Solution Architects, Data Scientists and Service Engineering teams.The ideal candidate will have extensive experience with design, development and operations that leverages deep knowledge in the use of services like Amazon Kinesis, Apache Kafka, Apache Spark, Amazon SageMaker, Amazon EMR, NoSQL technologies and other 3rd parties.This is a customer facing role. You will be required to travel to client locations and deliver professional services when needed.
MX, DIF, Mexico City
At Amazon Web Services (AWS), we’re hiring highly technical Data and Machine Learning engineers to collaborate with our customers and partners on key engagements. Our consultants will develop and deliver proof-of-concept projects, technical workshops, and support implementation projects. These professional services engagements will focus on customer solutions such as Machine Learning, Data and Analytics, HPC and more.In this role, you will work with our partners, customers and focus on our AWS offerings such Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena, Amazon SageMaker and more. You will help our customers and partners to remove the constraints that prevent them from leveraging their data to develop business insights.AWS Professional Services engage in a wide variety of projects for customers and partners, providing collective experience from across the AWS customer base and are obsessed about customer success. Our team collaborates across the entire AWS organization to bring access to product and service teams, to get the right solution delivered and drive feature innovation based upon customer needs.You will also have the opportunity to create white papers, writing blogs, build demos and other reusable collateral that can be used by our customers. Most importantly, you will work closely with our Solution Architects, Data Scientists and Service Engineering teams.The ideal candidate will have extensive experience with design, development and operations that leverages deep knowledge in the use of services like Amazon Kinesis, Apache Kafka, Apache Spark, Amazon SageMaker, Amazon EMR, NoSQL technologies and other 3rd parties.This is a customer facing role. You will be required to travel to client locations and deliver professional services when needed.