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DURHAM, N.C. -- Even if you cant name the tunes, youve probably heard them: from the iconic dun-dun-dun-dunnnn opening of Beethoven's Fifth Symphony to the melody of Ode to Joy, the German composers symphonies are some of the best known and widely performed in classical music.

Just as enthusiasts can recognize stylistic differences between one orchestras version of Beethovens hits and another, now machines can, too.

A Duke University team has developed a machine learning algorithm that listens to multiple performances of the same piece and can tell the difference between, say, the Berlin Philharmonic and the London Symphony Orchestra, based on subtle differences in how they interpret a score.

In a study published in a recent issue of the journal Annals of Applied Statistics, the team set the algorithm loose on all nine Beethoven symphonies as performed by 10 different orchestras over nearly eight decades, from a 1939 recording of the NBC Symphony Orchestra conducted by Arturo Toscanini, to Simon Rattles version with the Berlin Philharmonic in 2016.

Although each follows the same fixed score - the published reference left by Beethoven about how to play the notes -- every orchestra has a slightly different way of turning a score into sounds.

The bars, dots and squiggles on the page are mere clues, said Anna Yanchenko, a Ph.D. student and musician working with statistical science professor Peter Hoff at Duke. They tell the musicians what instruments should be playing and what notes they play, and whether to play slow or fast, soft or loud. But just how fast is fast? And how loud is loud?

Its up to the conductor -- and the individual musicians -- to bring the music to life; to determine exactly how much to speed up or slow down, how long to hold the notes, how much the volume should rise or fall over the course of a performance. For instance, if the score for a given piece says to play faster, one orchestra may double the tempo while another barely picks up the pace at all, Yanchenko said.

Hoff and Yanchenko converted each audio file into plots, called spectrograms and chromagrams, essentially showing how the notes an orchestra plays and their loudness vary over time. After aligning the plots, they calculated the timbre, tempo and volume changes for each movement, using new statistical methods they developed to look for consistent differences and similarities among orchestras in their playing.

Some of the results were expected. The 2012 Beethoven cycle with the Vienna Philharmonic, for example, has a strikingly similar sound to the Berlin Philharmonics 2016 version -- since the two orchestras were led by the same conductor.

But other findings were more surprising, such as the similarities between the symphonies conducted by Toscanini with regular modern instruments, and those played on period instruments more akin to Beethovens time.

The study also found that older recordings were more quirky and distinctive than newer ones, which tended to conform to more similar styles.

Yanchenko isnt using her code and mathematical models as a substitute for experiencing the music. On the contrary: shes a longtime concert-goer at the Boston Symphony Orchestra in her home state of Massachusetts. But she says her work helps her compare performance styles on a much larger scale than would be possible by ear alone.

Most previous AI efforts to look at how performance styles change across time and place have been limited to considering just a few pieces or instruments at a time. But the Duke teams method makes it possible to contrast many pieces involving scores of musicians and dozens of different instruments.

Rather than have people manually go through the audio and annotate it, the AI learns to spot patterns on its own and understands the special qualities of each orchestra automatically.

When shes not pursuing her Ph.D., Yanchenko plays trombone in Dukes Wind Symphony. Last semester, they celebrated Beethoven's 250th birthday pandemic-style with virtual performances of his symphonies in which all the musicians played their parts by video from home.

I listened to a lot of Beethoven during this project, Yanchenko said. Her favorite has to be his Symphony No. 7.

I really like the second movement, Yanchenko said. Some people take it very slow, and some people take it more quickly. It's interesting to see how different conductors can see the same piece of music.

The team's source code and data are available online at https://github.com/aky4wn/HMDS.

CITATION: "Hierarchical Multidimensional Scaling for the Comparison of Musical Performance Styles," Anna K. Yanchenko, Peter D. Hoff. Annals of Applied Statistics, December 2020. DOI: 10.1214/20-AOAS1391

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Machine Learning Meets the Maestros | Duke Today - Duke Today

Machine learning is one of the most used technologies in this generation. It has varied capabilities that can transform businesses across industries for the better. From being considered as a niche technology, machine learning is now seeing an increased adoption within companies in all sectors.

From a global perspective, brands are leveraging machine learning to accelerate innovation and better customer experience. For example, Nike uses machine learning for personalized product recommendations. In the F&B industry, Dominos maintains its 10 minutes or less pizza delivery time using machine learning technologies. Another widely used example is how automobile giant BMW uses machine learning to analyze data from vehicle subsystems and predicts the performance of vehicle components and recommends when they should be serviced.

In 2020, machine learning became a priority for tech companies in order to achieve revenue growth while reducing costs. In 2021, those companies are now exploring many matured applications of this technology. Disruptive tech organizations have been leading this technology across many areas like process automation, customer experience, and security.

Following the continuing growth trend, these five industries are likely to adopt machine learning to change their business processes in 2021.

The coronavirus global pandemic has highlighted the importance of investing on and optimizing the healthcare systems. Machine learning is being considered as the most promising technology that enables healthcare providers to generate large volumes of data for insightful clinical decisions. Machine learning also enables huge processes in drug discovery, cutting down the long discovery and development time and reducing overall costs. It can also improve healthcare delivery systems to better the overall quality of healthcare under low costs. In the future, machine learning is predicted to be a critical part of clinical trials. Including pharmaceuticals and the biotech industry, machine learning will be having a huge impact in all aspects.

The banking sector is already seeing many advanced use cases of machine learning, especially when it comes to fraud detection and automating processes. Machine learning applications will be proactively explored in areas in trading, investment modeling, risk prevention, and customer sentiment analysis. As countries are making digital transactions their primary mode of payment, machine learning is combining predictive analytics to play a pivotal role in helping financial companies to improve transaction efficiencies within the entire transaction lifecycle. Banks and financial institutions will also use machine learning technology to customize their banking products and offerings to stay up to date in the competitive environment.

Media giants like Amazon and Netflix have already popularized the data-based content consumption channels in recent times. When the world got initially struck with the global pandemic, the demand for new consumption models grew and left companies to leverage their artificial intelligence and machine learning capabilities to create value for the customers. In this process, machine learning is going to be crucial for the media and entertainment industry , whether its developing better recommendation engines, delivering hyper-targeted services, or presenting the most relevant content in real-time. Predictive modeling will also be key in communicating with the customers on time, anticipating their future demands, and making good investments.

The retail industry saw a big shift owing to the coronavirus pandemic. The pandemic has disrupted many traditional practices of this industry and machine learning has become a key enabler of change. From the perspective of brick and mortar stores or e-commerce companies, machine learning is helping this sector reinvent their supply chain, inventory management, predicting user behaviour, and analyzing trends. Dynamic pricing is emerging as a key machine learning application to help retailers thrive in the competitive market.

IoT devices have already flooded this industry and it is only going to increase. Machine learning will be critical to bridge the gaps created by huge amounts of data. It will serve as a building block for the industry along with automation, data connectivity, real-time error detection, supply chain visibility, warehousing efficiency, cost reduction, and asset tracking. Keeping traditional processes aside, machine learning will facilitate innovation and efficiency in the coming days.

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In 2021, Machine Learning Is Set To Transform These 5 Industries - Analytics Insight

For Immediate Release: March 19, 2021

Today, the U.S. Food and Drug Administration issued an emergency use authorization (EUA) for the first machine learning-based Coronavirus Disease 2019 (COVID-19) non-diagnostic screening device that identifies certain biomarkers that are indicative of some types of conditions, such as hypercoagulation (a condition causing blood to clot more easily than normal).

The Tiger Tech COVID Plus Monitor is intended for use by trained personnel to help prevent exposure to and spread of SARS-CoV-2, the virus that causes COVID-19. The device identifies certain biomarkers that may be indicative of SARS-CoV-2 infection as well as other hypercoagulable conditions (such as sepsis or cancer) or hyper-inflammatory states (such as severe allergic reactions), in asymptomatic individuals over the age of 5. The Tiger Tech COVID Plus Monitor is designed for use following a temperature reading that does not meet criteria for fever in settings where temperature check is being conducted in accordance with Centers for Disease Control and Prevention (CDC) and local institutional infection prevention and control guidelines. This device is not a substitute for a COVID-19 diagnostic test and is not intended for use in individuals with symptoms of COVID-19.

The FDA is committed to continuing to support innovative methods to fight the COVID-19 pandemic through new screening tools, said Jeff Shuren, M.D., J.D., director of FDAs Center for Devices and Radiological Health. Combining use of this new screening device, that can indicate the presence of certain biomarkers, with temperature checks could help identify individuals who may be infected with the virus, thus helping to reduce the spread of COVID-19 in a wide variety of public settings, including healthcare facilities, schools, workplaces, theme parks, stadiums and airports. The device is an armband with embedded light sensors and a small computer processor. The armband is wrapped around a persons bare left arm above the elbow during use. The sensors first obtain pulsatile signals from blood flow over a period of three to five minutes. Once the measurement is completed, the processor extracts some key features of the pulsatile signals, such as pulse rate, and feeds them into a probabilistic machine learning model that has been trained to make predictions on whether the individual is showing certain signals, such as hypercoagulation in blood. Hypercoagulation is known to be a common abnormality in COVID-19 patients. The result is provided in the form of different colored lights used to indicate if an individual is demonstrating certain biomarkers, or if the result is inconclusive.

The clinical performance of the Tiger Tech COVID Plus Monitor was studied in hospital and school settings. The hospital study, which was considered a validation study, enrolled 467 asymptomatic individuals, including 69 confirmed positive cases, and demonstrated that the Tiger Tech COVID Plus Monitor had a positive percent agreement (proportion of the COVID-19 positive individuals identified correctly by the device to possess certain biomarkers) of 98.6% and a negative percent agreement (proportion of the COVID-19 negative individuals identified correctly by the device to not possess certain biomarkers) of 94.5%. The school study, which was considered a confirmatory study, showed similar performance.

The Tiger Tech COVID Plus Monitor is not a diagnostic device and must not be used to diagnose or exclude SARS-CoV-2 infection. The device is intended for use on individuals without a fever. An individuals underlying condition may interfere with the COVID-19 related performance of the device and could lead to an incorrect screening result.

The FDA issued the EUA to Tiger Tech Solutions, Inc.

The FDA, an agency within the U.S. Department of Health and Human Services, protects the public health by assuring the safety, effectiveness, and security of human and veterinary drugs, vaccines, and other biological products for human use, and medical devices. The agency also is responsible for the safety and security of our nations food supply, cosmetics, dietary supplements, products that give off electronic radiation, and for regulating tobacco products.

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03/19/2021

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Coronavirus (COVID-19) Update: FDA Authorizes First Machine Learning-Based Screening Device to Identify Certain Biomarkers That May Indicate COVID-19...

OAKLAND, Calif.--(BUSINESS WIRE)-- Dascena, Inc., a machine learning diagnostic algorithm company that is targeting early disease intervention to improve patient care outcomes, today announced the publication in Kidney International Reports of results from a study evaluating the companys machine learning algorithm, PreviseTM, for the earlier prediction of acute kidney injury (AKI). Findings showed that Previse was able to predict the onset of AKI sooner than the standard hospital systems, XGBoost AKI prediction model and the Sequential Organ Failure Assessment (SOFA), up to 48 hours in advance of onset. Previse has previously received Breakthrough Device designation from the U.S. Food and Drug Administration (FDA).

AKI is a severe and complex condition that presents in many hospitalized patients, yet it is often diagnosed too late, resulting in significant kidney injury with no effective treatments to reverse damage and restore kidney function, said David Ledbetter, chief clinical officer of Dascena. If we are able to predict AKI onset earlier, physicians may be able to intervene sooner, reducing the damaging effects. These findings with Previse are exciting and further demonstrate the role we believe machine learning algorithms can play in disease prediction. Further, with Breakthrough Device designation from the FDA, we hope to continue to efficiently advance Previse through clinical studies so that we may be able to positively impact as many patients as possible through earlier detection.

The study was conducted to evaluate the ability of Previse to predict for Stage 2 or 3 AKI, as defined by KDIGO guidelines, compared to XGBoost and SOFA. Using convolutional neural networks (CNN) and patient Electronic Health Record (EHR) data, 12,347 patient encounters were analyzed, and measurements included Area Under the Receiver Operating Characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advanced prediction of AKI onset. Findings from the study demonstrated that on a hold-out test set, the algorithm attained an AUROC of 0.86, compared to 0.65 and 0.70 for XGBoost and SOFA, respectively, and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window prior to onset.

About Previse

Previse is an algorithm that continuously monitors hospitalized patients and can predict acute kidney injury more than a full day before patients meet the clinical criteria for diagnosis, providing clinicians with ample time to intervene and prevent long-term injury.

About Dascena

Dascena is developing machine learning diagnostic algorithms to enable early disease intervention and improve care outcomes for patients. For more information, visit dascena.com

View source version on businesswire.com: https://www.businesswire.com/news/home/20210316005151/en/

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Dascena Announces Publication of Results From Its Machine Learning Algorithm for Prediction of Acute Kidney Injury in Kidney International Reports -...

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Global Machine Learning as a Service (MLaaS) Market 2021 Future Growth Prospect, Industry Report And Growing Demand Analysis Till 2025 | Microsoft,...