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The Role of Automatic Speaker Recognition in the Investigative Process

The Role of Automatic Speaker Recognition in the Investigative Process
forensicmag.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from forensicmag.com Daily Mail and Mail on Sunday newspapers.

Petr Schwarz , Radim Kudla , National Institute Of Standards , Technology Speaker Recognition Evaluation , Neural Networks Dnns , Automatic Speaker Recognition , Deep Neural Networks , Log Likelihood Ratio , Probability Density Function , Detection Error Tradeoff , Acceptance Rate , Rejection Rate , National Institute , Equal Error Rate , தேசிய நிறுவனம் ஆஃப் தரநிலைகள் , ஆழமான நரம்பியல் நெட்வொர்க்குகள் , ஏற்றுக்கொள்ளுதல் ரேட் , தேசிய நிறுவனம் , சமம் பிழை ரேட் ,

Sensors | Free Full-Text | Quantization and Deployment of Deep Neural Networks on Microcontrollers

Embedding Artificial Intelligence onto low-power devices is a challenging task that has been partly overcome with recent advances in machine learning and hardware design. Presently, deep neural networks can be deployed on embedded targets to perform different tasks such as speech recognition, object detection or Human Activity Recognition. However, there is still room for optimization of deep neural networks onto embedded devices. These optimizations mainly address power consumption, memory and real-time constraints, but also an easier deployment at the edge. Moreover, there is still a need for a better understanding of what can be achieved for different use cases. This work focuses on quantization and deployment of deep neural networks onto low-power 32-bit microcontrollers. The quantization methods, relevant in the context of an embedded execution onto a microcontroller, are first outlined. Then, a new framework for end-to-end deep neural networks training, quantization and deploymen ....

Ambiq Apollo , Quantization Of Deep Neural Networks , Deployment Of Quantized Neural Network , Neural Network , Neural Networks , Human Activity Recognition , Floating Point Unit , Tensorflow Lite , Sparkfun Edge , Embedded Execution , Quantized Neural , Statistics Aware Weight Binning , Learned Step Size Quantization , Differentiable Quantization , Deep Neural Networks , Gaussian Mixture Models , Instruction Set Architecture , Rectified Linear Unit , Deep Neural , Fixed Point Quantization , Per Filter Scale , Quantization Aware Training , Flow Lite , நரம்பியல் வலைப்பின்னல் , நரம்பியல் நெட்வொர்க்குகள் , மனிதன் நடவடிக்கை அங்கீகாரம் ,

Detecting drones in cities is tough – Duke engineers say machine learning can help

by Ken Kingery May 20, 2021 .
DURHAM – Look, up in the sky! It’s a bird! It’s a plane! It’s…actually pretty easy for radar to tell the difference. Flying aliens from Krypton notwithstanding, there are simply not many things moving through the mostly empty, wide-open skies that are as big and fast as an airplane.
But if radar signals move down from the clouds and into a city’s streets, there are suddenly many objects that can be mistaken for one another. With only distance, speed and direction to go on, drones can easily be “hidden in plain sight” on radar displays among slowly moving cars, bicyclists, a person jogging or even the spinning blades of an air conditioning unit. ....

Duke Gardens , United States , Jeffrey Krolik , Helen Li , Clare Boothe Luce , Duke Forrest , Defense Advanced Research Projects Agency , Duke University , Neural Networks , World War , Deep Neural Networks , Science Drive , Clare Boothe Luce Professor , Computer Engineering , Graphics Processing Units , Field Programmable Gate Arrays , டியூக் தோட்டங்கள் , ஒன்றுபட்டது மாநிலங்களில் , ஹெலன் லி , தெளிவு பூத் லூஸ் , டியூக் காடு , டியூக் பல்கலைக்கழகம் , நரம்பியல் நெட்வொர்க்குகள் , உலகம் போர் , ஆழமான நரம்பியல் நெட்வொர்க்குகள் , அறிவியல் இயக்கி ,

Bringing Radar Down From Clouds to City Streets

Date Time
Bringing Radar Down From Clouds to City Streets
Look, up in the sky! It’s a bird! It’s a plane! It’s…actually pretty easy for radar to tell the difference. Flying aliens from Krypton notwithstanding, there are simply not many things moving through the mostly empty, wide-open skies that are as big and fast as an airplane.
But if radar signals move down from the clouds and into a city’s streets, there are suddenly many objects that can be mistaken for one another. With only distance, speed and direction to go on, drones can easily be “hidden in plain sight” on radar displays among slowly moving cars, bicyclists, a person jogging or even the spinning blades of an air conditioning unit. ....

Duke Gardens , United States , Jeffrey Krolik , Helen Li , Clare Boothe Luce , Duke Forrest , Defense Advanced Research Projects Agency , Hardwiringa Neural Network , Duke University , Neural Networks , World War , Deep Neural Networks , Science Drive , Clare Boothe Luce Professor , Computer Engineering , Graphics Processing Units , Field Programmable Gate Arrays , Machine Learning , டியூக் தோட்டங்கள் , ஒன்றுபட்டது மாநிலங்களில் , ஹெலன் லி , தெளிவு பூத் லூஸ் , டியூக் காடு , டியூக் பல்கலைக்கழகம் , நரம்பியல் நெட்வொர்க்குகள் , உலகம் போர் ,

Deep Neural Networks can be made more human-like by training with large datasets: IISc study

Image for representational purpose (Pic: PTI)
Researchers from the Indian Institute of Science (IISc) in their study have found crucial qualitative differences between the human brain and Deep Neural Networks, and these gaps can be filled by training the deep networks on larger datasets, incorporating more constraints or by modifying network architecture.
The team from the Centre for Neuroscience (CNS) studied 13 different perceptual effects and found that Convolutional or deep neural networks that have their object representations match coarsely with the brain are still outperformed by humans. Lots of studies have been showing similarities between deep networks and brains, but no one has really looked at systematic differences, said SP Arun, Associate Professor at CNS and senior author of the study in a note by the institute. Identifying these differences can push us closer to making these networks more brain-like, he added. ....

Georgin Jacob , Nature Communications , Indian Institute Of Science Iisc , Neural Networks , Centre For Neuroscience , Indian Institute , Deep Neural Networks , Associate Professor , Harish Katti , இயற்கை தகவல்தொடர்புகள் , இந்தியன் நிறுவனம் ஆஃப் அறிவியல் இஸ்க் , நரம்பியல் நெட்வொர்க்குகள் , மையம் க்கு நரம்பியல் , இந்தியன் நிறுவனம் , ஆழமான நரம்பியல் நெட்வொர்க்குகள் , இணை ப்ரொஃபெஸர் ,