What Are Headphone Drivers and How They Effect Sound Quality

For in-ear headphones, there is always talk of dynamic drivers and balanced armature drivers. Today we deal with the topic and show the differences. If the in-ear listener sits properly in the ear canal and thus "closes" well, the space between the eardrum and the membrane is closed and very small. The whole thing then works like a kind of spring system (or "push-pull mechanism") and the membrane can move the eardrum well with little deflection and little energy, ensuant in a very good bass upshot. As soon as there is a leak in this system, this is immediately noticeable by the fact that low frequencies are lost (as is the case with ear buttons). This is because the human ear is less sensitive to low frequencies (below about 150 Hz) than to higher frequencies.

So if we want to hear low frequencies better then a lot of energy has to be applied to amplify them. When using loudspeakers, low frequencies are still physically noticeable. This is not the case with headphones. Also, speaker diaphragms are larger and more stable (thicker material), which allows much more air to be set in motion than headphones. In order to be able to make the best possible use of the low energy that the headphone system develops, care must be taken to ensure that the headphones or in-ear headphones are optimally terminated.

Ok! Which driver was used certainly determines how well the in-ear headphones sound. In the development of the drivers most of the money usually flows in the production of in-ear headphones.
 

What are balanced armature drivers?

 
Balanced Armature Drivers (BA) are often used only in in-ear headphones in the higher price segment. Balanced armature drivers are made to sound particularly good in a certain frequency range, such as: As the heights, which is why in-ear headphones with Balanced Armature drivers often several drivers installed. For example, the Sony XBA 3 iP incorporates 3 Balanced Armature drivers, which ensure that the entire sound spectrum is covered.
 

Advantages of Balanced Armature Driver

 

• You can make a frequency range sound great
• The sound sounds more detailed
• The sound plays faster
• The treble sounds clearer than dynamic drivers
• They are smaller than in-ear headphones with dynamic sound transducers and weigh less
• They need less power than dynamic drivers

 

Disadvantages of Balanced Armature drivers

 

• The bass is weaker than dynamic drivers
• In-ear headphones with Balanced Armature drivers are more expensive
• Often several drivers are necessary to cover all frequency ranges

 

What are dynamic drivers?

 
Dynamic drivers in in-ear headphones make it possible for in-ear headphones to be offered at a good price. Unlike Balanced Armature drivers, only one driver covers the entire sound spectrum. They work on the same principle as loudspeaker boxes.
Advantages of dynamic drivers

• Cheaper than Balanced Armature sound transducer
• Bass frequency is better
• The sound signature is better coordinated
• Often they are also more robust than Balanced-Armature drivers

 

Disadvantages of dynamic drivers

 

• No detailed sound like Balanced Armature drivers
• The heights are not that clear in comparison
• They weigh more and are bigger too

 

Balanced Armature Drivers and Dynamic Drivers

 
In some in-ear headphones both types of drivers are worn, such. B. the Sony XBA H3. The advantage of having multiple types of drivers is that the bass and treble sound great, but the case is usually larger and they weigh more.

Moving Armature driver

Moving Armature drivers are new drivers that combine the benefits of Balanced Armature drivers and dynamic drivers. Moving Armature drivers work like Balanced Armature drivers but have the advantage that the entire frequency range is covered in contrast to Balanced Armature drivers where multiple drivers are needed, the Moving Armature driver in-ear headphones only need a driver. However, very few models use this type of driver so far and they are also quite expensive.

The classic and most commonly encountered headphone driver is - as with speakers - the electrodynamic principle assign.

However, in order to map the entire frequency spectrum as accurately as possible, partially modified drivers are used, such as the Variomotion technology from AGK (depending on the frequency position, a larger or smaller part of the diaphragm swings) or the ring driver of the Sennheiser HD800.

Google Cloud Natural Language API

Google’s Cloud Natural Language API

Cloud Natural Language API, established by Google is said to provide customers with language analyser which according to the company `reveals the structure as well as the meaning of your text. The public beta launch of Cloud natural language API is a new service giving developers access to Google-powered emotion analysis, entity, recognition together with grammar analysis.

 Some of this tends to gauge believing some words positive and the others negative. When observed by Motherboard it was found that the analyser of Google interpreted some words like homosexual to be negative. This is evident that the API that tends to judge depending on the information it has been fed, now seems to give out partial analysis. The tool has been developed to provide companies with a preview on how their language would be expected.

Editing complete sentences would provide predictive analysis on each word and as the overall statement on a negative to positive scale, respectively. AI systems have been trained in utilisation of texts, media as well as books given to it.

Whatever Cloud Natural Language API consumed to form its criteria in assessing English text for sentiment, it influenced the study to negative attribution of certain descriptive terms. No confirmation has been provided by Google to Motherboard as to the body of the text fed to the Cloud Natural Language API.
 
API Connects Other Pre-Trained Machine Learning API
 
Once it begins to engage content from the outside world even if begins with a remote set of contents to comprehend sentiments, it tends to get polluted with the negative word connections found in it. A confirmation had been given by Google to Motherboard that its NLP API had produced biased results in a statement.

There had been clear parallels with the ill-fated as well as impressionable AI chatbots Tay of Microsoft, which had been rapidly, pulled offline by the company in March 2016 after the users of Twitters had taught it to be a shockingly racist as well as sexist conspiracy philosopher.

In July, Google had tried once again with its bot Zo which had learned the same horrible habits form human and had to be quickly shut down. The new API connects the other pre-trained machine-learning API of Google such as the Cloud Speech API which has been made available in public beta, together with the Vision API and the Translate API.
 
Assist Text in English/Spanish/Japanese
 
The latest Cloud Natural Language API presently tends to assist texts in English, Spanish and Japanese. The purpose of Google here is to provide a service which could meet the scale as well as the performance essential for developers and enterprises in a comprehensive range of industries. Providing API for sentiment analysis and entity recognition is not new where services like Thomson Reuters Open Calais have been providing assistance for entity recognition for around ten years now. Sentiment analysis is also not a new concept. On the other hand, grammar analysis API which tend to classify parts of speech and develop dependency analyse trees are not as extensively available still. It would be interesting to know how developers would be utilising these apps though it is easy to see how the same could be utilised to power chat bots for instance and support them in comprehending incoming request.

Researchers Developed Flexible Photonic Devices

Photonics will have a direct impact on many areas of our daily life. Soon photonics will be fundamental, both for the improvement or replacement of existing processes and for the development of new solutions and new products.

On the other hand, society demands products with better and better features: new functionalities and improved properties, lightweight, flexible photonic devices, and capable of adapting to different materials and surfaces. Likewise, these developments must be competitive and not increase the price of the final product.

A team of MIT Associate professor Juejin Hu from the University of Central florida, China and France has developed a new method of making light based photonic devices. These special flexible photonic devices is made from a kind of glass called Chalcogenide. This specialized kind of glass material has a great flexible property which can be bend and strech to the very large extent without any damage. These flexible photonic devices can be used in field of biomedical sensors and flexible connectors in Electronics.

How about a device that can simultaneously detect blood oxygen level, heart rate and blood pressure? Yes, these flexible photonic devices of optical technology which are made from the strechy and bendable material can be mounted in skin to monitor the condition.

By using these kinds of new light based flexible photonic devices, we can stave off the condition for the conversion process. Because, if the original data is light based is having the advantages for a lot of applications.

The current photonic devices applied in the field are made up of rigid materials on rigid matters thus rises an intrinsic counterpart. The polymer based softmaterials is having a less refractive index tracks to not so good ability to circumscribe a light beam. To confront this issue, the team of MIT researchers have developed a stiff material that can stretch and bend which is almost like a Rubber. Its confuguration is like a spring made from a polymer matter has no noticeable abjection in its optical performance.

Other flexible photonic devices that are made by implanting nanorods of a rigid substrates in a polymer base need extra developmental steps. And hence they are not congruous with current systems. These flexible photonic devices can also be used for applications where the devices require to adapt to the rippled surfaces of some other material. But optics technology is extremely sensitive to strain, thus can observe deformations of lower than one hundredth of one percent.

This team recently has formulated a way of segregating layers of photonics, made of chalcogenide and graphene with customary semiconductor photonic electronic equipment. Current method of segregating such material need them to be made on a surface and then take off and tranfern to a semiconductor thin layer. This process is very difficult. But the new procedure permits the layers to be fancied directly on the surface of a semiconductor. This process no need a special temperature condition for the entire process and thus allows very simplified fabrication and more punctilious coalition.

This team of MIT researchers have confirmed very soon they develop this new technology of flexible photonic devices to reach commercially.

Introducing the New Firefox Quantum Web Browser with Double Speed

Amazingly faster performance with lower RAM usage is the key highlight of Firefox’s Quantum Browser

 
Once the web browser world was ruled by the Internet Explorer then came the Google Chrome which simply established itself as the numero uno with no major threats. Firefox is waging the war to dethrone the Chrome from the top position and to regain its dominance in the arena but it wouldn’t be a cake walk. Firefox is lying just at the bottom of the major popular web browsers by amounting just 9.1 market share and running right below the Microsoft Edge and Safari browser. Mozilla is looking forward to change its fortune with the launch new improved and faster Firefox Quantum browser.
Quantum translates into a fast browser

The Firefox Quantum browser comes with a built-in technology running on the 64 bit but at the same time it makes use of 30 per cent lesser RAM than other conventional browsers. In this way Firefox is competing right against the Chrome by offering better performance by lesser utilization of the map but this isn’t being communicated with the target audience with a massive PR strategy.

Firefox has went with a major overhaul for its browser by improvising not just the user interface but also the browser engines which power the search results. The new engine powering the Firefox Quantum browser comes has the ability to work in parallel manner which make sit amazingly faster than the earlier version of the web-browser due to the user of the latest Servo engine. Mozilla has even stated that this Servo engine will be able to offer support for the Mixed Reality features and functionalities which are expected to be next big thing in the technology world.
 

Bigger change for better browsing

 
This new browser has also lowered the user interface gradients to bring better visibility to the users. Microsoft Edge browser made a bad decision getting rid of the gradients which made it extremely difficult to navigate through the browser but that aren’t the case with Firefox’s browser. On the benchmark test this browser glides like a charm and comes with better numbers than all the browsers in the competition. The Speedometer benchmark gave this browser a score of mighty 70 compared to the 45 which the pre-Quantum Firefox browser came up with. Using the JetStream benchmark tests everyone was surprised with the 151 score achieved by the Firefox Quantum when compared to the Google Chrome’s 144 score.

All the benchmark testing and normal day-to-day usage suggests that this Firefox browser is way faster and snappier than other browser available in the market. Firefox has fixed all the bugs related to performance as well as responsiveness to enhance the overall user experience. Quantum browser certainly appears to be a major upgrade over the earlier version of the Firefox and it is right step towards bringing better browsing experience to the users. Now it all depends on the users whether they adopt the Firefox Quantum browser to increase its market share and give a stiff completion to the omnipotent domination of the Google Chrome.

AI Image Recognition Fooled By Single Pixel Change

Adversarial Model Images

According to research, computers can be misled into thinking that an image of a taxi can be a dog by only altering one pixel. These limits developed from the methods that Japanese function in tricking the extensively utilised AI-based image recognition system.

Several of the other scientists tend to now develop `adversarial’ model images to reveal the fragility of some kinds of recognition software. Experts have cautioned that there is no quick and easy means of fixing image recognition system of stopping them from being duped in this manner.

Su Jiawei together with colleagues at Kyushu University, in their research had made small alterations to plenty of the images which were then analysed by extensively utilised AI-based image recognition systems. All the systems that had been tested had been based on a kind of AI known as deep neural networks.

These systems usually tend to learn on being trained with plenty of various examples for the purpose of providing them with an intellect of how objects such as dogs and taxis tend to vary. It was observed by the researchers that altering one pixel in about 74% of the test images made the neural nets mistakenly label what they saw.

Designed – Pixel Based Attacks

A variety of pixel based attacks had been designed by Japanese researchers which had caught all the state-of-the-art image recognitions system that had been investigated. Mr Su from Kyushu leading the research had commented that as far as they were aware there was no data-set or network which is more robust than others.

 Several other research groups all over the world have been now developing `adversarial examples’ which tend to reveal the flaw of these systems according to Anish Athalye from the Massachusetts institute of Technology – MIT who has been dealing with this issue. A specimen made by Mr Athalye together with his team is a 3D printed turtle that one image classification system insists on labelling a rifle.

He informed BBC that more and more real world schemes have begun to incorporate neural networks and tends to be huge concern which these schemes could be possible to destabilize or attack utilising adversarial examples. He stated that though there had been no instances of malicious attacks in real life, the fact that these apparently smart schemes could be deceived with ease was a matter of concern.

Methods of Resisting Adversarial Exploitation

Web giants comprising of Facebook, Amazon as well as Google seems to be known for investigating methods of resisting adversarial exploitation. He stated that it is not some strange `corner case’ and it has been shown in their work that one can have a single object which steadily fools a network over viewpoints, even in the physical world.

He further added that the machine learning community do not tend to comprehend completely what seems to be going on with adversarial examples or why they seem to exist. Learning system established on neural network tends to involve creating links between large numbers of nodes such as nerve cells in a brain.

Analysis involves the network creating plenty of decision regarding what it tends to see and every decision should lead the network nearer to the accurate answer.