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Prosoniq
 
Prosoniq Products is a German software company researching and developing new techniques for audio editing and digital signal processing. During the last eight years we have developed new methods and algorithms that allow new rolex replica and previously unthinkable signal manipulations giving you enhanced access to all specific features of a particular audio recording on your computer.

Based on the technology of Neural Network processing employed for sound evaluation and processing, traditional analysis methods like the FFT or related transforms including all the negative side effects become almost obsolete in our software. Prosoniq's unique Multiple Component Feature Extraction provides relatively unlimited access to distinct spectral properties as well as to the phase relation and exact frequency of harmonics, formants and temporal sound developments.

The term "Neural Network" is a descriptive synonym for a data structure derived from simplified models of "real", that is organic, connected nerve fibres. Biologists as well as computer scientists have learned that "biological computers", like for example the human brain, have the ability to find and classify even insignificant patterns in large, unstructured data sets. A human is able to recognize the face of a person he/she knows in a large crowd of unknown people within a very short time, even though that person may be viewed under unfavorable conditions, say, with the face half-covered by a hat. The human perception is also able to recognize a single person's face even if the facial expression may vary significantly, thus making a simple comparison of images by straightforward numerical methods impossible.

The difference between a computer processor and a human brain can also be described in terms of systems architecture: the computer processor is a (albeit presumably very powerful) single processor that processes many different kinds of mathematical operations within a very short time, while our brain consists of many zillions of processors, each with a very limited set of operations and also very limited processing speed, but interwoven in a very complex manner yet not fully understood. This is why a single-processor-system can be a genius in numerical calculations, while a Neural Network (a set of interconnected processors) makes up for the shortcoming of not being numerically exact by having the ability to recognize patterns and generalize "rules" out of a set of examples.

During the last 20 years or so, computer scientists have developed a growing interest in such Neural Networks and various computers have been built to study and develop techniques and practical applications on this basis. Since developing computer hardware and specific Neural Processors is a very time and money-consuming topic, scientists soon came up with the idea of simulating Neural Networks completely in software using standard computer systems. The basic idea of employing the technique of Neural Networks in software is to use a (necessarily very powerful) computer processor to simulate a complex system of interconnected nerve cells (processors) and to study the behaviour of varying network structures to external stimuli.

There are a number of practical solutions, most of which can be found in today's computer science applications: image and speech recognition, optical character recognition, weather forecast, quality measurement as well as even computer games and many more. Artificial Neural Networks, although many million times less powerful than the human brain, can do an extraordinarily good job in identifying and recognizing consistent patterns in a seemingly chaotic set of data.

New transformation methods (as e.g. Prosoniq's MCFE) that provide an increased resolution of audio signal characteristics are the basis of advanced audio-resolving algorithms that make our products powerful sound processing systems. Neural Networks can be used to represent the spectral contents of a sound in a far more precise and (from the view of the human ear and auditory system) "natural" way than "pure technical" procedures as e.g. FFT and related transforms do. Combined with a Neural Network, our products are capable of doing an extraordinary variety of audio signal modifications ranging from post-production of CD-recordings to achieve optimum fidelity to audio signal restoration of historical recordings or restoration from artifacts introduced through telecommunication paths. Both the development of new and fast transformation methods and training sequences for Neural Networks make our products lightning fast and will bring a vast number of algorithms to you in real time. Additionally, we have a specialized set of algorithms that cope with the task of audio signal separation, making it possible to remove environmetal noises (as e.g. broadband spectrum noise, room reflections and reverb a.s.f.) from a particular audio recording. We are continuously developing new algorithms that will accomplish the tasks emerging with the increasing importance of the digital domain in audio signal editing and telecommunication in the future.
 



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