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Audio recognition
By Max Babych
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Recognition of sound signals

As we have said earlier in our articles with the growing of queries to develop automated systems to help business optimize its work and with the evolution of technologies the system of recognition become more and more popular, useful and required. Thus we can tell not only about video and image recognition but also about audio recognition which is also quite needed in modern sphere of web business and applications. In other words everywhere where you have to interact with music or to communicate with some device with your voice.

Audio recognition systems are built with the help of the same principles that is used when image or video recognition software is being developed. There also are some previously uploaded patterns with which system compares input audio data from outer sources. That is what it is if to talk about it generally. Pattern recognition of audio has the same level of requirement in the business sphere and in technologies because people become more concerned about their comfort. Voice commands provide this comfort to users of different kind of software because it reduces the number of manual actions and gives high level of automated work.

How audio distinction works

Recognition of impulsive sound signals is just one big half of all specters of recognition technologies. It is used primarily when we talk about defining what someone has said. Systems listens to a sound signal in order to recognize what was said, what the impulse was given. Then there is a process of filtering out any hash that could be there. It is worth to say that in such cases software tries to set a frequency and to define a sound peak that are used by speaker source and then to built a spectrogram which is near the last step to finally recognize what was said by someone. And do not forget about comparing with patterns in systems which is also required in order to read on machine language what was said. And of course such software uses an determinant of amplitude  which is used when we talk about setting a frequency before further stages of recognition.

The most known examples of this type are Google’s voice recognition system, Apple’s Siri or voice command system in autonomous cars of five-level autonomy.  When you say “OK, Google” program starts almost the same algorithm which was described earlier. It also listens to your voice, sets its basic features and then build special  data dimension processes according to the methodology that gives it an opportunity to identify what users said and what he want to see on his screen. Siri also has almost similar algorithm of work but maybe a little bit better because uses another technologies in the field of artificial intelligence. Microsoft has developed the same type of voice recognition system  in Microsoft Windows 10 named Cortana. It also a helper which can hear you and understand.

Areas of using sound recognition technologies

Finally, the most useful area of applying such type of recognition is developing advanced driving assistance systems. As they are fully autonomous and do not allow human to drive car owner need a way to talk with a car. So audio recognition systems is best variant for such goals. Car user can not only set a route for a car just by his voice but also ask car to find any information in Internet to make a ride more interesting.

Second area of such type of recognition is audio fingerprinting software. This is a technology which provides people with an ability to recognize some stable elements of audio information for example song. The technology of such type seems to be like a sensor technology of scanning human fingerprint, that is why it named so. Acoustic fingerprint algorithm depends on recognition of two sound elements which sound alike to human ear even if they have offset in binary representation. However, system define the value of these changes. This technology is like a technology of recognition of human fingerprints where small difference between pattern and input is a normal situation and input data have to match to match to pattern.

While developing audio fingerprinting python programming language can be used because it passes to this goal in the best way. The most known example of implementing of such technology is Shazam application. It compare audio information from source with pattern and even if there is a small difference in binary channel it conclude that the sources are matched.

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