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Lyra is a special new audio codec that uses learning toolstools for making high-quality calls and can handle unreliable internet connections.

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To put an end to the all-too-familiar intermittent automatic voice calls with competitive bandwidth, Google is offering open source Lyra along with an audio codec that uses learning devices to consistently provide high-quality calls when faced with questionable partnerships.

Google’s AI team provides developers with Lyra, which integrates their interaction with apps. As a result, the new tool allows voice calls to be made with the same superiority as the most widely used codecs in existence, while requiring 60% less bandwidth.

Audio codecs are now widely used for real-time Internet communications. This method involves compressing the new An input music file into a smaller packet that requires less bandwidth to transmit, and then decodes the file into a waveform that can be played through the listener’s call speaker.

The more compressed the file is, the less data is required to send the sound to the listener. But there is a trade-off: in general, the most highly compressed files can be even more difficult to recover, and can usually be decompressed into less understandable automatic speech signals.

“As a result, a constant hurdle to codec development for YouTube and audio is ensuring good quality, consuming less data, and minimizing real-time communication latency,” Andrew Storus and Michael Chainen, Google Systems Engineers, wrote in the post on site.

Engineers first presented Lyra with the possible solution of all equations longer than in February. Basically, Lyra works on top of regular audio codecs: the system consists of two parts, plus an encoder and a decoder. When the user speaks in his , the encoder identifies and extracts aspects of the user. speech, called features, that appear in blocks of 40 milliseconds and then compresses the sent data and the aforementioned network for the decoder to possibly read at a high level of the voice to the recipient. . Enter

To speed up the decoder, please note that Google’s AI engineers have equipped the method with a special machine learning model. An algorithm called generation and editing, trained on thousands of hours of related data, is able to completely reconstruct an audio file using a limited set of most features.

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While traditional codecs can only generate information from parameters to recreate a type of sound, a system generator can read functions and generate replacement sounds based on a small amount of data.

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In recent years, generative models have become the subject of most research, and various companies are showing interest in these methods. Engineers have already developed state-of-the-art devices, starting with DeepMind’s WaveNet which generates speech that mimics the human voice.

Equipped with any typem model that reconstructs audio using less data, so lyra can handle highly compressed files at extreme bitrates while delivering high-quality ads on the other end of the line.

Storus Chinen and compared the performance of Lyra with Opus, the best open source codec designed for most Internet voice applications.

When Opus is in a high bandwidth environment with 32 kbps noise, Opus delivers speech quality that is indistinguishable from the original; However, when working in environments with limited bandwidth up to 6 Kbps, the codec shows deterioration in the quality of the song.

For comparison, Lyra compresses raw audio at up to 3 kbps. Based on the opinions of crowdsourcing experts and listeners, the researchers found that the quality of the musical output compares well with all other parts. At the same time, additional codecs capable of operating at comparable bitrates, such as Lyra and Speex, all performed the worst, characterized by unnatural and r Botized voices.

“Lyra should be used wherever bandwidth limits for excessive bitrates are not sufficient, and existing low-bitrate codecs simply don’t provide adequate quality,” said Storus Chinen et al.

This idea will appeal to the vast majority of internet users who have experienced insufficient bandwidth while working from home during the COVID-19 pandemic, especially last year.

Demand for broadband services has skyrocketed since the crisis began, with some carriers experiencing a 60% increase in internet traffic compared to all of last year, causing network congestion or something like that. freezing the conference call.

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However, even before the start of the COVID-19 crisis, some users are currently experiencing unreliable internet speeds: in the UK, for example, several hotels are still unable to find ultra-fast broadband.