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Additionally, WaveNetEQ, the generative model-based packet-loss-concealment system currently used in Duo, has demonstrated how this technology can be used in real-world scenarios. DeepMind’s WaveNet was the first of these generative models that paved the way for many to come. These shortcomings have led to the development of a new generation of high-quality audio generative models that have revolutionized the field by being able to not only differentiate between signals, but also generate completely new ones. However traditional parametric codecs, which simply extract from speech critical parameters that can then be used to recreate the signal at the receiving end, achieve low bitrates, but often sound robotic and unnatural. In this sense, Lyra is very similar to other traditional parametric codecs, such as MELP. On the other end, a generative model uses those features to recreate the speech signal. The features themselves are log mel spectrograms, a list of numbers representing the speech energy in different frequency bands, which have traditionally been used for their perceptual relevance because they are modeled after human auditory response. Features, or distinctive speech attributes, are extracted from speech every 40ms and are then compressed for transmission. The basic architecture of the Lyra codec is quite simple.
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To do this, we’ve applied traditional codec techniques while leveraging advances in machine learning (ML) with models trained on thousands of hours of data to create a novel method for compressing and transmitting voice signals. Quote Lyra is a high-quality, very low-bitrate speech codec that makes voice communication available even on the slowest networks. Wanted: damaged sample files (lossless, lossy)
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