In recent years, the proliferation of internet streaming applications has brought about the need for low-bit rate speech and audio coding methods. Several parametric designs such as the sinusoids+transients+noise model have been somewhat successful for speech and audio synthesis. Low-bit rate and streaming applications are restricted by only being able to transmit a limited number of parameters for loudness estimation, resulting in reduced audio quality. Current techniques for selecting relevant parameters are based on either Signal to Mask Ratio (SMR) or loudness patterns. Despite their popularity, an SMR focus has a tendency to neglect perceptually relevant sinusoids, and the loudness focused methods are computationally costly and aren?t proficient in delay?critical applications.
Researchers at Arizona State University have created a novel and efficient technique for estimating relevant perceptual quantities like loudness patterns of individual sinusoids. This routine is helpful for accurate parameter selection in low-bit rate speech and audio applications.
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Benefits and Advantages