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Stefan Bayer

Researcher at Philips

Publications -  53
Citations -  1195

Stefan Bayer is an academic researcher from Philips. The author has contributed to research in topics: Audio signal & Encoder. The author has an hindex of 18, co-authored 52 publications receiving 1191 citations.

Papers
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Patent

Apparatus and Method for Encoding/Decoding an Audio Signal Using an Aliasing Switch Scheme

TL;DR: In this article, an apparatus for encoding an audio signal comprises the windower (11) for windowing a first block of the audio signal using an analysis window having an aliasing portion and a further portion, and a transformer (13) for converting the processed first subblock and the processed second sub-block from the different domain into a further different domain using the same block transform rule.
Journal Article

The ISO/MPEG Unified Speech and Audio Coding Standard—Consistent High Quality for All Content Types and at All Bit Rates

TL;DR: All aspects of this standardization effort are outlined, starting with the history and motivation of the MPEG work item, describing all technical features of the final system, and further discussing listening test results and performance numbers which show the advantages of the new system over current state-of-the-art codecs.
Patent

Low bitrate audio encoding/decoding scheme with common preprocessing

TL;DR: An audio decoder comprises a spectral domain decoding branch, an LPC-domain decoding branch and one or more switches for switching between the branches and a common post processing stage for post-processing a time-domain audio signal for obtaining a post-processed audio signal.
Patent

Audio transform coding using pitch correction

TL;DR: In this article, the audio signal is sampled within a first and a second frame of the sequence of frames, the second frame following the first frame, the sampling using information on a pitch contour of the first and the second frames to derive a first sampled representation.
Patent

Method and discriminator for classifying different segments of a signal

TL;DR: In this article, a short-term and long-term classification of audio and speech segments is performed by extracting at least one shortterm feature from the signal and a shortterm classification result is delivered.