Speech/Silence segmentation for real-time coding via rule based adaptive endpoint detection

Lynch, John F.; Josenhans, James G.; Crochiere, R.

doi:10.1109/icassp.1987.1169516

Cited by 12 publications

(3 citation statements)

References 5 publications

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“…An algorithm proposed by Savoji [11], enhancing an algorithm originally proposed by Lynch et al [8], satisfies several of our criteria. The modified algorithm has the lowest complexity of the available research and still performs quite well.…”

Section: Silence Detection Algorithmsmentioning

confidence: 89%

“…It would create an effect of talking very quickly, since the syllables and words would run very closely to each other. Studies have shown that 99.56% of "continuous-speech" segments have periods of silence smaller than 150 ms [8]. For this reason, and using a 64-Kbps (8 KBps) signal as an example, removal of a block of silence smaller than 1200 bytes would reduce the quality of speech.…”

Section: Silence Detection In Multimedia Communication Environmentsmentioning

confidence: 99%

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Silence detection for multimedia communication systems

1999

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Silence detection and removal is an essential building block of any multimedia video conferencing system. It reduces the bandwidth requirements of the underlying network transport service and helps to maintain an acceptable end-to-end delay for audio. We analyze the requirements for a silence detection algorithm hosted on a multimedia communication system, and propose a novel low-complexity algorithm operating in the non-linear µ-law domain. After discussing the constraints which are imposed by the architecture of the system hardware (computer, packet-based network), we show that several recently proposed silence detection algorithms fail to meet all of these constraints. A new approach is then introduced, based on the small-and large-signal behavior of the speech waveform in the µ-law domain. The new algorithm is compared with a recent design that meets several of our requirements; experimental results indicate that it performs significantly better in the particular environment at hand.

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Section: Silence Detection Algorithmsmentioning

confidence: 89%

Section: Silence Detection In Multimedia Communication Environmentsmentioning

confidence: 99%

Silence detection for multimedia communication systems

1999

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show abstract

“…Additional knowledge about speech signals can be added to the algorithm so that speech can be differentiated from transient background sounds [27]. For example, speech must include breath pauses, and these occur with well known timing characteristics [25].…”

Section: Perceptuallymentioning

confidence: 99%