Noise reduction and echo cancellation front-end for speech codecs

Basbug, F.; Swaminathan, K.; Nandkumar, S.

doi:10.1109/tsa.2002.807350

Cited by 17 publications

(16 citation statements)

References 9 publications

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“…3 The polarization component perpendicular to the POI is denoted by ⊥, while denotes the component parallel to the POI. The reflectance coefficients [21] from each interface of the window are…”

Section: Image Formation Modelmentioning

confidence: 99%

“…The corresponding PSFs h ⊥ r and h ⊥ t are derived analogously, by using R ⊥ and T ⊥ instead of R , T in Eqs. (3,4).…”

Section: Image Formation Modelmentioning

confidence: 99%

“…In analogy to the displaced replica in our study, a sound reflection creates a delayed echo. In the field of acoustics [3], this effect is generally referred to as reverberations. Hence, we use the term visual reverberations to describe the effect we deal with.…”

Section: Introductionmentioning

confidence: 99%

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Overcoming visual reverberations

Diamant

Schechner

2008

2008 IEEE Conference on Computer Vision and Pattern Recognition

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Section: Image Formation Modelmentioning

confidence: 99%

“…The corresponding PSFs h ⊥ r and h ⊥ t are derived analogously, by using R ⊥ and T ⊥ instead of R , T in Eqs. (3,4).…”

Section: Image Formation Modelmentioning

confidence: 99%

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Overcoming visual reverberations

Diamant

Schechner

2008

2008 IEEE Conference on Computer Vision and Pattern Recognition

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“…noise suppression algorithms) are based on methods such as linear and nonlinear spectral subtraction, [1], [2], Wiener filtering, [3], the wavelet transform, [4], and other methods (cf. [3], [5], [6], [7]). …”

Section: Introductionmentioning

confidence: 99%

Fixed-Point Implementation of Noise Reduction Using StarCore¿ SC3400

Dyba¹,

Su²,

Deng³

2009

2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop

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The presence of relatively high-level background noise in a telecommunication channel may lower the perceived voice quality of speech signals as well as degrade in-band signaling. The challenge is to reduce the noise to a satisfactory level while minimizing the use of computational resources.This paper describes an ef cient noise reduction algorithm and its implementation on a high-performance Digital Signal Processor (DSP) based on the Freescale StarCore TM SC3400 core.The NR implementation methodology takes advantage of the StarCore TM architecture and Code Warrior TM development tools to reduce engineering efforts. After performing code optimization by exploring a mix of highlevel and machine-level languages, the NR computational cost is reduced to approximately 0.6 Millions of Cycles Per Second for the narrow-band applications (i.e., G.711 with 8kHz sampling rate).The algorithm has been evaluated using different approaches, including subjective evaluation by expert listeners. An overall noise reduction of 10-12dB (for standard setting of 13dB noise reduction threshold) has been achieved for most natural-speech signals polluted with stationary noise. The noise reduction component has also been evaluated using wideband signals (16kHz sampling rate). The machine cycle count increased to 1 MCSP (approximately) while the overall noise reduction of 9-12dB was achieved without observing adverse side effects related to voice quality.

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“…These systems often benefit from using voice activity detectors (VADs) which are frequently used in such application scenarios for different purposes. Speech/non-speech detection is an unsolved problem in speech processing and affects numerous applications including robust speech recognition (Karray & Martin, 2003), (Ramírez et al, 2003), discontinuous transmission (ETSI, 1999), (ITU, 1996), estimation and detection of speech signals (Krasny, 2000), real-time speech transmission on the Internet (Sangwan et al, 2002) or combined noise reduction and echo cancellation schemes in the context of telephony (Basbug et al, 2003). The speech/non-speech classification task is not as trivial as it appears, and most of the VAD algorithms fail when the level of background noise increases.…”

Section: Introductionmentioning

confidence: 99%

New Advances in Voice Activity Detection using HOS and Optimization Strategies

Ramírez¹

2007

Robust Speech Recognition and Understanding

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Noise reduction and echo cancellation front-end for speech codecs

Cited by 17 publications

References 9 publications

Overcoming visual reverberations

Overcoming visual reverberations

Fixed-Point Implementation of Noise Reduction Using StarCore¿ SC3400

New Advances in Voice Activity Detection using HOS and Optimization Strategies

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