A new distortion measure for spectral quantization based on the LSF intermodel interlacing property

Lee, Mi Suk; Kim, Hong Kook; Lee, Hwang Soo

doi:10.1016/s0167-6393(00)00080-7

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…From the basic LSP derivation formulas, the peak frequencies in the speech spectrum are found near the adjacently located LSPs, whereas relatively flattened valley frequencies are located around the isolated LSPs [12,13]. To validate this property of LSP parameters, we compared the spectrum of a speech frame with the location of LSPs.…”

Section: Modification Of the Comb Filter Using Line Spectral Pairmentioning

confidence: 99%

Unsupervised noise reduction scheme for voice-based information retrieval in mobile environments

Park

Jang

Kim

et al. 2013

Multimed Tools Appl

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This study proposes an unsupervised noise reduction scheme that improves the performance of voice-based information retrieval tasks in mobile environments. Various types of noises could interfere with speech processing tasks, and noise reduction has become an essential technique in this field. In particular, noise reduction needs to be carefully processed in mobile environments based on the speech coding system and the client-server architecture. In this study, we propose an effective noise reduction scheme that employs the adaptive comb filtering technique. A way of directly using several codec parameters during the filtering process is also investigated. In particular, we modify the conventional comb filter using line spectral pair parameters. To verify the efficiency of the proposed noise reduction approach, we conducted speech recognition experiments using the Aurora2 database. Our approach provided superior recognition performance under various noise conditions compared to the conventional techniques.

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Section: Modification Of the Comb Filter Using Line Spectral Pairmentioning

confidence: 99%

Unsupervised noise reduction scheme for voice-based information retrieval in mobile environments

Park

Jang

Kim

et al. 2013

Multimed Tools Appl

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“…As adjacent LSFs become closer, for example around 500 Hz,   decreases and hence becomes more resonant around those frequencies [2,3] . However, when a single LSF is isolated, far from its neighbors, By the definition of discrete Fourier transform, the impulse response of  at frequency  is expressed…”

Section: Equation 1 Is Equivalently Expressed In the Frequency Domain Asmentioning

confidence: 99%

“…This paper proposes a novel procedure for noise spectral magnitude estimation which also eliminates the use of VAD in a very efficient manner. From the basic LSF derivation formulae it is observed that the local maxima of LPC spectra are near the adjacently located LSFs [2,3] , and relatively flattened valleys across frequency are around the isolated LSFs. In the proposed method the spectral magnitudes at LSFs are considered as representatives of the peaks and valleys of the corresponding LPC spectra, and participate in estimating noise spectral magnitude.…”

Section: Introductionmentioning

confidence: 99%

Noise Spectrum Estimation Using Line Spectral Frequencies for Robust Speech Recognition

Jang¹,

Park²,

Kim³

2012

The Journal of the Acoustical Society of Korea

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This paper presents a novel method for estimating reliable noise spectral magnitude for acoustic background noise suppression where only a single microphone recording is available. The proposed method finds noise estimates from spectral magnitudes measured at line spectral frequencies (LSFs), under the observation that adjacent LSFs are near the peak frequencies and isolated LSFs are close to the relatively flattened valleys of LPC spectra. The parameters used in the proposed method are LPC coefficients, their corresponding LSFs, and the gain of LPC residual signals, so it suits well to LPC-based speech coders.

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“…From the basic LSF derivation formulae, it is observed that the peak frequencies of LPC spectra are found near the adjacently located LSFs, whereas relatively flattened valley frequencies are located around the isolated LSFs [9] [10]. In other words, the spectral magnitudes at LSFs are considered to be representative of the peaks and valleys of the corresponding LPC spectra.…”

Section: Noise Suppression Based On Line Spectral Frequenciesmentioning

confidence: 99%

Untitled

2011

AECE

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1 Abstract-This paper proposes a noise suppression technique for speech-centric interface of various smart devices. The proposed method estimates noise spectral magnitudes from line spectral frequencies (LSFs), using the observation that adjacent LSFs correspond to peak frequencies of spectrum, whereas isolated LSFs are close to flattened valley frequencies retaining noise components. Over a course of segmented time frames, the logarithms of spectral magnitudes at respective LSFs are computed, and their distribution is then modeled by the Rayleigh probability density function. The standard deviation from the Rayleigh function approximates the noise spectral magnitude. The model is updated at every frame in an online manner so that it can deal with real-time inputs. Once the noise spectral magnitude is estimated, a time-domain Wiener filter is derived for the suppression of the estimated noise spectral magnitude, and this is then applied to the input noisy speech signals. Our proposed approach operates well on most smart devices owing to its low computational complexity and real-time implementation. Speech recognition experiments, conducted to evaluate the proposed technique, show that our method exhibits superior performance, with less distortion of original speech, when compared to conventional noise suppression techniques.

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A new distortion measure for spectral quantization based on the LSF intermodel interlacing property

Cited by 8 publications

References 12 publications

Unsupervised noise reduction scheme for voice-based information retrieval in mobile environments

Unsupervised noise reduction scheme for voice-based information retrieval in mobile environments

Noise Spectrum Estimation Using Line Spectral Frequencies for Robust Speech Recognition

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