Improved 1-Bark bandwidth auditory filter

Sekey, A.; Hanson, Brian A.

doi:10.1121/1.390954

Cited by 24 publications

(7 citation statements)

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“…The critical band rate is expressed in Barks [25]. For computing the perceptual spectral distance, a total number of 29 1-Bark bandwith bandpass filters are used.…”

Section: Measurement Methodologymentioning

confidence: 99%

Effectiveness of a periodic and aperiodic decomposition method for analysis of voice sources

d'Alessandro

Darsinos

Yegnanarayana

1998

IEEE Trans. Speech Audio Process.

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Decomposition of speech into periodic and aperiodic components is useful in analyzing and describing the characteristics of voice sources. Such a decomposition is also useful in controlling the excitation source for synthesis. This paper addresses the issue of decomposition of speech into periodic and aperiodic components in the context of speech production. The effectiveness of a recently proposed algorithm for decomposing speech into these components is examined for analysis of voice sources. Synthetic signals are generated using formant synthesis. Different sources of aperiodicity encountered in normal speech production are considered, using a set of parameters to control the synthetic signals. The sources of aperiocity studied are 1) additive pulsed or continuous random noise, and 2) modulation aperiodicities due to variation in the fundamental frequency, jitter, and shimmer. Three types of measures are used to characterize these voices: ratio of energies in the periodic and aperiodic components, perceptual spectral distance, and spectrograms. The results demonstrate the effectiveness of the periodic-aperiodic decomposition algorithm for analyzing aperiodicities for a wide variety of voices, and point out the limitations of the algorithm.Index Terms-Jitter, periodic and aperiodic decomposition, shimmer, voice quality assessment, voice source analysis.

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“…The critical band rate is expressed in Barks [25]. For computing the perceptual spectral distance, a total number of 29 1-Bark bandwith bandpass filters are used.…”

Section: Measurement Methodologymentioning

confidence: 99%

Effectiveness of a periodic and aperiodic decomposition method for analysis of voice sources

d'Alessandro

Darsinos

Yegnanarayana

1998

IEEE Trans. Speech Audio Process.

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“…where k is the frequency bin index and m is the time frame index. Then, in order to transform the output from the STFT Y (k, m) into the critical band, an analytical function is used to express the transformation between frequency f (in Hz) and critical band z (in bark scale), which is defined by [29] f = 600 sinh z 6 .…”

Section: Critical Band Speech Enhancementmentioning

confidence: 99%

“…where i =[ 1, 2, · · · , I]. The number of critical bands I is chosen with respect to the bark scale [29]. Here, M(i, k) are the critical bandpass filter coefficients, which are defined as…”

Section: Critical Band Speech Enhancementmentioning

confidence: 99%

“…Tables 11 and 12 demonstrate the average results of the listening test in terms of speech quality, background noise, musical noise, and the overall performance of each estimation method by determining the mean of the rating scores. Ten normal hearing participants in the age of (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) took part in this test. They were asked to rate speech signals estimated by three different a priori SNR estimators in terms of speech, background noise, and musical noise as explained in the previous section.…”

Section: Evaluation Of Listening Testsmentioning

confidence: 99%

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An adaptive a priori SNR estimator for perceptual speech enhancement

Nahma

Yong²,

Dam

et al. 2019

J AUDIO SPEECH MUSIC PROC.

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In this paper, an adaptive averaging a priori SNR estimation employing critical band processing is proposed. The proposed method modifies the current decision-directed a priori SNR estimation to achieve faster tracking when SNR changes. The decision-directed estimator (DD) employs a fixed weighting with the value close to one, which makes it slow in following the onsets of speech utterances. The proposed SNR estimator provides a means to solve this issue by employing an adaptive weighting factor. This allows an improved tracking of onset changes in the speech signal. As a consequence, it results in better preservation of speech components. This adaptive technique ensures that the weighting between the modified decision-directed a priori estimate and the maximum likelihood a priori estimate is a function of the speech absence probability. The estimate of the speech absence probability is modeled by a sigmoid function. Furthermore, a critical band mapping for the short-time Fourier transform analysis-synthesis system is utilized in the speech enhancement to achieve less musical noise. In addition, to evaluate the ability of the a priori SNR estimation method in preserving speech components, we proposed a modified objective measurement known as modified hamming distance. Evaluations are performed by utilizing both objective and subjective measurements. The experimental results show that the proposed method improves the speech quality under different noise conditions. Moreover, it maintains the advantage of the DD approach in eliminating the musical noise under different SNR conditions. The objective results are supported by subjective listening tests using 10 subjects (5 males and 5 females).

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“…Stage 2: "Smearing" Y ( x ) by the prototype critical-band filter F ( x ) , conveniently expressed by its logarithm [5]: taken at one Bark intervals. However, this is equivalent to performing the convolution only at discrete intervals, which is the same as collecting energies by a bank of analysis filters with the following properties: (i) all filters have the same shape, (ii) the spacing between the maxima is a constant 1 Bark, and (iii) adjacent filter characteristics intersect at points 3 dB down from their maxima, i.e.…”

Section: Critical-band Filteringmentioning

confidence: 99%

Auditory distortion measure for speech coding

Wang

Sekey

Gersho

1991

[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing

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norm for comparative ratin automatic prediction from the value. In the rest of this paper proposed measure, analyze its scores, and compare it to other 2.(Smseg), the distortion m several known features of sounds by the human e a , spe . Frequency scale band integration in the cochlea, ABSTRACTA new, perceptually-motivated objective measure for estimating the subjective quality of coded speech is presented. It takes into account (i) auditory frequency warping (Bark transformation), (ii) critical-band integration, (iii) amplitude sensitivity variations with frequency and (iv) conversion from loudness level to loudness. For each 10 ms segment of an utterance, a weighted spectral vector is computed via 15 critical band filters. The overall distortion, called Bark Spectral Distortion (BSD), is the average squared Euclidean distance between spectral vectors of the original and coded utterance. In tests with speech distorted by a modulated noise reference unit (IvlNRU) or coded at rates of 2.4 -64 kbh, the measure predicted mean opinion score (MOS) ratings notably better than segmental SNR. The standard error in estimating MOS scores with the new measure was 0.2 -0.3.

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Improved 1-Bark bandwidth auditory filter

Cited by 24 publications

References 0 publications

Effectiveness of a periodic and aperiodic decomposition method for analysis of voice sources

Effectiveness of a periodic and aperiodic decomposition method for analysis of voice sources

An adaptive a priori SNR estimator for perceptual speech enhancement

Auditory distortion measure for speech coding

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