Online Monaural Speech Enhancement Based on Periodicity Analysis and A Priori SNR Estimation

Chen, Zhangli; Hohmann, Volker

doi:10.1109/taslp.2015.2456423

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…A set of four auditory features per frequency band was extracted subsequently to the auditory preprocessing stage: Periodicity, periodic energy, and periodicity‐based interaural time (ITD) and level (ILD) difference (Josupeit & Hohmann, ). The first stage for extracting these features was the calculation of a synchrogram (Chen & Hohmann, ), as illustrated in Figure for a single frequency band. The preprocessed signals of each frequency band f c were analysed every 10 ms as follows: The signal around each considered time instance n was segmented into 8 shorter signals, each of a specific length P (see Figure a).…”

Section: Stimulimentioning

confidence: 99%

Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task

Josupeit

Schoenmaker

Par

et al. 2018

Eur J of Neuroscience

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Human listeners robustly decode speech information from a talker of interest that is embedded in a mixture of spatially distributed interferers. A relevant question is which time-frequency segments of the speech are predominantly used by a listener to solve such a complex Auditory Scene Analysis task. A recent psychoacoustic study investigated the relevance of low signal-to-noise ratio (SNR) components of a target signal on speech intelligibility in a spatial multitalker situation. For this, a three-talker stimulus was manipulated in the spectro-temporal domain such that target speech time-frequency units below a variable SNR threshold (SNR ) were discarded while keeping the interferers unchanged. The psychoacoustic data indicate that only target components at and above a local SNR of about 0 dB contribute to intelligibility. This study applies an auditory scene analysis "glimpsing" model to the same manipulated stimuli. Model data are found to be similar to the human data, supporting the notion of "glimpsing," that is, that salient speech-related information is predominantly used by the auditory system to decode speech embedded in a mixture of sounds, at least for the tested conditions of three overlapping speech signals. This implies that perceptually relevant auditory information is sparse and may be processed with low computational effort, which is relevant for neurophysiological research of scene analysis and novelty processing in the auditory system.

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Section: Stimulimentioning

confidence: 99%

Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task

Josupeit

Schoenmaker

Par

et al. 2018

Eur J of Neuroscience

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“…The DD approach can eliminate the musical noise efficiently. However, a priori SNR estimation of DD method tracks the instantaneous SNR with one frame delay [5]. To solve this problem, the TSNR [6] method has been proposed by Plapous refine the a priori SNR estimation based on result of DD.…”

Section: Guidelinesmentioning

confidence: 99%

The Priori SNR Estimation Based on the Post Frame Information and Self-Adaptive Averaging Factor

Yi¹,

Li²,

Zhang³

2017

Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)

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In view of the estimation problem of the priori signal-to-noise ratio (SNR) estimation in single-channel speech enhancement algorithm, a novel method based on the further information is proposed. In this paper, the information from the post frame is considered in obtaining priori SNR of the current frame. Firstly, the original priori SNR is achieved by the decision directed (DD) algorithm. secondly, in order to solve the smoothness drawback of Two-Step Noise Reduction (TSNR) method, we utilize the correlation of the inter-frame and introduce speech information from the post frame to refine the estimation priori SNR. Lastly, to track noisy speech change more quickly, a self-adaptive averaging factor is introduced by the minimum mean-squared error (MMSE) estimator. The proposed algorithm has good performance in reducing the speech distortion while the advantages in noise suppression are kept. The simulation experiment results show that the performance of proposed algorithm is superior than TSNR algorithm under various noise conditions.

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“…Periodicity features were extracted from the preprocessed signals. They were based on the extraction of the normalized "synchrogram" Sðt; f c ; PÞ (Chen and Hohmann, 2015). The normalized synchrogram Sðt; f c ; PÞ is the ratio of the harmonic signal energy for the period P and the total signal energy in the same time window for a [t, f c ] bin, computed for a number of tested candidate periods P 0 .…”

Section: Periodicity Featuresmentioning

confidence: 99%

“…1, model part A). Binaural features were calculated using a slightly modified version of the binaural model of Dietz et al (2011); monaural features were periodicity (Chen and Hohmann, 2015) and spectral energy. Second, target-related binaural features were selected using a binary mask (BM) (Fig.…”

Section: Introductionmentioning

confidence: 99%

Modeling of speech localization in a multi-talker mixture using periodicity and energy-based auditory features

Josupeit

Kopčo

Hohmann

2016

The Journal of the Acoustical Society of America

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A recent study showed that human listeners are able to localize a short speech target simultaneously masked by four speech tokens in reverberation [Kopčo, Best, and Carlile (2010). J. Acoust. Soc. Am. 127, 1450-1457]. Here, an auditory model for solving this task is introduced. The model has three processing stages: (1) extraction of the instantaneous interaural time difference (ITD) information, (2) selection of target-related ITD information ("glimpses") using a template-matching procedure based on periodicity, spectral energy, or both, and (3) target location estimation. The model performance was compared to the human data, and to the performance of a modified model using an ideal binary mask (IBM) at stage (2). The IBM-based model performed similarly to the subjects, indicating that the binaural model is able to accurately estimate source locations. Template matching using spectral energy and using a combination of spectral energy and periodicity achieved good results, while using periodicity alone led to poor results. Particularly, the glimpses extracted from the initial portion of the signal were critical for good performance. Simulation data show that the auditory features investigated here are sufficient to explain human performance in this challenging listening condition and thus may be used in models of auditory scene analysis.

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Online Monaural Speech Enhancement Based on Periodicity Analysis and A Priori SNR Estimation

Cited by 11 publications

References 25 publications

Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task

Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task

The Priori SNR Estimation Based on the Post Frame Information and Self-Adaptive Averaging Factor

Modeling of speech localization in a multi-talker mixture using periodicity and energy-based auditory features

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