Methods for reducing audible artifacts in a wavelet-based broad-band denoising system

Ramarapu, Pavan K.; Maher, Robert C.

doi:10.1016/s0920-5489(99)90934-0

Cited by 3 publications

(7 citation statements)

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“…3. The first channel of a reference file captured by NAO containing one dictated word 6 J. Audio Eng.…”

Section: Resultsmentioning

confidence: 99%

“…As far as state of the art in speech enhancement and signal denoising is concerned, various joint time-frequency analysis (JTFA) approaches have been proposed [5]. Spectral subtraction is among the most common and popular approaches, but its main drawback is that musical noise artifacts and audible noise residuals remain (birdy noise) [6]. Short-term windowing is usually applied where window lengths and sampling rates are configured to accommodate time and frequency resolution.…”

Section: Best Regards the Authors 0 Introductionmentioning

confidence: 99%

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Improving Multilingual Interaction for Consumer Robots through Signal Enhancement in Multichannel Speech

Tsardoulias¹,

Thallas²,

Symeonidis³

et al. 2016

J. Audio Eng. Soc.

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Social robotics have become a trend in contemporary robotics research, since they can be successfully used in a wide range of applications. One of the most fundamental communication skills a consumer robot must have is the oral interaction with a human, in order to provide feedback or accept commands. There are quite a few well established Automatic Speech Recognition (ASR) tools, however without providing efficient results, especially in less popular languages, and more importantly under noisy conditions. The current paper investigates different voice activity detection and noise elimination methodologies to be used with ASRbased oral interaction with an affordable budget robot, NAO v4. Acoustically semi-stationary environments are assumed, which in conjunction to the high background noise of the NAO's microphones make the ASR quite difficult to succeed. Full Title:Improving multilingual interaction for consumer robots through signal enhancement in multichannel speechAdditional Information: Question ResponseHas this article previously been published at a conference or convention?No Please enter the text of an appropriate cover letter to accompany your submission, briefly describing the article you want to submit and saying why you believe it is suitable for publication in the AES Journal.Within the context of this paper several single and multi-channel VAD/noise estimation and noise elimination strategies were implemented and evaluated towards ASR in acoustically semi-stationary environments. This work aspires to be assistive in the consumer robotics domain, since the utilization of expensive hardware (microphones or robots in general) is usually out of the question, thus a robust VAD and noise reduction approach should exist. The main novelty of this paper does not lie in the implementations per se but in the adaptation approach followed, aiming to optimize the parameters of each method in order to improve ASR results. Concluding, we believe that the current work is eligible for submission in the AES Journal, since it researches a realistic problem current consumer robots have, from a signal processing point of view.Social robotics have become a trend in contemporary robotics research, since they can be successfully used in a wide range of applications. One of the most fundamental communication skills a consumer robot must have is the oral interaction with a human, in order to provide feedback or accept commands. There are quite a few well established Automatic Speech Recognition (ASR) tools, without however providing efficient results, especially in less popular languages, and more importantly under noisy conditions. The current paper investigates different voice activity detection and noise elimination methodologies to be used with ASRbased oral interaction with an affordable budget robot, NAO v4. Acoustically quasi-stationary environments are assumed, which in conjunction with the high background noise of the NAO's microphones make the ASR quite difficult to succeed. 8

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“…3. The first channel of a reference file captured by NAO containing one dictated word 6 J. Audio Eng.…”

Section: Resultsmentioning

confidence: 99%

Section: Best Regards the Authors 0 Introductionmentioning

confidence: 99%

Improving Multilingual Interaction for Consumer Robots through Signal Enhancement in Multichannel Speech

Tsardoulias¹,

Thallas²,

Symeonidis³

et al. 2016

J. Audio Eng. Soc.

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“…In order to achieve a sufficient perceptual quality for distorted audio signals, intensive research has been conducted in the area of error concealment, in particular denoising, declicking and decrackling (see, e. g., [8][9][10][11][12]). However, from the point of view of source decoding, these algorithms work on signals generated by hard-decision.…”

Section: Introductionmentioning

confidence: 99%

Delayless soft-decision decoding of high-quality audio transmitted over awgn channels

Pflug

Fingscheidt

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Short-range wireless audio transmission with high quality on the one hand often encounters error-prone channels, while on the other hand decoding delay plays a critical role in the application. A lot of prior art in audio error concealment is either only intuitively motivated or adds too much delay to the transmission. In this paper we propose a framework for transmitted audio error concealment without any algorithmic delay. As a novelty, it strongly follows a Bayesian approach for quantized but uncompressed audio, which can in principle be applied to any type of wireless channel yielding some kind of reliability information. Two methods to compute audio prediction coefficients are presented, one based on the autocorrelation method, the other one on the normalized least-mean-square (NLMS) algorithm. Simulation results for additive white Gaussian noise (AWGN) channels show the significant effect of the proposed approaches.

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“…Χαρακτηριστικά είναι και τα υπολείµµατα θορύβου τύπου "birdy noise", τα οποία εµφανίζονται κυρίως σε χαµηλές σηµατοθορυβικές σχέσεις, δηλαδή όταν οι συνιστώσες του σήµατος µεταβάλλονται πολύ κοντά στα επίπεδα κατωφλίωσης. Οι σχετικές υλοποιήσεις, µέσω βραχυπρόθεσµου µετασχηµατισµού Fourier (Short Term Fourier Transform -STFT), τείνουν να παράγουν πρόσθετα «σφάλµατα αποθορυβοποίησης» (de-noising artifacts), µε ποιο χαρακτηριστική την εµφάνιση «µουσικών ήχων» [177]- [179], [184]- [187], [189]- [190]. Όσον αφορά την ενσωµάτωση του φίλτρου A(k), αυτό παρέχει τη δυνατότητα περαιτέρω διαµόρφωσης της «συνολικής» κατωφλίωσης "c•A(k)", ανάλογα µε το φασµατικό περιεχόµενο του ενθόρυβου σήµατος και τα χαρακτηριστικά του θορύβου.…”

Section: εξαρτώµενο από τη συχνότητα παραµετρικό φίλτρο Wiener (Frequency Depended Parametric Wiener Filter -Fdpwf)unclassified

“…Έχει διαπιστωθεί ότι, κατά την εφαρµογή του φίλτρου Wiener στα διαστήµατα σιγής, τα αποτελέσµατα αποθορυβοποίησης δεν είναι τόσο ικανοποιητικά [177], [187], [189]. Ένα δυσάρεστο αποτέλεσµα, είναι η παρουσία σφαλµάτων αποθορυβοποίησης (de-noising artifacts), όπως είναι τα «υπολείµµατα θορύβου» (noise residuals, "birdy noise"), τα οποία οφείλονται στις µικρές και τυχαίες ενεργειακές διακυµάνσεις του θορύβου [177], [179], [184], [187], [189]- [190]. Είναι φανερό ότι λόγω της απουσίας σήµατος, δεν είναι δυνατή η ακουστική απόκρυψη (masking) των υπολειµµάτων θορύβου [188], [202].…”

Section: επεξεργασία Wdwf σηµείο προς σηµείο τύπος-ιιunclassified

Τεχνικές Επεξεργασίας Και Διαχείρισης Οπτικοακουστικού Περιεχομένου Στη Μελέτη Βιοακουστικών Φαινομένων

Δημούλας¹

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Εννοείται ο πόνος που δεν οφείλεται σε κάποια παθολογική κατάσταση του πεπτικού συστήµατος ή κάποιου άλλου γειτονικού οργάνου, αλλά σε λειτουργικά ζητήµατα κινητικότητας. * Τα τεστ, που διεξάγονται σε δείγµατα υποκειµένων (εθελοντές) πριν την έγκριση κυκλοφορίας των φαρµάκων, βασίζονται αποκλειστικά σε αξιολόγηση σχετικών ερωτηµατολογίων. † Η µετεγχειρητική λειτουργία του εντέρου κρίνεται ιδιαίτερα σηµαντική στην περαιτέρω πορεία ανάρρωσης του ασθενή.

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Methods for reducing audible artifacts in a wavelet-based broad-band denoising system

Cited by 3 publications

References 0 publications

Improving Multilingual Interaction for Consumer Robots through Signal Enhancement in Multichannel Speech

Improving Multilingual Interaction for Consumer Robots through Signal Enhancement in Multichannel Speech

Delayless soft-decision decoding of high-quality audio transmitted over awgn channels

Τεχνικές Επεξεργασίας Και Διαχείρισης Οπτικοακουστικού Περιεχομένου Στη Μελέτη Βιοακουστικών Φαινομένων

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