Relevant bibliographies by topics / Influence of audio signal

Academic literature on the topic 'Influence of audio signal'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Influence of audio signal"

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Saiapina, Inna, Mykhailo Babaiev, and Olha Аnanіevа. "Reducing noise influence on an audio frequency track circuit." MATEC Web of Conferences 294 (2019): 03015. http://dx.doi.org/10.1051/matecconf/201929403015.

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In connection with electromagnetic interference influence on the track circuits, the purpose of the research is finding the means to increase the noise immunity of an audio frequency track circuit. The authors propose a new engineering solution, which enables reducing the effect of noises on the input of the track receiver in the intervals between signal current pulses. The proposed noise-immune audio frequency track circuit is based on inserting a delay line, an adjustable single-pulse generator and a controlled electronic switch into the existing audio frequency track circuit equipment. To analyze its efficiency, the operation of the audio frequency track circuit was simulated under conditions of traction current disturbances, impulse and fluctuation interferences with the known parameters. The results show that proposed device for railway transport allows to increase a signal-to-noise ratio on the track receiver input from 8% to 30%, depending on the interference parameters and the level of the useful signal.
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Sokolov, Sergei A., and Yuri A. Kovalgin. "INFLUENCE OF THE COMPRESSION ALGORITHMS ON THE QUALITY OF AUDIO PROGRAMS IN DRM DIGITAL RADIO BROADCASTING SYSTEM." T-Comm 15, no. 7 (2021): 4–13. http://dx.doi.org/10.36724/2072-8735-2021-15-7-4-13.

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This work studied the influence of MPEG-4 HE-AAC v.2 and MPEG-4 xHE-AAC compression algorithms on the reproduction performance of high-quality audio signals. The first part of the article is based on the analysis of earlier publications, studied quality of the compression algorithms for MPEG-4 ISO/IEC 14493-3 (AAC, AAC+SBR, PS, AAC+SBR+PS) and MPEG D Surround standards applied in MPEG-4 HE-AAC v.2 and MPEG-4 xHE-AAC codecs of DAB and DRM systems for digital audio broadcasting, respectively. It has been demonstrated that their use for processing of high-quality audio signals in the frequency range of 40…15000 Hz, allowed to reduce the digital-data rate at the output to 24…30 êbit/s, without observing noticeable phenomena. This conclusion was confirmed in the second part of the article, which described the results of relative comparison of the quality of reproduction of audio signals broadcasted via analogues FM or digital DRM channels. The equipment employed for their realization was described, as well as the methodology to obtain test recordings in order to perform the statistical data analysis. Further, the specific data scales were shown to evaluate the signal pairs being compared. Results of the experiments and their analysis confirmed that, for a digital data rate of 30 êbit/s obtained at the output of the MPEG-4 xHE-AAC coder, the difference in quality was rather weak for audio signals ranging from 40 to 15000 Hz, broadcasted via both analogues and digital channels. In addition, only a marginal difference was noticed for the signal pairs received from the digital output channels of the transmitter processor and DRM receiver. It has been demonstrated that the MPEG-4 xHE-AAC codec, compared to the previous MPEG-4 HE-AAC v.2 version, exhibited clear advantages for practical applications in terms of sound quality. Based on the gained experience, common criteria of deterioration of the data quality were formulated and generalized for all audio compression MPEG standards.
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Chen, Bo, Heung Kou, Bowen Hou, and Yanbing Zhou. "Music Feature Extraction Method Based on Internet of Things Technology and Its Application." Computational Intelligence and Neuroscience 2022 (April 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/8615152.

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Due to the influence of factors such as strong music specialization, complex music theory knowledge, and various variations, it is difficult to identify music features. We have developed a music characteristic identification system using the Internet-based method. The physical sensing layer of our designed system deploys audio sensors on various coordinates to capture the raw audio signal and performs audio signal processing and analysis using the TMS320VC5402 digital signal processor; the Internet transport layer places audio sensors at various locations to capture the raw audio signal. The TMS320VC5402 digital signal processor is used for audio signal diagnosis and treatment. The network transport layer transmits the finished audio signal to the data base of song signal in the application layer of the system; the song characteristic analysis block in the application layer adopts dynamics. The music characteristic analysis block in the applied layer adopts dynamic time warping algorithm to acquire the maximal resemblance between the test template and the reference template to achieve music signal characteristic identification and identify music tunes and music modes based on the identification results. The application layer music feature analysis block adopts dynamic time regularization algorithm and mel-frequency cepstrum coefficient to achieve music signal feature recognition and identify music tunes and music patterns based on the recognition results. We have verified through experiments, and the results show that the system operates consistently, can obtain high-quality music samples, and can extract good music characteristics.
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Sarno, Riyanarto, Johanes Andre Ridoean, Dwi Sunaryono, and Dedy Rahman Wijaya. "Classification of Music Mood Using MPEG-7 Audio Features and SVM with Confidence Interval." International Journal on Artificial Intelligence Tools 27, no. 05 (August 2018): 1850016. http://dx.doi.org/10.1142/s0218213018500161.

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Psychologically, music can affect human mood and influence human behavior. In this paper, a novel method for music mood classification is introduced. In the experiment, music mood classification was performed using feature extraction based on MPEG-7 features from the ISO/IEC 15938 standard for describing multimedia content. The result of this feature extraction are 17 low-level descriptors. Here, we used the Audio Power, Audio Harmonicity, and Audio Spectrum Projection features. Moreover, the discrete wavelet transform (DWT) was utilized for audio signal reconstruction. The reconstructed audio signals were classified by the new method, which uses a support vector machine with a confidence interval (SVM-CI). According to the experimental results, the success rate of the proposed method was satisfactory and SVM-CI outperformed the ordinary SVM.
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Garcia, Jane Mertz, and Michael P. Cannito. "Influence of Verbal and Nonverbal Contexts on the Sentence Intelligibility of a Speaker With Dysarthria." Journal of Speech, Language, and Hearing Research 39, no. 4 (August 1996): 750–60. http://dx.doi.org/10.1044/jshr.3904.750.

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The influence of verbal and nonverbal contextual factors on intelligibility was examined using sentences produced under varying conditions by a speaker with severe flaccid dysarthria. Contextual factors included (a) concurrent production of communication gestures, (b) predictiveness of message content, (c) relatedness of sentences to specific situational contexts, and (d) prior familiarization with the speaker. Sentences produced by the speaker were audio- and video-recorded and presented to 96 listeners/viewers who were assigned to three different methods of presentation of the stimuli: (a) audio + video, (b) audio-only, or (c) video-only conditions. Results indicated that gestures, predictiveness, and context influenced intelligibility; however, complex interactions were observed among these factors and methods of presentation of the stimuli. Results were interpreted in light of Lindblom's "mutuality model," indicating that when signal fidelity is poor, as in the present speaker with dysarthria, differing combinations of signal-independent information may be employed to enhance listener understanding of spoken messages.
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Bang, Woorim, and Ji Won Yoon. "Power Grid Estimation Using Electric Network Frequency Signals." Security and Communication Networks 2019 (September 24, 2019): 1–11. http://dx.doi.org/10.1155/2019/1982168.

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The electric network frequency (ENF) has a statistical uniqueness according to time and location. The ENF signal is always slightly fluctuating for the load balance of the power grid around the fundamental frequency. The ENF signals can be obtained from the power line using a frequency disturbance recorder (FDR). The ENF signal can also be extracted from video files or audio files because the ENF signal is also saved due to the influence of the electromagnetic field when video files or audio files are recorded. In this paper, we propose a method to find power grid from ENF signals collected from various time and area. We analyzed ENF signals from the distribution level of the power system and online uploaded video files. Moreover, a hybrid feature extraction approach, which employs several features, is proposed to infer the location of the signal belongs regardless of the time that the signal was collected. Employing our suggested feature extraction methods, the signal which extracted from the power line can be classified 95.21% and 99.07% correctly when ENF signals have 480 and 1920 data points, respectively. In the case of ENF signals extracted from multimedia, the accuracy varies greatly according to the recorded environment such as network status and microphone quality. When constructing a feature vector from 120 data points of ENF signals, we could identify the power grid had an average of 94.17% accuracy from multimedia.
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Hanzlik, Tomasz. "Class D audio amplifier and method for compensation of power supply voltage influence on output audio signal in class D audio amplifier." Journal of the Acoustical Society of America 120, no. 5 (2006): 2400. http://dx.doi.org/10.1121/1.2395103.

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Kropotov, Y. A., A. A. Belov, and A. Y. Prockuryakov. "Increasing signal/acoustic interference ratio in telecommunications audio exchange by adaptive filtering methods." Information Technology and Nanotechnology, no. 2416 (2019): 271–76. http://dx.doi.org/10.18287/1613-0073-2019-2416-271-276.

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The paper deals with the issues of increasing signal/noise ratio in telecommunication audio exchange systems. The study of characteristics of speech signals and acoustic noises, such as mathematical expectation, dispersion, relative intensity of acoustic speech signals and various types of acoustic noises and interference is carried out. It is shown that in the design of telecommunications systems, in particular loudspeaker systems operating under the influence of external acoustic noise of high intensity, it is necessary to solve the problem of developing algorithms to effectively suppress the above mentioned interference to ensure the necessary signal/noise ratio in communication systems. A mathematical model of the autocorrelation function of the speech signal by using the Lagrange interpolation polynomial of order 10, considered the creation of adaptive algorithms to suppress acoustic noise by linear filtering methods. Thus suppression of acoustic noises and hindrances is possible at the expense of operated change of area of a cutting in the interval from 0 Hz to 300-1000 Hz, depending on a hindrance conditions.
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Přibil, Jiří, Anna Přibilová, and Ivan Frollo. "Analysis of Energy Relations between Noise and Vibration Signals in the Scanning Area of an Open-Air MRI Device." Proceedings 4, no. 1 (November 14, 2018): 29. http://dx.doi.org/10.3390/ecsa-5-05730.

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The paper analyzes the influence of different magnetic resonance (MR) scan sequence parameters (echo time, repetition time, orientation of scan slices, sequence type, mass of a tested object) on the energy of the produced noise and vibration. The measured sound pressure levels, together with the recorded noise and vibration signals, were stored in a database and then processed using similar methods to speech signal analysis because the main frequencies of the acoustic noise and vibration lie in the standard audio frequency range. In the signal processing phase, four types of parameters describing the signal energy were determined and statistically analyzed, and the obtained results were visually and numerically compared.
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Oruganti, Rakesh. "Detecting and Analyzing Emotions Using Text Stream Messages." ECS Transactions 107, no. 1 (April 24, 2022): 16913–23. http://dx.doi.org/10.1149/10701.16913ecst.

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Emotions are the gateway to converse your point of view to others. Generally, you can express your emotions either by audio or writing or symbols. Amid of audio and writing, audio/voice is a persuasive method to communicate emotions. During this work, we can get these emotions through the audio of the human using a deep learning network. We are extensively classify the emotions of the humans into irate, quiet, disdain, dread, glad, nonpartisan, miserable, and shock for effective emotion prediction. The audio features define how the audio signal reacts to the disturbing influence perceptible throughout. Thus, these features play a vital role in recognizing the emotion in audio. We are applying MFCC (Mel-Frequency Cepstral Coefficients) to the audio information to extract these essential features. MFCC is the preeminent method to extract the features from audio. Moreover, to get exact features, we are applying increase methods to the audio information.
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Dissertations / Theses on the topic "Influence of audio signal"

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Saruwatari, Hiroshi. "BLIND SIGNAL SEPARATION OF AUDIO SIGNALS." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10406.

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Scott, Hugh R. R. "Multiresolution techniques for audio signal restoration." Thesis, University of Warwick, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307347.

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Ясунова, Масума Пулатівна. "Метод оцінки інтегральної активності ЕЕГ під впливом аудіо сигналів." Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2021. https://ela.kpi.ua/handle/123456789/43674.

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Обсяг звіту становить 57 сторінок, міститься 32 ілюстрації, 14 таблиць, 5 формул, 2 додатки. Загалом опрацьовано 36 джерел. Актуальність даної роботи полягаєу визначенні залежності біоелектричної активності головного мозку від амплітудно-частотних характеристик звукового сигналу. У наш час музика супроводжує наше життя, тому важливо визначити який вплив вона має на електричну активність головного мозку і як саме змінюються характеристики показників мозкової активності при прослуховуванні музичних сигналів. Мета:визначити ефективність впливу аудіо сигналів різного амплітудно-частотного складу на зміну інтегральної активності мозку. Для досягнення мети дипломної роботи було сформовано ряд наступних задач: 1. Проаналізувати амплітудно частотні характеристики обраних аудіо сигналів; 2. Дослідити зміну ЕЕГ-ритмів на фоні впливу обраних аудіо сигналів 3. Дослідити вплив частотних характеристик аудіо сигналу на інтегральну електричну активність мозку.
The scope of the report is 57 pages, contains 32 illustrations, 14 tables, 2 annexes. In total, 36 sources were used. The relevance of this work lies in determining the dependence of the bioelectric activity of the brain on the amplitude-frequency characteristics of the sound signal. Nowadays, music accompanies our life, so it is important to determine what impact it has on the electrical activity of the brain and how the characteristics of the indicators of brain activity change when listening to musical signals. Purpose: to determine the effectiveness of audio signal impact of different amplitude-frequency composition on the change of brain integral activity. To achieve the goal of the thesis, the following tasks were formed: 1. Analyze the amplitude-frequency characteristics of the selected audio signals; 2. Investigate the change in EEG rhythms against the background of the influence of selected audio signals; 3. Investigate the influence of frequency characteristics of the audio signal on the integrated electrical activity of the brain.
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Chiu, Leung Kin. "Efficient audio signal processing for embedded systems." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44775.

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We investigated two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound "richer" and "fuller," using a combination of bass extension and dynamic range compression. We also developed an audio energy reduction algorithm for loudspeaker power management by suppressing signal energy below the masking threshold. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine learning algorithm AdaBoost is used to select the most relevant features for a particular sound detection application. We also designed the circuits to implement the AdaBoost-based analog classifier.
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Wellhausen, Jens. "Algorithms for audio signal segmentation and separation /." Aachen : Shaker, 2007. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016149157&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Kwong, Mylène. "Détection de transitoires dans un signal audio." Sherbrooke : Université de Sherbrooke, 2004.

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Moinnereau, Marc-Antoine. "Encodage d'un signal audio dans un électroencéphalogramme." Mémoire, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10554.

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Les interfaces cerveau-machine visent à établir un lien de communication entre le cerveau et un système externe à ce dernier. Les électroencéphalogrammes (EEG), dans ce contexte, ont l’avantage d’être non invasifs. Par contre, l’information sensorielle qui se retrouve dans un signal EEG est beaucoup moins ciblée que dans un signal neuronal acquis par une méthode invasive. De plus, étant donné que le cortex auditif est situé dans des repliements du tissu cortical, les neurones qui déchargent, suite à un stimulus auditif, sont parallèles à la surface corticale sur laquelle les EEG sont enregistrés. Par conséquent, l’information auditive qui se retrouve dans le canal EEG situé vis-à-vis du cortex auditif est faible. L’objectif principal de ce projet de recherche consiste donc à étudier la répartition de l’information auditive dans l’ensemble des canaux EEG. Pour ce faire, nous utilisons deux approches. Dans la première, nous tenterons d’estimer l’activité corticale sous-jacente à partir des signaux EEG en utilisant un modèle de couplage bande fréquence. En effet, certaines bandes de fréquences sont des bons prédicteurs des décharges neuronales. Cependant, cette approche n’a pas été validée pour le système auditif, nous confronterons donc l’estimation obtenue à une autre estimation en ayant recours à un modèle spécialisé pour l’encodage du signal de parole faisant appel aux processus ponctuels. Ce modèle prend en compte les dynamiques intrasèques des neurones et également des propriétés spectrotemporelles du stimulus d’entrée. Dans la seconde approche, nous étudierons la possibilité de classifier 3 voyelles (a, i et u) en fonction du nombre de canaux EEG utilisés ainsi que leur répartition sur le cuir chevelu. Nous aurons recours, pour cela, à un réservoir de neurone à décharge récurrent activé en entrée par les données EEG. Les résultats démontrent que l’information auditive se retrouve en fait dans l’ensemble des canaux EEG et qu’elle n’est pas confinée à un nombre restreint d’électrodes. Il est également montré que lorsque l’on utilise les 64 électrodes que comporte l’EEG pour classifier les 3 voyelles, on obtient une classification de l’ordre de 80%, mais aussi qu’un nombre limité de 10 électrodes suffit pour obtenir une classification satisfaisante et, qu’en plus, la position de ces électrodes sur le cuir chevelu est peu importante.
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Kwong, Mylène. "Détection de transitoires dans un signal audio." Mémoire, Université de Sherbrooke, 2004. http://savoirs.usherbrooke.ca/handle/11143/1254.

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Le travail présenté dans ce mémoire de maîtrise porte sur la détection de transitoires dans un signal audio. L'objectif visé est de pouvoir extraire le rythme et la segmentation du signal musical. Ce projet s'inscrit dans le cadre général d'un dispositif de transcription automatique de la musique. La problématique majeure réside dans la détection de toutes les transitoires contenues dans le signal musical, y compris celles masquées par des composantes stationnaires de plus haute énergie. Les méthodes que nous avons envisagées sont basées sur des décompositions temps-fréquence du signal et une décomposition du signal en deux composantes, l'une stationnaire et l'autre transitoire.Le premier algorithme développé décompose le signal en bandes de fréquences.Le signal d'enveloppe de chaque bande de fréquences est analysé (par une étude sur le signal de dérivée) afin d'identifier les positions des transitoires. L'utilisation d'un opérateur de Teager (qui met en évidence des transitoires dans le signal) sur des signaux réels de guitare, a permis de passer de 67% à 92% de bonnes détections des transitoires de haute énergie (à peine 50% de toutes les transitoires) sans toutefois permettre la détection de transitoires masquées de faible énergie toutes aussi importantes. La méthode que nous avons envisagée, pour détecter un maximum de transitoires, utilise un pré-traitement séparant l'information transitoire du reste du signal. Un filtrage fréquentiel adaptatif permet de retirer au signal sa composante harmonique, avant de procéder à la localisation temporelle des transitoires. La méthode évaluée avec des signaux réels riches de guitare permet de détecter 85% des transitoires.
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Anderson, David Verl. "Audio signal enhancement using multi-resolution sinusoidal modeling." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15394.

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Carlo, Diego Di. "Echo-aware signal processing for audio scene analysis." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S075.

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La plupart des méthodes de traitement du signal audio considèrent la réverbération et en particulier les échos acoustiques comme une nuisance. Cependant, ceux-ci transmettent des informations spatiales et sémantiques importantes sur les sources sonores et des méthodes essayant de les prendre en compte ont donc récemment émergé.. Dans ce travail, nous nous concentrons sur deux directions. Tout d’abord, nous étudions la manière d’estimer les échos acoustiques à l’aveugle à partir d’enregistrements microphoniques. Deux approches sont proposées, l’une s’appuyant sur le cadre des dictionnaires continus, l’autre sur des techniques récentes d’apprentissage profond. Ensuite, nous nous concentrons sur l’extension de méthodes existantes d’analyse de scènes audio à leurs formes sensibles à l’écho. Le cadre NMF multicanal pour la séparation de sources audio, la méthode de localisation SRP-PHAT et le formateur de voies MVDR pour l’amélioration de la parole sont tous étendus pour prendre en compte les échos. Ces applications montrent comment un simple modèle d’écho peut conduire à une amélioration des performances
Most of audio signal processing methods regard reverberation and in particular acoustic echoes as a nuisance. However, they convey important spatial and semantic information about sound sources and, based on this, recent echo-aware methods have been proposed. In this work we focus on two directions. First, we study the how to estimate acoustic echoes blindly from microphone recordings. Two approaches are proposed, one leveraging on continuous dictionaries, one using recent deep learning techniques. Then, we focus on extending existing methods in audio scene analysis to their echo-aware forms. The Multichannel NMF framework for audio source separation, the SRP-PHAT localization method, and the MVDR beamformer for speech enhancement are all extended to their echo-aware versions
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Books on the topic "Influence of audio signal"

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Digital audio signal processing. 2nd ed. Chichester, West Sussex, England: Wiley, 2008.

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Bharitkar, Sunil, and Chris Kyriakakis, eds. Immersive Audio Signal Processing. New York, NY: Springer New York, 2006. http://dx.doi.org/10.1007/0-387-28503-2.

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Self, Douglas. Small Signal Audio Design. Third edition. | Abingdon, Oxon ; New York, NY : Routledge, 2020.: Focal Press, 2020. http://dx.doi.org/10.4324/9781003031833.

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Small signal audio design. Oxford: Focal Press, 2010.

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Self, Douglas. Small Signal Audio Design. Burlington: Elsevier, 2010.

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Gold, Ben, Nelson Morgan, and Dan Ellis. Speech and Audio Signal Processing. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118142882.

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Scott, Hugh R. R. Multiresolution techniques for audio signal restoration. [s.l.]: typescript, 1995.

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Adaptive signal models: Theory, algorithms, and audio applications. Boston: Kluwer Academic Publishers, 1998.

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Li, Francis F., and Trevor J. Cox. Digital Signal Processing in Audio and Acoustical Engineering. Edited by Francis F. Li and Trevor J. Cox. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, [2019]: CRC Press, 2019. http://dx.doi.org/10.1201/9781315117881.

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Designing audio effect plug-ins in C++ with digital audio signal processing theory. Waltham, MA: Focal Press, 2013.

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Book chapters on the topic "Influence of audio signal"

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de Freitas Piedade Melo, Dirceu, Inacio de Sousa Fadigas, and Hernane Borges de Barros Pereira. "Community detection in visibility networks: an approach to categorize percussive influence on audio musical signals." In Studies in Computational Intelligence, 321–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50901-3_26.

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Robinson, Kevin. "Signal Characteristics." In Practical Audio Electronics, 57–80. Abingdon, Oxon : Routledge, an imprint of the Taylor & Francis Group, 2020.: Focal Press, 2020. http://dx.doi.org/10.4324/9780429343056-5.

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Maher, Robert C. "Audio Signal Enhancement." In Modern Acoustics and Signal Processing, 69–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99453-6_6.

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Rao, Preeti. "Audio Signal Processing." In Studies in Computational Intelligence, 169–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75398-8_8.

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Tarr, Eric. "Introduction to Signal Synthesis." In Hack Audio, 79–101. New York, NY : Routledge, 2019. | Series: Audio Engineering Society presents: Routledge, 2018. http://dx.doi.org/10.4324/9781351018463-7.

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Moreau, Nicolas. "Audio Coding." In Tools for Signal Compression, 141–48. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118616611.ch8.

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Moreau, Nicolas. "Audio Coding." In Tools for Signal Compression, 123–40. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118616611.ch7.

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Godsill, Simon J., and Peter J. W. Rayner. "Digital Signal Processing." In Digital Audio Restoration, 15–38. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1561-8_2.

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Self, Douglas. "Signal Switching." In Small Signal Audio Design, 575–609. Third edition. | Abingdon, Oxon ; New York, NY : Routledge, 2020.: Focal Press, 2020. http://dx.doi.org/10.4324/9781003031833-21.

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Tarr, Eric. "Signal Gain and DC Offset." In Hack Audio, 57–78. New York, NY : Routledge, 2019. | Series: Audio Engineering Society presents: Routledge, 2018. http://dx.doi.org/10.4324/9781351018463-6.

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Conference papers on the topic "Influence of audio signal"

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Lahaie, Olivier, Roch Lefebvre, and Philippe Gournay. "Influence of audio bandwidth on speech emotion recognition by human subjects." In 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2017. http://dx.doi.org/10.1109/globalsip.2017.8308604.

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Westermann, Adam, and Jorg Buchholz. "The influence of informational masking in complex real-world environments." In 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2013. http://dx.doi.org/10.1109/waspaa.2013.6701873.

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Coler, Henrik von, Shiva Sundaram, Robert Schleicher, and Gabriel Curio. "Towards the influence of vibration on evaluation of speech utterances in mobile devices." In 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2011. http://dx.doi.org/10.1109/aspaa.2011.6082292.

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Lehmann, Eric A., Anders M. Johansson, and Sven Nordholm. "Modeling of Motion Dynamics and its Influence on the Performance of a Particle Filter for Acoustic Speaker Tracking." In 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. IEEE, 2007. http://dx.doi.org/10.1109/aspaa.2007.4392979.

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Dalga, Derya, and Simon Doclo. "Influence of secondary path estimation errors on the performance of ANC-motivated noise reduction algorithms for hearing aids." In 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2013. http://dx.doi.org/10.1109/waspaa.2013.6701812.

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Stanojevic, Miodrag, Milos Bjelic, Jelena Certic, and Dragana Sumarac Pavlovic. "Psychoacoustic analysis of decimation filter parameters influence on the quality of audio signals." In 2015 23rd Telecommunications Forum Telfor (TELFOR). IEEE, 2015. http://dx.doi.org/10.1109/telfor.2015.7377563.

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Petrovic, Nikola, Milos Bjelic, Jelena Certic, Miodrag Stanojevic, and Dragana Sumarac Pavlovic. "Analyses of decimation filter stopband attenuation influence on subjective quality of audio signals." In 2016 24th Telecommunications Forum (TELFOR). IEEE, 2016. http://dx.doi.org/10.1109/telfor.2016.7818882.

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Shrestha, Sundar, Anand Koirala, Maksym Spiryagin, and Qing Wu. "Wheel-Rail Interface Condition Estimation via Acoustic Sensors." In 2020 Joint Rail Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/jrc2020-8037.

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Abstract The surface roughness between the wheel and rail has a huge influence on the rolling noise level. The presence of the third body such as frost or grease at the wheel-rail interface contributes towards change in adhesion coefficient resulting in the generation of acoustic noise at various levels. Therefore, it is possible to estimate adhesion conditions between the wheel and rail from the analysis of audio patterns originating from wheel-rail interaction. In this study, a new approach to estimate adhesion condition is proposed which takes rolling noise as input. Acoustic sensors (Behringer B-5 condenser microphone) for audio data acquisition were installed on a scaled bogie test rig. The cardioid configuration of the sensor was chosen for picking up the source signal while avoiding the surround sound. The test rig was operated at the speed of 40 and 60 rpm multiple times in both dry and wet friction conditions. The proposition behind running the setup several times was to get a large set of acoustic signals under different adhesion conditions because there exists no such dataset in the public repositories. 30 seconds interval of rolling noise data from the continuous audio signal was extracted as samples for the training W-RICE model. Each sample was pre-processed using the Librosa python package to extract seven basic features/signatures: zero-crossing rate, spectral centroid, spectral bandwidth, spectral roll-off, MFCCs (Mel-frequency cepstral coefficients), RMS (root-mean-square) energy and Chroma frequencies. These features were used as input to an MLP (Multi-Layered Perceptron) neural network and trained for four different classes (dry and wet conditions with speed of 40 rpm and 60 rpm each). MLP model training and testing were implemented in Keras deep learning library. MLPs can automatically learn useful and meaningful relationships among the input features for classifying the input audio sample to one of the output classes/categories. For four output categories considered, 100% classification accuracy was achieved on the test set while accuracies of 99.56%, 73.24%, and 63.59% were achieved for three validation sets consisting of audio samples of varying noise levels.
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Wang, Jun, and Shengchen Li. "Comparing the Influence of Depth and Width of Deep Neural Network Based on Fixed Number of Parameters for Audio Event Detection." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461713.

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Obolonin, Ivan A., and Natalya A. Rygovskaya. "The analysis of filtration influence at primary pulsed-code conversion an distortion of input signals of coders with a compression of audio data." In 2009 International Conference and Seminar on Micro/Nanotechnologies and Electron Devices (EDM). IEEE, 2009. http://dx.doi.org/10.1109/edm.2009.5173963.

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Reports on the topic "Influence of audio signal"

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Sapiro, Guillermo. Structured and Collaborative Signal Models: Theory and Applications in Image, Video, and Audio Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada586672.

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Stender, Anthony. Rod-like plasmonic nanoparticles as optical building blocks: how differences in particle shape and structural geometry influence optical signal. Office of Scientific and Technical Information (OSTI), January 2013. http://dx.doi.org/10.2172/1116721.

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Pearman, T. R. R., A. Bates, K. Robert, A. Callaway, C. Lo Iacono, R. Hall, and V. A I Huvenne. The influence of the scale of enquiry and estimated biological parameters on the biological signal obtained from underwater video data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305910.

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Day, Christopher M., Hiromal Premachandra, and Darcy M. Bullock. Characterizing the Impacts of Phasing, Environment, and Temporal Factors on Pedestrian Demand at Traffic Signals. Purdue University, 2011. http://dx.doi.org/10.5703/1288284317352.

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There is a need for more and higher quality data on pedestrian demand patterns for a number of applications in planning, transportation engineering, public health, and other areas. It is particularly desirable to better characterize the influence of daily, weekly, and annual variations; the impact of weather and special events; and the effects of changes in pedestrian phasing. This paper proposes and demonstrates a methodology for quantifying the relative demand for pedestrian service at a signalized intersection by using the percent of signal cycles per hour in which the pedestrian phase was actuated. Although this performance measure does not by itself provide a pedestrian count, it can be used as a surrogate to characterize how pedestrian volumes vary due to operating conditions. More importantly, since this technique does not require new sensors, the data can be collected at thousands of intersections across the nation where pedestrian push buttons are in use. This paper documents findings from over a year of data collection at a signalized intersection on a college campus. The effects of daily/weekly/annual variations, special events, weather (temperature and precipitation), seasonal changes in activity patterns, and changes in pedestrian signal phasing are documented. A Tobit model is used to account for the influences of these variables and understand how they co-influence pedestrian activity. The implementation of an exclusive pedestrian phase is associated with a 9% increase in pedestrian phase utilization at the intersection. This change is associated with a decrease in user cost relative to performing midblock crossings. The modeled impact of snowfall events adds further insight by showing that as the user cost of making midblock crossings increases, pedestrian activity at the intersection increases.
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Baluk, Nadia, Natalia Basij, Larysa Buk, and Olha Vovchanska. VR/AR-TECHNOLOGIES – NEW CONTENT OF THE NEW MEDIA. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11074.

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The article analyzes the peculiarities of the media content shaping and transformation in the convergent dimension of cross-media, taking into account the possibilities of augmented reality. With the help of the principles of objectivity, complexity and reliability in scientific research, a number of general scientific and special methods are used: method of analysis, synthesis, generalization, method of monitoring, observation, problem-thematic, typological and discursive methods. According to the form of information presentation, such types of media content as visual, audio, verbal and combined are defined and characterized. The most important in journalism is verbal content, it is the one that carries the main information load. The dynamic development of converged media leads to the dominance of image and video content; the likelihood of increasing the secondary content of the text increases. Given the market situation, the effective information product is a combined content that combines text with images, spreadsheets with video, animation with infographics, etc. Increasing number of new media are using applications and website platforms to interact with recipients. To proceed, the peculiarities of the new content of new media with the involvement of augmented reality are determined. Examples of successful interactive communication between recipients, the leading news agencies and commercial structures are provided. The conditions for effective use of VR / AR-technologies in the media content of new media, the involvement of viewers in changing stories with augmented reality are determined. The so-called immersive effect with the use of VR / AR-technologies involves complete immersion, immersion of the interested audience in the essence of the event being relayed. This interaction can be achieved through different types of VR video interactivity. One of the most important results of using VR content is the spatio-temporal and emotional immersion of viewers in the plot. The recipient turns from an external observer into an internal one; but his constant participation requires that the user preferences are taken into account. Factors such as satisfaction, positive reinforcement, empathy, and value influence the choice of VR / AR content by viewers.
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Corriveau, Elizabeth, Ashley Mossell, Holly VerMeulen, Samuel Beal, and Jay Clausen. The effectiveness of laser-induced breakdown spectroscopy (LIBS) as a quantitative tool for environmental characterization. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40263.

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Laser-induced breakdown spectroscopy (LIBS) is a rapid, low-cost analytical method with potential applications for quantitative analysis of soils for heavy metal contaminants found in military ranges. The Department of Defense (DoD), Army, and Department of Homeland Security (DHS) have mission requirements to acquire the ability to detect and identify chemicals of concern in the field. The quantitative potential of a commercial off-the-shelf (COTS) hand-held LIBS device and a classic laboratory bench-top LIBS system was examined by measuring heavy metals (antimony, tungsten, iron, lead, and zinc) in soils from six military ranges. To ensure the accuracy of the quantified results, we also examined the soil samples using other hand-held and bench-top analytical methods, to include Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and X-Ray Fluorescence (XRF). The effects of soil heterogeneity on quantitative analysis were reviewed with hand-held and bench-top systems and compared multivariate and univariate calibration algorithms for heavy metal quantification. In addition, the influence of cold temperatures on signal intensity and resulting concentration were examined to further assess the viability of this technology in cold environments. Overall, the results indicate that additional work should be performed to enhance the ability of LIBS as a reliable quantitative analytical tool.
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DeRobertis, Michelle, Christopher E. Ferrell, Richard W. Lee, and David Moore. City Best Practices to Improve Transit Operations and Safety. Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1951.

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Public, fixed-route transit services most commonly operate on public streets. In addition, transit passengers must use sidewalks to access transit stops and stations. However, streets and sidewalks are under the jurisdiction of municipalities, not transit agencies. Various municipal policies, practices, and decisions affect transit operations, rider convenience, and passenger safety. Thus, these government entities have an important influence over the quality, safety, and convenience of transit services in their jurisdictions. This research identified municipal policies and practices that affect public transport providers’ ability to deliver transit services. They were found from a comprehensive literature review, interviews and discussions with five local transit agencies in the U.S., five public transportation experts and staff from five California cities. The city policies and practices identified fall into the following five categories: Infrastructure for buses, including bus lanes, signal treatments, curbside access; Infrastructure for pedestrians walking and bicycling to, and waiting at, transit stops and stations; Internal transportation planning policies and practices; Land development review policies; Regional and metropolitan planning organization (MPO) issues. The understanding, acknowledgment, and implementation of policies and practices identified in this report can help municipalities proactively work with local transit providers to more efficiently and effectively operate transit service and improve passenger comfort and safety on city streets.
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Coplin, David L., Shulamit Manulis, and Isaac Barash. roles Hrp-dependent effector proteins and hrp gene regulation as determinants of virulence and host-specificity in Erwinia stewartii and E. herbicola pvs. gypsophilae and betae. United States Department of Agriculture, June 2005. http://dx.doi.org/10.32747/2005.7587216.bard.

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Gram-negative plant pathogenic bacteria employ specialized type-III secretion systems (TTSS) to deliver an arsenal of pathogenicity proteins directly into host cells. These secretion systems are encoded by hrp genes (for hypersensitive response and pathogenicity) and the effector proteins by so-called dsp or avr genes. The functions of effectors are to enable bacterial multiplication by damaging host cells and/or by blocking host defenses. We characterized essential hrp gene clusters in the Stewart's Wilt of maize pathogen, Pantoea stewartii subsp. stewartii (Pnss; formerly Erwinia stewartii) and the gall-forming bacterium, Pantoea agglomerans (formerly Erwinia herbicola) pvs. gypsophilae (Pag) and betae (Pab). We proposed that the virulence and host specificity of these pathogens is a function of a) the perception of specific host signals resulting in bacterial hrp gene expression and b) the action of specialized signal proteins (i.e. Hrp effectors) delivered into the plant cell. The specific objectives of the proposal were: 1) How is the expression of the hrp and effector genes regulated in response to host cell contact and the apoplastic environment? 2) What additional effector proteins are involved in pathogenicity? 3) Do the presently known Pantoea effector proteins enter host cells? 4) What host proteins interact with these effectors? We characterized the components of the hrp regulatory cascade (HrpXY ->7 HrpS ->7 HrpL ->7 hrp promoters), showed that they are conserved in both Pnss and Fag, and discovered that the regulation of the hrpS promoter (hrpSp) may be a key point in integrating apoplastic signals. We also analyzed the promoters recognized by HrpL and demonstrated the relationship between their composition and efficiency. Moreover, we showed that promoter strength can influence disease expression. In Pnss, we found that the HrpXY two-component signal system may sense the metabolic status of the bacterium and is required for full hrp gene expression in planta. In both species, acyl-homoserine lactone-mediated quorum sensing may also regulate epiphytic fitness and/or pathogenicity. A common Hrp effector protein, DspE/WtsE, is conserved and required for virulence of both species. When introduced into corn cells, Pnss WtsE protein caused water-soaked lesions. In other plants, it either caused cell death or acted as an Avr determinant. Using a yeast- two-hybrid system, WtsE was shown to interact with a number of maize signal transduction proteins that are likely to have roles in either programmed cell death or disease resistance. In Pag and Pab, we have characterized the effector proteins HsvG, HsvB and PthG. HsvG and HsvB are homologous proteins that determine host specificity of Pag and Pab on gypsophila and beet, respectively. Both possess a transcriptional activation domain that functions in yeast. PthG was found to act as an Avr determinant on multiple beet species, but was required for virulence on gypsophila. In addition, we demonstrated that PthG acts within the host cell. Additional effector genes have been characterized on the pathogenicity plasmid, pPATHₚₐg, in Pag. A screen for HrpL- regulated genes in Pnsspointed up 18 candidate effector proteins and four of these were required for full virulence. It is now well established that the virulence of Gram-negative plant pathogenic bacteria is governed by Hrp-dependent effector proteins. However; the mode of action of many effectors is still unresolved. This BARD supported research will significantly contribute to the understanding of how Hrp effectors operate in Pantoea spp. and how they control host specificity and affect symptom production. This may lead to novel approaches for genetically engineering plants resistant to a wide range of bacterial pathogens by inactivating the Hrp effectors with "plantabodies" or modifying their receptors, thereby blocking the induction of the susceptible response. Alternatively, innovative technologies could be used to interfere with the Hrp regulatory cascade by blocking a critical step or mimicking plant or quorum sensing signals.
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