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Relevant bibliographies by topics / Speech and audio signals

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Academic literature on the topic 'Speech and audio signals'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Speech and audio signals"

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Rao*, G. Manmadha, Raidu Babu D.N, Krishna Kanth P.S.L, Vinay B., and Nikhil V. "Reduction of Impulsive Noise from Speech and Audio Signals by using Sd-Rom Algorithm." International Journal of Recent Technology and Engineering 10, no. 1 (2021): 265–68. http://dx.doi.org/10.35940/ijrte.a5943.0510121.

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Removal of noise is the heart for speech and audio signal processing. Impulse noise is one of the most important noise which corrupts different parts in speech and audio signals. To remove this type of noise from speech and audio signals the technique proposed in this work is signal dependent rank order mean (SD-ROM) method in recursive version. This technique is used to replace the impulse noise samples based on the neighbouring samples. It detects the impulse noise samples based on the rank ordered differences with threshold values. This technique doesn’t change the features and tonal qualit

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S. Ashwin, J., and N. Manoharan. "Audio Denoising Based on Short Time Fourier Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 1 (2018): 89. http://dx.doi.org/10.11591/ijeecs.v9.i1.pp89-92.

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<p>This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio de-noising technique based on Short Time Fourier Transform (STFT) is implemented. The proposed architecture uses a novel approach to estimate environmental noise from speech adaptively. Here original speech signals are given as input signal. Using AWGN, noises are added to the signal. Then noised s

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Kacur, Juraj, Boris Puterka, Jarmila Pavlovicova, and Milos Oravec. "Frequency, Time, Representation and Modeling Aspects for Major Speech and Audio Processing Applications." Sensors 22, no. 16 (2022): 6304. http://dx.doi.org/10.3390/s22166304.

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There are many speech and audio processing applications and their number is growing. They may cover a wide range of tasks, each having different requirements on the processed speech or audio signals and, therefore, indirectly, on the audio sensors as well. This article reports on tests and evaluation of the effect of basic physical properties of speech and audio signals on the recognition accuracy of major speech/audio processing applications, i.e., speech recognition, speaker recognition, speech emotion recognition, and audio event recognition. A particular focus is on frequency ranges, time

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Nittrouer, Susan, and Joanna H. Lowenstein. "Beyond Recognition: Visual Contributions to Verbal Working Memory." Journal of Speech, Language, and Hearing Research 65, no. 1 (2022): 253–73. http://dx.doi.org/10.1044/2021_jslhr-21-00177.

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Purpose: It is well recognized that adding the visual to the acoustic speech signal improves recognition when the acoustic signal is degraded, but how that visual signal affects postrecognition processes is not so well understood. This study was designed to further elucidate the relationships among auditory and visual codes in working memory, a postrecognition process. Design: In a main experiment, 80 young adults with normal hearing were tested using an immediate serial recall paradigm. Three types of signals were presented (unprocessed speech, vocoded speech, and environmental sounds) in thr

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B, Nagesh, and Dr M. Uttara Kumari. "A Review on Machine Learning for Audio Applications." Journal of University of Shanghai for Science and Technology 23, no. 07 (2021): 62–70. http://dx.doi.org/10.51201/jusst/21/06508.

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Audio processing is an important branch under the signal processing domain. It deals with the manipulation of the audio signals to achieve a task like filtering, data compression, speech processing, noise suppression, etc. which improves the quality of the audio signal. For applications such as natural language processing, speech generation, automatic speech recognition, the conventional algorithms aren’t sufficient. There is a need for machine learning or deep learning algorithms which can be implemented so that the audio signal processing can be achieved with good results and accuracy. In th

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Kubanek, M., J. Bobulski, and L. Adrjanowicz. "Characteristics of the use of coupled hidden Markov models for audio-visual polish speech recognition." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 2 (2012): 307–16. http://dx.doi.org/10.2478/v10175-012-0041-6.

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Abstract. This paper focuses on combining audio-visual signals for Polish speech recognition in conditions of the highly disturbed audio speech signal. Recognition of audio-visual speech was based on combined hidden Markov models (CHMM). The described methods were developed for a single isolated command, nevertheless their effectiveness indicated that they would also work similarly in continuous audiovisual speech recognition. The problem of a visual speech analysis is very difficult and computationally demanding, mostly because of an extreme amount of data that needs to be processed. Therefor

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Timmermann, Johannes, Florian Ernst, and Delf Sachau. "Speech enhancement for helicopter headsets with an integrated ANC-system for FPGA-platforms." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 5 (2023): 2720–30. http://dx.doi.org/10.3397/in_2022_0382.

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During flights, helicopter pilots are exposed to high noise levels caused by rotor, engine and wind. To protect the health of passengers and crew, noise-dampening headsets are used. Modern active noise control (ANC) headset can further reduce the noise exposure for humans in helicopters. Internal or external voice transmission in the helicopter must be adapted to the noisy environment and speech signals are therefore heavily amplified. To improve the quality of communication in helicopters speech and background noise in the transmitted audio signals should be separated. Subsequently the noise

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Abdallah, Hanaa A., and Souham Meshoul. "A Multilayered Audio Signal Encryption Approach for Secure Voice Communication." Electronics 12, no. 1 (2022): 2. http://dx.doi.org/10.3390/electronics12010002.

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In this paper, multilayer cryptosystems for encrypting audio communications are proposed. These cryptosystems combine audio signals with other active concealing signals, such as speech signals, by continuously fusing the audio signal with a speech signal without silent periods. The goal of these cryptosystems is to prevent unauthorized parties from listening to encrypted audio communications. Preprocessing is performed on both the speech signal and the audio signal before they are combined, as this is necessary to get the signals ready for fusion. Instead of encoding and decoding methods, the

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Yin, Shu Hua. "Design of the Auxiliary Speech Recognition System of Super-Short-Range Reconnaissance Radar." Applied Mechanics and Materials 556-562 (May 2014): 4830–34. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4830.

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To improve the usability and operability of the hybrid-identification reconnaissance radar for individual use, a voice identification System was designed. By using SPCE061A audio signal microprocessor as the core, a digital signal processing technology was used to obtain Doppler radar signals of audio segments by audio cable. Afterwards, the A/D acquisition was conducted to acquire digital signals, and then the data obtained were preprocessed and adaptively filtered to eliminate background noises. Moreover, segmented FFT transforming was used to identify the types of the signals. The overall d

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Moore, Brian C. J. "Binaural sharing of audio signals." Hearing Journal 60, no. 11 (2007): 46–48. http://dx.doi.org/10.1097/01.hj.0000299172.13153.6f.

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Dissertations / Theses on the topic "Speech and audio signals"

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Mason, Michael. "Hybrid coding of speech and audio signals." Thesis, Queensland University of Technology, 2001.

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Trinkaus, Trevor R. "Perceptual coding of audio and diverse speech signals." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/13883.

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Mészáros, Tomáš. "Speech Analysis for Processing of Musical Signals." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234974.

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Hlavním cílem této práce je obohatit hudební signály charakteristikami lidské řeči. Práce zahrnuje tvorbu audioefektu inspirovaného efektem talk-box: analýzu hlasového ústrojí vhodným algoritmem jako je lineární predikce, a aplikaci odhadnutého filtru na hudební audio-signál. Důraz je kladen na dokonalou kvalitu výstupu, malou latenci a nízkou výpočetní náročnost pro použití v reálném čase. Výstupem práce je softwarový plugin využitelný v profesionálních aplikacích pro úpravu audia a při využití vhodné hardwarové platformy také pro živé hraní. Plugin emuluje reálné zařízení typu talk-box a pos

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Choi, Hyung Keun. "Blind source separation of the audio signals in a real world." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/14986.

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Lucey, Simon. "Audio-visual speech processing." Thesis, Queensland University of Technology, 2002. https://eprints.qut.edu.au/36172/7/SimonLuceyPhDThesis.pdf.

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Speech is inherently bimodal, relying on cues from the acoustic and visual speech modalities for perception. The McGurk effect demonstrates that when humans are presented with conflicting acoustic and visual stimuli, the perceived sound may not exist in either modality. This effect has formed the basis for modelling the complementary nature of acoustic and visual speech by encapsulating them into the relatively new research field of audio-visual speech processing (AVSP). Traditional acoustic based speech processing systems have attained a high level of performance in recent years, but the p

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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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Zeghidour, Neil. "Learning representations of speech from the raw waveform." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE004/document.

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Bien que les réseaux de neurones soient à présent utilisés dans la quasi-totalité des composants d’un système de reconnaissance de la parole, du modèle acoustique au modèle de langue, l’entrée de ces systèmes reste une représentation analytique et fixée de la parole dans le domaine temps-fréquence, telle que les mel-filterbanks. Cela se distingue de la vision par ordinateur, un domaine où les réseaux de neurones prennent en entrée les pixels bruts. Les mel-filterbanks sont le produit d’une connaissance précieuse et documentée du système auditif humain, ainsi que du traitement du signal, et son

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Bando, Yoshiaki. "Robust Audio Scene Analysis for Rescue Robots." Kyoto University, 2018. http://hdl.handle.net/2433/232410.

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Moghimi, Amir Reza. "Array-based Spectro-temporal Masking For Automatic Speech Recognition." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/334.

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Over the years, a variety of array processing techniques have been applied to the problem of enhancing degraded speech to improve automatic speech recognition. In this context, linear beamforming has long been the approach of choice, for reasons including good performance, robustness and analytical simplicity. While various non-linear techniques - typically based to some extent on the study of auditory scene analysis - have also been of interest, they tend to lag behind their linear counterparts in terms of simplicity, scalability and exibility. Nonlinear techniques are also more difficult to

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Brangers, Kirstin M. "Perceptual Ruler for Quantifying Speech Intelligibility in Cocktail Party Scenarios." UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/31.

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Systems designed to enhance intelligibility of speech in noise are difficult to evaluate quantitatively because intelligibility is subjective and often requires feedback from large populations for consistent evaluations. Attempts to quantify the evaluation have included related measures such as the Speech Intelligibility Index. These require separating speech and noise signals, which precludes its use on experimental recordings. This thesis develops a procedure using an Intelligibility Ruler (IR) for efficiently quantifying intelligibility. A calibrated Mean Opinion Score (MOS) method is also

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Books on the topic "Speech and audio signals"

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

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Deller, John R. Discrete-time processing of speech signals. Institute of Electrical and Electronics Engineers, 2000.

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G, Proakis John, and Hansen John H. L, eds. Discrete-time processing of speech signals. Macmillan Pub. Co., 1993.

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V, Madisetti, ed. Video, speech, and audio signal processing and associated standards. CRC Press, 2009.

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V, Madisetti, ed. Video, speech, and audio signal processing and associated standards. CRC Press, 2009.

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Gold, Bernard. Speech and audio signal processing: Processing and perception of speech and music. 2nd ed. Wiley, 2011.

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Nelson, Morgan, ed. Speech and audio signal processing: Processing and perception of speech and music. John Wiley, 2000.

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Madisetti, V. Video, speech, and audio signal processing and associated standards. 2nd ed. CRC Press, 2010.

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Madisetti, V. Video, speech, and audio signal processing and associated standards. 2nd ed. CRC Press, 2010.

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S, Atal Bishnu, Cuperman Vladimir, and Gersho Allen, eds. Speech and audio coding for wireless and network applications. Kluwer Academic Publishers, 1993.

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Book chapters on the topic "Speech and audio signals"

1

Buchanan, William J. "Speech and Audio Signals." In Advanced Data Communications and Networks. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8670-2_8.

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Buchanan, Bill. "Speech and Audio Signals." In Handbook of Data Communications and Networks. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-0905-6_9.

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Buchanan, W. J. "Speech and Audio Signals." In The Handbook of Data Communications and Networks. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4020-7870-5_19.

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Buchanan, W. "Speech and Audio Signals." In Advanced Data Communications and Networks. CRC Press, 2023. http://dx.doi.org/10.1201/9781003420415-8.

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Richter, Michael M., Sheuli Paul, Veton Këpuska, and Marius Silaghi. "Audio Signals and Speech Recognition." In Signal Processing and Machine Learning with Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-45372-9_18.

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Buchner, Herbert, and Walter Kellermann. "TRINICON for Dereverberation of Speech and Audio Signals." In Speech Dereverberation. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-056-4_10.

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Douglas, Scott C., and Malay Gupta. "Convolutive Blind Source Separation for Audio Signals." In Blind Speech Separation. Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6479-1_1.

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Herre, Jürgen, and Manfred Lutzky. "Perceptual Audio Coding of Speech Signals." In Springer Handbook of Speech Processing. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-49127-9_18.

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Kellermann, Walter. "Beamforming for Speech and Audio Signals." In Handbook of Signal Processing in Acoustics. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-30441-0_35.

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Shamsi, Meysam, Nelly Barbot, Damien Lolive, and Jonathan Chevelu. "Mixing Synthetic and Recorded Signals for Audio-Book Generation." In Speech and Computer. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60276-5_46.

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Conference papers on the topic "Speech and audio signals"

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Shriver, Stefanie, Alan W. Black, and Ronald Rosenfeld. "Audio signals in speech interfaces." In 6th International Conference on Spoken Language Processing (ICSLP 2000). ISCA, 2000. http://dx.doi.org/10.21437/icslp.2000-35.

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"Signal Processing. Speech and Audio Processing." In 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2022. http://dx.doi.org/10.1109/iwssip55020.2022.9854416.

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Phan, Huy, Lars Hertel, Marco Maass, Radoslaw Mazur, and Alfred Mertins. "Representing nonspeech audio signals through speech classification models." In Interspeech 2015. ISCA, 2015. http://dx.doi.org/10.21437/interspeech.2015-682.

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Kammerl, Julius, Neil Birkbeck, Sasi Inguva, et al. "Temporal synchronization of multiple audio signals." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854474.

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"Main Track: Speech and Audio Processing." In 2020 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2020. http://dx.doi.org/10.1109/iwssip48289.2020.9145083.

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Movassagh, Mahmood, Joachim Thiemann, and Peter Kabal. "Joint entropy-scalable coding of audio signals." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288537.

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van der Waal, R. G., and R. N. J. Veldhuis. "Subband coding of stereophonic digital audio signals." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.151053.

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Rajagopalan, R., and B. Subramanian. "Removal of impulse noise from audio and speech signals." In International Symposium on Signals, Circuits and Systems, 2003. SCS 2003. IEEE, 2003. http://dx.doi.org/10.1109/scs.2003.1226973.

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Ziolko, Mariusz, Bartosz Ziolko, and Rafal Samborski. "Dual-Microphone Speech Extraction from Signals with Audio Background." In 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2009. http://dx.doi.org/10.1109/iih-msp.2009.34.

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Braun, Jerome J., and Haim Levkowitz. "Internet-oriented visualization with audio presentation of speech signals." In Photonics West '98 Electronic Imaging, edited by Robert F. Erbacher and Alex Pang. SPIE, 1998. http://dx.doi.org/10.1117/12.309555.

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Reports on the topic "Speech and audio signals"

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DeLeon, Phillip L. Techniques for Preprocessing Speech Signals for More Effective Audio Interfaces. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada412195.

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Mammone, Richard J., Khaled Assaleh, Kevin Farrell, Ravi Ramachandran, and Mihailo Zilovic. A Modulation Model for Characterizing Speech Signals. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada311661.

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Spittka, J., and K. Vos. RTP Payload Format for the Opus Speech and Audio Codec. RFC Editor, 2015. http://dx.doi.org/10.17487/rfc7587.

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Herrnstein, A. Start/End Delays of Voiced and Unvoiced Speech Signals. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/15006006.

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Chan, A. D., K. Englehart, B. Hudgins, and D. F. Lovely. Hidden Markov Model Classification of Myoelectric Signals in Speech. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada410037.

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STANDARD OBJECT SYSTEMS INC. Advanced Audio Interface for Phonetic Speech Recognition in a High Noise Environment. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada373461.

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Nelson, W. T., Robert S. Bolia, Mark A. Ericson, and Richard L. McKinley. Spatial Audio Displays for Speech Communications: A Comparison of Free Field and Virtual Acoustic Environments. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada430289.

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Nelson, W. T., Robert S. Bolia, Mark A. Ericson, and Richard L. McKinley. Monitoring the Simultaneous Presentation of Spatialized Speech Signals in a Virtual Acoustic Environment. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada430284.

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Nelson, W. T., Robert S. Bolia, Mark A. Ericson, and Richard L. McKinley. Monitoring the Simultaneous Presentation of Multiple Spatialized Speech Signals in the Free Field. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada430298.

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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from t

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