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Letteratura scientifica selezionata sul tema "Vocoder"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "Vocoder"

1

Cychosz, Margaret, Matthew B. Winn, and Matthew J. Goupell. "How to vocode: Using channel vocoders for cochlear-implant research." Journal of the Acoustical Society of America 155, no. 4 (2024): 2407–37. http://dx.doi.org/10.1121/10.0025274.

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The channel vocoder has become a useful tool to understand the impact of specific forms of auditory degradation—particularly the spectral and temporal degradation that reflect cochlear-implant processing. Vocoders have many parameters that allow researchers to answer questions about cochlear-implant processing in ways that overcome some logistical complications of controlling for factors in individual cochlear implant users. However, there is such a large variety in the implementation of vocoders that the term “vocoder” is not specific enough to describe the signal processing used in these exp

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2

Karoui, Chadlia, Chris James, Pascal Barone, David Bakhos, Mathieu Marx, and Olivier Macherey. "Searching for the Sound of a Cochlear Implant: Evaluation of Different Vocoder Parameters by Cochlear Implant Users With Single-Sided Deafness." Trends in Hearing 23 (January 2019): 233121651986602. http://dx.doi.org/10.1177/2331216519866029.

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Cochlear implantation in subjects with single-sided deafness (SSD) offers a unique opportunity to directly compare the percepts evoked by a cochlear implant (CI) with those evoked acoustically. Here, nine SSD-CI users performed a forced-choice task evaluating the similarity of speech processed by their CI with speech processed by several vocoders presented to their healthy ear. In each trial, subjects heard two intervals: their CI followed by a certain vocoder in Interval 1 and their CI followed by a different vocoder in Interval 2. The vocoders differed either (i) in carrier type—(sinusoidal

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3

Roebel, Axel, and Frederik Bous. "Neural Vocoding for Singing and Speaking Voices with the Multi-Band Excited WaveNet." Information 13, no. 3 (2022): 103. http://dx.doi.org/10.3390/info13030103.

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The use of the mel spectrogram as a signal parameterization for voice generation is quite recent and linked to the development of neural vocoders. These are deep neural networks that allow reconstructing high-quality speech from a given mel spectrogram. While initially developed for speech synthesis, now neural vocoders have also been studied in the context of voice attribute manipulation, opening new means for voice processing in audio production. However, to be able to apply neural vocoders in real-world applications, two problems need to be addressed: (1) To support use in professional audi

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4

Harding, Eleanor, Etienne Gaudrain, Imke Hrycyk, et al. "Arousal but not valence: Music emotion categorization in normal hearing and cochlear implanted participants." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A287. http://dx.doi.org/10.1121/10.0018868.

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Perceiving acoustic cues that convey music emotion is challenging for cochlear implant (CI) users. Emotional arousal (stimulating/relaxing) can be conveyed by temporal cues such as tempo, while emotional valence (positive/negative) can be conveyed by spectral information salient to pitch and harmony. It is however unclear the extent to which other temporal and spectral features convey emotional arousal and valence in music, respectively. In 23 normal-hearing participants, we varied the quality of temporal and spectral content using vocoders during a music emotion categorization task—musical ex

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5

Ausili, Sebastian A., Bradford Backus, Martijn J. H. Agterberg, A. John van Opstal, and Marc M. van Wanrooij. "Sound Localization in Real-Time Vocoded Cochlear-Implant Simulations With Normal-Hearing Listeners." Trends in Hearing 23 (January 2019): 233121651984733. http://dx.doi.org/10.1177/2331216519847332.

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Bilateral cochlear-implant (CI) users and single-sided deaf listeners with a CI are less effective at localizing sounds than normal-hearing (NH) listeners. This performance gap is due to the degradation of binaural and monaural sound localization cues, caused by a combination of device-related and patient-related issues. In this study, we targeted the device-related issues by measuring sound localization performance of 11 NH listeners, listening to free-field stimuli processed by a real-time CI vocoder. The use of a real-time vocoder is a new approach, which enables testing in a free-field env

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6

Wess, Jessica M., and Joshua G. W. Bernstein. "The Effect of Nonlinear Amplitude Growth on the Speech Perception Benefits Provided by a Single-Sided Vocoder." Journal of Speech, Language, and Hearing Research 62, no. 3 (2019): 745–57. http://dx.doi.org/10.1044/2018_jslhr-h-18-0001.

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PurposeFor listeners with single-sided deafness, a cochlear implant (CI) can improve speech understanding by giving the listener access to the ear with the better target-to-masker ratio (TMR; head shadow) or by providing interaural difference cues to facilitate the perceptual separation of concurrent talkers (squelch). CI simulations presented to listeners with normal hearing examined how these benefits could be affected by interaural differences in loudness growth in a speech-on-speech masking task.MethodExperiment 1 examined a target–masker spatial configuration where the vocoded ear had a p

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7

Bosen, Adam K., and Michael F. Barry. "Serial Recall Predicts Vocoded Sentence Recognition Across Spectral Resolutions." Journal of Speech, Language, and Hearing Research 63, no. 4 (2020): 1282–98. http://dx.doi.org/10.1044/2020_jslhr-19-00319.

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Purpose The goal of this study was to determine how various aspects of cognition predict speech recognition ability across different levels of speech vocoding within a single group of listeners. Method We tested the ability of young adults ( N = 32) with normal hearing to recognize Perceptually Robust English Sentence Test Open-set (PRESTO) sentences that were degraded with a vocoder to produce different levels of spectral resolution (16, eight, and four carrier channels). Participants also completed tests of cognition (fluid intelligence, short-term memory, and attention), which were used as

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8

Yang, Jing, Jenna Barrett, Zhigang Yin, and Li Xu. "Recognition of foreign-accented vocoded speech by native English listeners." Acta Acustica 7 (2023): 43. http://dx.doi.org/10.1051/aacus/2023038.

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This study examined how talker accentedness affects the recognition of noise-vocoded speech by native English listeners and how contextual information interplays with talker accentedness during this process. The listeners included 20 native English-speaking, normal-hearing adults aged between 19 and 23 years old. The stimuli were English Hearing in Noise Test (HINT) and Revised Speech Perception in Noise (R-SPIN) sentences produced by four native Mandarin talkers (two males and two females) who learned English as a second language. Two talkers (one in each sex) had a mild foreign accent and th

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9

Bak, Taejun, Junmo Lee, Hanbin Bae, Jinhyeok Yang, Jae-Sung Bae, and Young-Sun Joo. "Avocodo: Generative Adversarial Network for Artifact-Free Vocoder." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12562–70. http://dx.doi.org/10.1609/aaai.v37i11.26479.

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Neural vocoders based on the generative adversarial neural network (GAN) have been widely used due to their fast inference speed and lightweight networks while generating high-quality speech waveforms. Since the perceptually important speech components are primarily concentrated in the low-frequency bands, most GAN-based vocoders perform multi-scale analysis that evaluates downsampled speech waveforms. This multi-scale analysis helps the generator improve speech intelligibility. However, in preliminary experiments, we discovered that the multi-scale analysis which focuses on the low-frequency

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10

Shi, Yong Peng. "Research and Implementation of MELP Algorithm Based on TMS320VC5509A." Advanced Materials Research 934 (May 2014): 239–44. http://dx.doi.org/10.4028/www.scientific.net/amr.934.239.

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A kind of MELP vocode is designed based on DSP TMS320VC5509A in this article. Firstly, it expatiates the MELP algorithm,then the idea of modeling and realization process on DSP based is proposed. At last we can complete the function simulation of the encoding and decoding system,and the experiment result shows that the synthetical signals fit well with the original ones, and the quality of the speech got from the vocoder is good.

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Tesi sul tema "Vocoder"

1

LeBlanc, Wilfrid P. (Wilfrid Paul) Carleton University Dissertation Engineering Electrical. "An advanced speech coder based on a rate-distortion theory framework." Ottawa, 1988.

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2

Griffin, Daniel W. "Multi-band excitation vocoder." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14803.

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3

Martins, José Antônio. "Vocoder LPC com quantização vetorial." [s.n.], 1991. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261389.

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Orientador : Fabio Violaro<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica<br>Made available in DSpace on 2018-07-13T23:59:40Z (GMT). No. of bitstreams: 1 Martins_JoseAntonio_M.pdf: 6784204 bytes, checksum: 4e9df50ca8f72e1710d541924b76a67c (MD5) Previous issue date: 1991<br>Resumo: Neste trabalho são descritos os princípios do vocoder LPC, sendo mostrados os métodos para cálculo dos parâmetros do mesmo. Também são apresentados os resultados de simulações de vocoders LPC usando quantização escalar, quantização vetorial e interpolação dos parâmet

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4

Hudson, Nicholaus D. W. "The self-excited vocoder for mobile telephony." Thesis, University of Bath, 1992. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760629.

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Moore, James Thomas. "A mixed excitation vocoder with fuzzy logic classifier." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23960.

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6

Foley, Jeffrey J. (Jeffrey Joseph). "Digital implementation of a frequency-lowering channel vocoder." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/38798.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.<br>Includes bibliographical references (p. 58-59).<br>by Jeffrey J. Foley.<br>M.Eng.

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Carr, Raymond C. "Improvements to a pitch-synchronous linear predictive coding (LPC) vocoder." Thesis, University of Ottawa (Canada), 1989. http://hdl.handle.net/10393/5954.

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8

Yeh, Ernest Nanjung 1975. "Advanced Vocoder Idle Slot Exploitation for TIA IS-136 standard." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47580.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.<br>Includes bibliographical references (p. 55).<br>by Ernest Nanjung Yeh.<br>S.B.and M.Eng.

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9

Manjunath, Sharath. "Implementation of a variable rate vocoder and its performance analysis." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06102009-063255/.

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Iyengar, Vasu. "A low delay 16 kbit/sec coder for speech signals /." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63799.

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Libri sul tema "Vocoder"

1

Surphlis, David S. Multi-band excitation vocoder. The Author], 1996.

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2

Redding, Christopher. Voice quality assessment of vocoders in tandem configuration. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 2001.

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3

N, DeMinco, Lindner Jeanne, United States. National Telecommunications and Information Administration, and Institute for Telecommunication Sciences, eds. Voice quality assessment of vocoders in tandem configuration. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 2001.

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4

Moore, James Thomas. A mixed excitation vocoder with fuzzy logic classifier. Naval Postgraduate School, 1992.

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5

Papamichalis, Panos E. Practical approaches to speech coding. Prentice-Hall, 1987.

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6

Tompkins, Dave. How to wreck a nice beach: The vocoder from World War II to hip-hop : the machine speaks. Melville House, 2011.

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7

Tompkins, Dave. How to wreck a nice beach: The vocoder from World War II to hip-hop : the machine speaks. Melville House, 2010.

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8

Ramamurthy, Karthikeyan N. MATLAB software for the code excited linear prediction algorithm: The Federal Standard, 1016. Morgan & Claypool Publishers, 2010.

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9

Jean-Pierre, Tubach, Boë Louis-Jean, and Calliope (Association), eds. La Parole et son traitement automatique. Masson, 1989.

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10

Carroll, Angela, and Charles Moore. Vocoder. Petite Ivy Press, 2021.

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Capitoli di libri sul tema "Vocoder"

1

Weik, Martin H. "vocoder." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_20884.

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2

Drymonitis, Alexandros. "Phase Vocoder Techniques." In The Python Audio Cookbook. Focal Press, 2023. http://dx.doi.org/10.4324/9781003386964-4.

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3

Chung, Jae H., and Ronald W. Schafer. "Vector Excitation Homomorphic Vocoder." In Advances in Speech Coding. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3266-8_23.

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4

Gerstlauer, Andreas. "Design of a GSM Vocoder." In System Design. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1481-7_3.

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Pirkle, Will C. "FFT Processing: The Phase Vocoder." In Designing Audio Effect Plugins in C++. Routledge, 2019. http://dx.doi.org/10.4324/9780429490248-20.

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Vít, Jakub, Zdeněk Hanzlíček, and Jindřich Matoušek. "Czech Speech Synthesis with Generative Neural Vocoder." In Text, Speech, and Dialogue. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27947-9_26.

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7

Sun, Chengzhe, Ehab AlBadawy, Timothy F. Davison, Sarah R. Robinson, Ming-Ching Chang, and Siwei Lyu. "Using Vocoder Artifacts For Audio Deepfakes Detection." In Adversarial Multimedia Forensics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49803-9_11.

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8

Vondra, Martin, and Robert Vích. "Speech Emotion Modification Using a Cepstral Vocoder." In Development of Multimodal Interfaces: Active Listening and Synchrony. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12397-9_23.

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Loizou, Philipos C. "Speech Processing in Vocoder-Centric Cochlear Implants." In Cochlear and Brainstem Implants. S. KARGER AG, 2006. http://dx.doi.org/10.1159/000094648.

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Lu, Tangle, and Xiaoqun Zhao. "An MELP Vocoder Based on UVS and MVF." In Machine Learning and Intelligent Communications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52730-7_5.

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Atti di convegni sul tema "Vocoder"

1

Lanzendörfer, Luca A., Florian Grötschla, Michael Ungersböck, and Roger Wattenhofer. "High-Fidelity Music Vocoder using Neural Audio Codecs." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890252.

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2

Akiyoshi, Shingo, Ken’ichi Furuya, and Hideyuki Mizuno. "F0 Controllable General-Purpose Vocoder Enhanced Using Speaker Vectors." In 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE). IEEE, 2024. https://doi.org/10.1109/gcce62371.2024.10760838.

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3

Liu, Zhijun, Kuan Chen, and Kai Yu. "Neural Homomorphic Vocoder." In Interspeech 2020. ISCA, 2020. http://dx.doi.org/10.21437/interspeech.2020-3188.

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4

Ribeiro, Carlos M., Isabel M. Trancoso, and Diamantino A. Caseiro. "Phonetic vocoder assessment." In 6th International Conference on Spoken Language Processing (ICSLP 2000). ISCA, 2000. http://dx.doi.org/10.21437/icslp.2000-663.

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5

Barbany, Oriol, Antonio Bonafonte, and Santiago Pascual. "Multi-Speaker Neural Vocoder." In IberSPEECH 2018. ISCA, 2018. http://dx.doi.org/10.21437/iberspeech.2018-7.

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6

Tamamori, Akira, Tomoki Hayashi, Kazuhiro Kobayashi, Kazuya Takeda, and Tomoki Toda. "Speaker-Dependent WaveNet Vocoder." In Interspeech 2017. ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-314.

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Prusa, Zdenek, and Nicki Holighaus. "Phase vocoder done right." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081353.

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Cohen, Aaron E., Yvette T. Lee, and David A. Heide. "Vocoder susceptibility to baseband Trojans." In 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). IEEE, 2017. http://dx.doi.org/10.1109/uemcon.2017.8249068.

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Obranovich, Charles R., John M. Golusky, Robert D. Preuss, et al. "300 bps noise robust vocoder." In MILCOM 2010 - 2010 IEEE Military Communications Conference. IEEE, 2010. http://dx.doi.org/10.1109/milcom.2010.5680311.

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Moreno, Asunción, José A. R. Fonollosa, and Josep Vidal. "Vocoder design based on HOS." In 3rd European Conference on Speech Communication and Technology (Eurospeech 1993). ISCA, 1993. http://dx.doi.org/10.21437/eurospeech.1993-25.

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Rapporti di organizzazioni sul tema "Vocoder"

1

Heide, David A., Aaron E. Cohen, Yvette T. Lee, and Thomas M. Moran. Universal Vocoder Using Variable Data Rate Vocoding. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada588068.

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2

Mack, M. A., and B. Gold. The Intelligibility of Non-Vocoded and Vocoded Semantically Anomalous Sentences. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160401.

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Redding, Christopher, Nicholas DeMinco, and Jeanne Lindner. Voice Quality Assessment of Vocoders in Tandem Configuration. Institute for Telecommunication Sciences, 2001. https://doi.org/10.70220/54jv3154.

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4

Li, A. RTP Payload Format for Enhanced Variable Rate Codecs (EVRC) and Selectable Mode Vocoders (SMV). RFC Editor, 2003. http://dx.doi.org/10.17487/rfc3558.

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Mack, M., J. Tierney, and M. E. Boyle. The Intelligibility of Natural and LPC-Vocoded Words and Sentences Presented to Native and Non-Native Speakers of English. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada226180.

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