Vocoder Development: Difference between revisions
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== Introduction == | == Introduction == | ||
First emerged in the 1930s, the term "vocoder" is derived from "voice" and "coder", and was initially conceived for telecommunication purposes. In the 1950s, Lincoln Laboratory conducted research on detecting pitch in speech, which subsequently influenced the development of voice coders, commonly known as vocoders. These devices are designed to decrease the amount of bandwidth required for transmitting speech. This reduction in bandwidth offers two advantages: it reduces the cost of transmitting and receiving speech, while also enhancing the potential for maintaining privacy.<ref>https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=6fb19070c1314f599ecbd134672df89fba037155</ref> | First emerged in the 1930s, the term "vocoder" is derived from "voice" and "coder", and was initially conceived for telecommunication purposes. In the 1950s, Lincoln Laboratory conducted research on detecting pitch in speech, which subsequently influenced the development of voice coders, commonly known as vocoders. These devices are designed to decrease the amount of bandwidth required for transmitting speech. This reduction in bandwidth offers two advantages: it reduces the cost of transmitting and receiving speech, while also enhancing the potential for maintaining privacy.<ref>Gold, Bernard. “Gold A History of Vocoder Research at Lincoln Laboratory.”, [https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=6fb19070c1314f599ecbd134672df89fba037155 The Lincoln Laboratory Journal. Volume 3. Number 2 (1990)]</ref> | ||
Initially, vocoder described a device designed to compress speech for efficient transmission over telephone lines. The idea involved splitting speech into parts using filters and then reconstructing it on the other end. The goal was to save bandwidth, but in practice, early vocoders faced challenges in preserving the natural quality of speech. Additionally, the original vocoder only transmitted loudness, not sound quality | Initially, a vocoder described a device designed to compress speech for efficient transmission over telephone lines. The idea involved splitting speech into parts using filters and then reconstructing it on the other end. The goal was to save bandwidth, but in practice, early vocoders faced challenges in preserving the natural quality of speech. Additionally, the original vocoder only transmitted loudness, not sound quality<ref name=":0">Gordon, John William, and John Strawn. ''[https://ccrma.stanford.edu/files/papers/stanm55.pdf An introduction to the phase vocoder]'' No. 55. CCRMA, Department of Music, Stanford University, 1987.</ref>. | ||
As technology advanced, the phase vocoder emerged, preserving both the loudness and sound quality of speech. The phase vocoder is a significant advancement in vocoder technology and plays a pivotal role in modern speech synthesis and audio signal processing. It was developed to address critical limitations in early vocoders and revolutionized the way we process and manipulate audio signals. <ref name=":0" /> | As technology advanced, the phase vocoder emerged, preserving both the loudness and sound quality of speech. The phase vocoder is a significant advancement in vocoder technology and plays a pivotal role in modern speech synthesis and audio signal processing. It was developed to address critical limitations in early vocoders and revolutionized the way we process and manipulate audio signals. <ref name=":0" /> | ||
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== Future Research == | == Future Research == | ||
Despite the significant advancements in technology and the development of new speech synthesis systems, ranging from [[DECtalk Speech Synthesizer (1984)|DECtalk]] in the 1980s to the cutting-edge [[Advancements in AI TTS (2020s)|AI models]] of today, vocoders continue to play a key role in various applications. Today, vocoders are integral components of state-of-the-art speech synthesis systems, including WORLD<ref>Morise, Masanori, Fumiya Yokomori, e Kenji Ozawa. «WORLD: A Vocoder-Based High-Quality Speech Synthesis System for Real-Time Applications». ''IEICE Transactions on Information and Systems'' E99.D, fasc. 7 (2016): 1877–84. <nowiki>https://doi.org/10.1587/transinf.2015EDP7457</nowiki>.</ref>, designed specifically for real-time applications, and BigVGAN<ref>Lee, Sang-gil, Wei Ping, Boris Ginsburg, Bryan Catanzaro, and Sungroh Yoon. «BigVGAN: A Universal Neural Vocoder with Large-Scale Training». arXiv, 16 february 2023. <nowiki>http://arxiv.org/abs/2206.04658</nowiki>.</ref> , which uses the power of generative adversarial networks (GANs<ref>Rocca, Joseph. "Understanding generative adversarial networks (gans)." ''Medium'' 7 (2019): 20.</ref>). A majority of contemporary vocoders rely on neural networks for their operation, but improvements could be made in this direction too. Additionally, the integration of generative AI holds the promise of further enhancing the quality of vocoder-synthesized voices. | |||
Furthermore, vocoders are largely used in the field of music production, although we have yet to achieve the creation of truly authentic singing voices through them. The potential to explore and refine the use of vocoders in this context offers a pathway to a broader advancement in speech synthesis technology. | |||
Additionally, vocoders are currently deployed to address other challenges in voice technology. Vocoder-synthesized voices serve as tools in training noise-robust models<ref>Zheng, Nengheng, Yupeng Shi, Yuyong Kang, e Qinglin Meng. «A Noise-Robust Signal Processing Strategy for Cochlear Implants Using Neural Networks». In ''ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)'', 8343–47, 2021. <nowiki>https://doi.org/10.1109/ICASSP39728.2021.9413452</nowiki>.</ref> and detect fake audio<ref>Yan, Xinrui, Jiangyan Yi, Jianhua Tao, Chenglong Wang, Haoxin Ma, Tao Wang, Shiming Wang, e Ruibo Fu. «An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio». In ''Proceedings of the 1st International Workshop on Deepfake Detection for Audio Multimedia'', 61–68. DDAM ’22. New York, NY, USA: Association for Computing Machinery, 2022. <nowiki>https://doi.org/10.1145/3552466.3556525</nowiki>.</ref>. | |||
== LLM Review == | == LLM Review == | ||
Revision as of 14:50, 14 October 2023
Introduction
First emerged in the 1930s, the term "vocoder" is derived from "voice" and "coder", and was initially conceived for telecommunication purposes. In the 1950s, Lincoln Laboratory conducted research on detecting pitch in speech, which subsequently influenced the development of voice coders, commonly known as vocoders. These devices are designed to decrease the amount of bandwidth required for transmitting speech. This reduction in bandwidth offers two advantages: it reduces the cost of transmitting and receiving speech, while also enhancing the potential for maintaining privacy.[1]
Initially, a vocoder described a device designed to compress speech for efficient transmission over telephone lines. The idea involved splitting speech into parts using filters and then reconstructing it on the other end. The goal was to save bandwidth, but in practice, early vocoders faced challenges in preserving the natural quality of speech. Additionally, the original vocoder only transmitted loudness, not sound quality[2].
As technology advanced, the phase vocoder emerged, preserving both the loudness and sound quality of speech. The phase vocoder is a significant advancement in vocoder technology and plays a pivotal role in modern speech synthesis and audio signal processing. It was developed to address critical limitations in early vocoders and revolutionized the way we process and manipulate audio signals. [2]
Historical Context
Chenyi
Key Innovations
Amber
Impact
Wenjun
Future Research
Despite the significant advancements in technology and the development of new speech synthesis systems, ranging from DECtalk in the 1980s to the cutting-edge AI models of today, vocoders continue to play a key role in various applications. Today, vocoders are integral components of state-of-the-art speech synthesis systems, including WORLD[3], designed specifically for real-time applications, and BigVGAN[4] , which uses the power of generative adversarial networks (GANs[5]). A majority of contemporary vocoders rely on neural networks for their operation, but improvements could be made in this direction too. Additionally, the integration of generative AI holds the promise of further enhancing the quality of vocoder-synthesized voices.
Furthermore, vocoders are largely used in the field of music production, although we have yet to achieve the creation of truly authentic singing voices through them. The potential to explore and refine the use of vocoders in this context offers a pathway to a broader advancement in speech synthesis technology.
Additionally, vocoders are currently deployed to address other challenges in voice technology. Vocoder-synthesized voices serve as tools in training noise-robust models[6] and detect fake audio[7].
LLM Review
Erin
References
- ↑ Gold, Bernard. “Gold A History of Vocoder Research at Lincoln Laboratory.”, The Lincoln Laboratory Journal. Volume 3. Number 2 (1990)
- ↑ 2.0 2.1 Gordon, John William, and John Strawn. An introduction to the phase vocoder No. 55. CCRMA, Department of Music, Stanford University, 1987.
- ↑ Morise, Masanori, Fumiya Yokomori, e Kenji Ozawa. «WORLD: A Vocoder-Based High-Quality Speech Synthesis System for Real-Time Applications». IEICE Transactions on Information and Systems E99.D, fasc. 7 (2016): 1877–84. https://doi.org/10.1587/transinf.2015EDP7457.
- ↑ Lee, Sang-gil, Wei Ping, Boris Ginsburg, Bryan Catanzaro, and Sungroh Yoon. «BigVGAN: A Universal Neural Vocoder with Large-Scale Training». arXiv, 16 february 2023. http://arxiv.org/abs/2206.04658.
- ↑ Rocca, Joseph. "Understanding generative adversarial networks (gans)." Medium 7 (2019): 20.
- ↑ Zheng, Nengheng, Yupeng Shi, Yuyong Kang, e Qinglin Meng. «A Noise-Robust Signal Processing Strategy for Cochlear Implants Using Neural Networks». In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 8343–47, 2021. https://doi.org/10.1109/ICASSP39728.2021.9413452.
- ↑ Yan, Xinrui, Jiangyan Yi, Jianhua Tao, Chenglong Wang, Haoxin Ma, Tao Wang, Shiming Wang, e Ruibo Fu. «An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio». In Proceedings of the 1st International Workshop on Deepfake Detection for Audio Multimedia, 61–68. DDAM ’22. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3552466.3556525.
Team Members
Alice Vanni, Amber, Chenyi, Erin Shi, Wenjun