Tag: synthesis

Singing Synthesis Beyond Human-Level Naturalness: Not What You Think – Kanru Hua – ADC23

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Singing Synthesis Beyond Human-Level Naturalness: Not What You Think - Kanru Hua - ADC 2023

Achieving human-level naturalness is often viewed as the pinnacle of vocal synthesis research. While recent advances in Text-to-Speech (TTS) using deep generative models has reported subjective ratings comparable to human speech, singing synthesis hasn't reached this milestone. In this presentation, we showcase a singing synthesis system that, intriguingly, exceeds raw recordings in comparative mean opinion score tests—with statistical significance. However, as we delve deeper, we highlight the subtle but crucial differences between true human parity and competitive ratings in subjective tests, challenging our understanding of “naturalness” in this domain. We will also unpack the complexities of subjective quality evaluation, the unique challenges posed by singing versus speech, and shed light on the implications these findings hold for future designs of singing synthesis systems.

Link to Slides:
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Kanru Hua

Kanru Hua founded Dreamtonics (developer of Synthesizer V) in 2019, after dropping out of University of Illinois. A self-taught programmer and researcher, Kanru has been focusing on bridging speech signal processing algorithms with the latest advances in generative models, as well as addressing the production challenges of deploying neural networks for audio processing. He was nominated for Forbes JAPAN 30 UNDER 30 in 2022.
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Streamed & Edited by Digital Medium Ltd: https://online.digital-medium.co.uk
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Organized and produced by JUCE: https://juce.com/
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Special thanks to the ADC23 Team:

Sophie Carus
Derek Heimlich
Andrew Kirk
Bobby Lombardi
Tom Poole
Ralph Richbourg
Jim Roper
Jonathan Roper
Prashant Mishra

#adc #audiodev #dsp #audio #audiotech

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Creating Your Own Singing Voice Synthesizer: Overcoming Data Collection Challenges – by @mattricesound – ADCx SF

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Join Us For ADC23 - London - 13-15 November 2023
More Info: https://audio.dev/
@audiodevcon

Creating Your Own Singing Voice Synthesizer: Overcoming Data Collection Challenges - Matthew Rice - ADCx SF

While singing voice synthesizers have existed for decades, recent deep-learning-based products (Sinsy, Vocaloid) have greatly improved the quality of the results. However, these systems provide only a limited number of pre-trained "voices" based on proprietary datasets. Luckily, open-source systems (NNSVS, OpenUtau, VISinger, DiffSinger) exist, allowing users to use custom datasets to create a singing voice synthesizer. Unfortunately, creating the necessary datasets is a time-consuming process that requires collecting phoneme-level timing and other data points. As a result, few public datasets are available, and those that do exist are mostly restricted to Mandarin Chinese and Japanese. In this talk, I will demonstrate several approaches to collecting this data, from manual labeling to fully automated procedures, making it easier for everyone to create their own personalized singing voice synthesizers.

Slides: https://data.audio.dev/talks/ADCxSF/2023/creating-your-own-singing-voice-synthesizer/slides.pdf
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Matthew Rice

Matthew Rice is a master's student at Queen Mary University of London, studying Sound and Music Computing with a focus on music production applications of deep learning. Previously, Matthew was at startup Mayk as a software engineer, working on both the audio engine and audio research teams. Matthew also has experience in digital hardware and embedded systems, having worked at Qualcomm designing PMICs and audio codec drivers.

Edited by Digital Medium Ltd - online.digital-medium.co.uk
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Organized and produced by JUCE: https://juce.com/
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Special thanks to the ADC Team:

Sophie Carus
Derek Heimlich
Andrew Kirk
Bobby Lombardi
Tom Poole
Ralph Richbourg
Jim Roper
Jonathan Roper

#audiodevcon #audiodev #synthesizer

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