arXiv subjects

Mark Vinton

Publications and source records attributed to Mark Vinton.

3 recordsLinked to original sources

arXiv2026

Interpretable Destination-Aware Synthesizer Modulation Recovery

Synthesizer programming is a challenging task, particularly in configuring modulation: the time-varying control of parameters such as pitch, filter cutoff, and oscillator level by modulators such as Low-Frequency Oscillators (LFOs). Prior work has shown that modulation can be reconstructed from the clean audio through curve matching, yet the recovered parameters are often untransferable to modern synthesizers. We present an interpretable destination-aware modulation and waveform recovery pipeline that mirrors how musicians often recreate sounds on a synthesizer. First, the system identifies the modulated destinations; then it recovers the LFO shape associated with each destination, and estimates the oscillator waveform to match the source timbre. The training is supported by a differentiable synthesizer that incorporates a noise oscillator, and more modulation options than previous work. We carefully train the models for optimal perceptual quality, with extensive exploration of perceptual losses and adversarial training. Through objective and subjective evaluation, we show that predicting the destinations correctly is essential, our model excels in the modulation focused inverse synthesis tasks, and our Gammatone loss and CQT discriminator significantly outperform the traditionally-used MSS loss in improving perceptual quality. We provide audio samples from the subjective test.

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arXiv2026

Tacit-TTS: From Autoregressive Decoding to Masked Prediction for Efficient Transcript-Free Voice Cloning

TTS systems with autoregressive semantic modeling have demonstrated strong zero-shot voice cloning performance and rich expressive variation, but their sequential decoding incurs substantial latency. Non-autoregressive alternatives offer much faster generation, yet often rely on more restrictive reference conditioning, such as requiring transcripts of the reference speech during inference. We present Tacit-TTS, an efficient transcript-free zero-shot voice cloning system distilled from IndexTTS2. Our model replaces autoregressive text-to-semantic decoding with masked non-autoregressive generation, introduces training-free acoustic length estimation, and accelerates the flow-matching renderer through ReFlow distillation. Across two English and two Mandarin datasets, Tacit-TTS achieves competitive zero-shot quality while generating speech over 10x faster than IndexTTS2 for utterances longer than 5 seconds. Its transcript-free conditioning further supports cross-lingual and non-lexical references. We validate this capability using references from eight other languages, infant babble, and synthetic gibberish, where transcript-dependent systems often degrade or fail due to unreliable ASR transcripts.

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arXiv2022

High Quality Audio Coding with MDCTNet

We propose a neural audio generative model, MDCTNet, operating in the perceptually weighted domain of an adaptive modified discrete cosine transform (MDCT). The architecture of the model captures correlations in both time and frequency directions with recurrent layers (RNNs). An audio coding system is obtained by training MDCTNet on a diverse set of fullband monophonic audio signals at 48 kHz sampling, conditioned by a perceptual audio encoder. In a subjective listening test with ten excerpts chosen to be balanced across content types, yet stressful for both codecs, the mean performance of the proposed system for 24 kb/s variable bitrate (VBR) is similar to that of Opus at twice the bitrate.

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