Reducing F0 Frame Error of F0 tracking algorithms under noisy conditions with an unvoiced/voiced classification frontend

By Wei-Chung Chu, Abeer A. Alwan. 2009

122 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Signal Processing

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Citations in the corpus

WaveGrad: Estimating Gradients for Waveform Generation (2020, background): “F 0 Frame Error (FFE) metric (Chu & Alwan, 2009) which combines Gross Pitch Error (GPE) and Voicing Decision Error (VDE).”
Speech Resynthesis from Discrete Disentangled Self-Supervised Representations (2021, background): “Next, to evaluate the presence of F0 in the discrete units, we flattened the F0 units before synthesizing the signal and calculated VDE and FFE with respect to the original F0 values.”
InstructTTS: Modelling Expressive TTS in Discrete Latent Space With Natural Language Style Prompt (2023, methodology): “For prosody similarity, we use three pitch-related metrics: Gross Pitch Error (GPE), Voicing Decision Error (VDE) [69] and F0 Frame Error (FFE) [70].”
Generating Diverse and Natural Text-to-Speech Samples Using a Quantized Fine-Grained VAE and Autoregressive Prosody Prior (2020, methodology): “The QFVAE models have higher FFE and MCD than the baseline, however their performances improve as the number of classes increased.”
Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models (2019, unknown): “F0 Frame Error (FFE) [25]:”
Mellotron: Multispeaker Expressive Voice Synthesis by Conditioning on Rhythm, Pitch and Global Style Tokens (2019, unknown): “In this section we provide quantitative results that compare Gross Pitch Error (GPE) [20], Voicing Decision Error (VDE) [20] and F0 Frame Error (FFE) [21] between Mellotron and E2E-Prosody [1].”
Hierarchical Prosody Modeling for Non-Autoregressive Speech Synthesis (2020, background): “are several metrics used in early works to evaluate pairwise prosody similarity [1, 27]:”
An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning (2020, background): “We note that GPE reports the percentage of voiced frames whose pitch values are more than 20% different from the reference, while FFE reports the percentage of frames that either contain a 20% pitch error or a voicing decision error [263].”
Review of end-to-end speech synthesis technology based on deep learning (2021, background): “Chu W, Alwan A (2009) Reducing f0 frame er- ror of f0 tracking algorithms under noisy conditions with an unvoiced/voiced classification frontend.”
Fully-Hierarchical Fine-Grained Prosody Modeling For Interpretable Speech Synthesis (2020, methodology): “Finally, the mel-cepstral distortion (MCD), the F0 Frame Error (FFE) [38], which is a combination of the Gross Pitch Error (GPE) and the Voicing Decision Error (VDE), are used to quantify the reconstruction performance.”
Normalization Driven Zero-Shot Multi-Speaker Speech Synthesis (2021, methodology): “…architecture are given in 1 Apart from subjective evaluation, we have used the metrics namely Gross Pitch Error(GPE) [31], Voicing Decision Error(VDE) [31], F0 Frame Error(FFE) [32], Mel Cepstral Distortion(MCD) [33] which are used in audio signal processing to measure the prosody of the signal.”
Improve GAN-based Neural Vocoder using Pointwise Relativistic LeastSquare GAN (2021, methodology): “Starting from the baseline model, MelGAN and PWGAN, which apply LSGAN and STFT loss, we added each of the proposed methods one at a time measuring MCD, FFE, and PESQ.”
Unsupervised Speech Decomposition via Triple Information Bottleneck (2020, methodology): “SPEECHFLOW achieves a GPE of 1.04%, a VDE of 8.14%, and an FFE of 8.86%.”
fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit (2021, background): “FFE(p, p̂,v, v̂) = VDE(v, v̂) + ∑T−1 t=1 1[|pt − p̂t| > 0.2 · pt]1[vt]1[v̂t] T .”
Unsupervised Cross-Domain Singing Voice Conversion (2020, background): “(iv) Voicing Decision Error (VDE) [53], which measures the portion of frames with voicing decision error, (v) F0 Frame Error (FFE) [54], measures the percentage of frames that contain a deviation of more than 20% in pitch value or have a voicing decision error.”