mlsanityData to 14 Oct 2026

Singing Voice Synthesis Based on a Musical Note Position-Aware Attention Mechanism

arXiv 2212.13703 ↗First posted 28 Dec 2022eess.AS cs.LG cs.SDcited by 0

Not on the map. Why?

Abstract (arXiv)

This paper proposes a novel sequence-to-sequence (seq2seq) model with a musical note position-aware attention mechanism for singing voice synthesis (SVS). A seq2seq modeling approach that can simultaneously perform acoustic and temporal modeling is attractive. However, due to the difficulty of the temporal modeling of singing voices, many recent SVS systems with an encoder-decoder-based model still rely on explicitly on duration information generated by additional modules. Although some studies perform simultaneous modeling using seq2seq models with an attention mechanism, they have insufficient robustness against temporal modeling. The proposed attention mechanism is designed to estimate the attention weights by considering the rhythm given by the musical score. Furthermore, several techniques are also introduced to improve the modeling performance of the singing voice. Experimental results indicated that the proposed model is effective in terms of both naturalness and robustness of timing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 28 Dec 2022.

Builds on

24 of 33 references.

References · 24

  1. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  2. 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 804
  3. 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 717
  4. 2017Tacotron: Towards End-to-End Speech Synthesiscited by 490
  5. 2015Attention-Based Models for Speech Recognitioncited by 401
  6. 2020Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modelingcited by 65
  7. 2017Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attentioncited by 57
  8. 2020Parallel Tacotron: Non-Autoregressive and Controllable TTScited by 49
  9. 2020HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesiscited by 39
  10. 2019Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTScited by 33
  11. 2020XiaoiceSing: A High-Quality and Integrated Singing Voice Synthesis Systemcited by 33
  12. 2019Adversarially Trained End-to-end Korean Singing Voice Synthesis Systemcited by 22
12 more
  1. 2020ByteSing: A Chinese Singing Voice Synthesis System Using Duration Allocated Encoder-Decoder Acoustic Models and WaveRNN Vocoderscited by 21
  2. 2018Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesiscited by 17
  3. 2019Sequence-to-sequence Singing Synthesis Using the Feed-forward Transformercited by 17
  4. 2017A Neural Parametric Singing Synthesizercited by 14
  5. 2021Sinsy: A Deep Neural Network-Based Singing Voice Synthesis Systemcited by 14
  6. 2020Adversarially Trained Multi-Singer Sequence-To-Sequence Singing Synthesizercited by 12
  7. 2019Singing Synthesis: with a little help from my attentioncited by 8
  8. 2019Singing Voice Synthesis Using Deep Autoregressive Neural Networks for Acoustic Modelingcited by 7
  9. 2019Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networkscited by 7
  10. 2020Sequence-to-sequence Singing Voice Synthesis with Perceptual Entropy Losscited by 6
  11. 2022Singing-Tacotron: Global duration control attention and dynamic filter for End-to-end singing voice synthesiscited by 4
  12. 2021Neural Sequence-to-Sequence Speech Synthesis Using a Hidden Semi-Markov Model Based Structured Attention Mechanismcited by 1

Built on by

Not cited yet.