19 research outputs found
CrossSinger: A Cross-Lingual Multi-Singer High-Fidelity Singing Voice Synthesizer Trained on Monolingual Singers
It is challenging to build a multi-singer high-fidelity singing voice
synthesis system with cross-lingual ability by only using monolingual singers
in the training stage. In this paper, we propose CrossSinger, which is a
cross-lingual singing voice synthesizer based on Xiaoicesing2. Specifically, we
utilize International Phonetic Alphabet to unify the representation for all
languages of the training data. Moreover, we leverage conditional layer
normalization to incorporate the language information into the model for better
pronunciation when singers meet unseen languages. Additionally, gradient
reversal layer (GRL) is utilized to remove singer biases included in lyrics
since all singers are monolingual, which indicates singer's identity is
implicitly associated with the text. The experiment is conducted on a
combination of three singing voice datasets containing Japanese Kiritan
dataset, English NUS-48E dataset, and one internal Chinese dataset. The result
shows CrossSinger can synthesize high-fidelity songs for various singers with
cross-lingual ability, including code-switch cases.Comment: Accepted by ASRU202
WeSinger 2: Fully Parallel Singing Voice Synthesis via Multi-Singer Conditional Adversarial Training
This paper aims to introduce a robust singing voice synthesis (SVS) system to
produce very natural and realistic singing voices efficiently by leveraging the
adversarial training strategy. On one hand, we designed simple but generic
random area conditional discriminators to help supervise the acoustic model,
which can effectively avoid the over-smoothed spectrogram prediction and
improve the expressiveness of SVS. On the other hand, we subtly combined the
spectrogram with the frame-level linearly-interpolated F0 sequence as the input
for the neural vocoder, which is then optimized with the help of multiple
adversarial conditional discriminators in the waveform domain and multi-scale
distance functions in the frequency domain. The experimental results and
ablation studies concluded that, compared with our previous auto-regressive
work, our new system can produce high-quality singing voices efficiently by
fine-tuning different singing datasets covering from several minutes to a few
hours. A large number of synthesized songs with different timbres are available
online https://zzw922cn.github.io/wesinger2 and we highly recommend readers to
listen to them.Comment: accepted at ICASSP 202
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1.1 Motivation 1
1.2 Problems in singing voice synthesis 4
1.3 Task of interest 8
1.3.1 Single-singer SVS 9
1.3.2 Multi-singer SVS 10
1.3.3 Expressive SVS 11
1.4 Contribution 11
2 Background 13
2.1 Singing voice 14
2.2 Source-filter theory 18
2.3 Autoregressive model 21
2.4 Related works 22
2.4.1 Speech synthesis 25
2.4.2 Singing voice synthesis 29
3 Adversarially Trained End-to-end Korean Singing Voice Synthesis System 31
3.1 Introduction 31
3.2 Related work 33
3.3 Proposed method 35
3.3.1 Input representation 35
3.3.2 Mel-synthesis network 36
3.3.3 Super-resolution network 38
3.4 Experiments 42
3.4.1 Dataset 42
3.4.2 Training 42
3.4.3 Evaluation 43
3.4.4 Analysis on generated spectrogram 46
3.5 Discussion 49
3.5.1 Limitations of input representation 49
3.5.2 Advantages of using super-resolution network 53
3.6 Conclusion 55
4 Disentangling Timbre and Singing Style with multi-singer Singing Synthesis System 57
4.1Introduction 57
4.2 Related works 59
4.2.1 Multi-singer SVS system 60
4.3 Proposed Method 60
4.3.1 Singer identity encoder 62
4.3.2 Disentangling timbre & singing style 64
4.4 Experiment 64
4.4.1 Dataset and preprocessing 64
4.4.2 Training & inference 65
4.4.3 Analysis on generated spectrogram 65
4.4.4 Listening test 66
4.4.5 Timbre & style classification test 68
4.5 Discussion 70
4.5.1 Query audio selection strategy for singer identity encoder 70
4.5.2 Few-shot adaptation 72
4.6 Conclusion 74
5 Expressive Singing Synthesis Using Local Style Token and Dual-path Pitch Encoder 77
5.1 Introduction 77
5.2 Related work 79
5.3 Proposed method 80
5.3.1 Local style token module 80
5.3.2 Dual-path pitch encoder 85
5.3.3 Bandwidth extension vocoder 85
5.4 Experiment 86
5.4.1 Dataset 86
5.4.2 Training 86
5.4.3 Qualitative evaluation 87
5.4.4 Dual-path reconstruction analysis 89
5.4.5 Qualitative analysis 90
5.5 Discussion 93
5.5.1 Difference between midi pitch and f0 93
5.5.2 Considerations for use in the actual music production process 94
5.6 Conclusion 95
6 Conclusion 97
6.1 Thesis summary 97
6.2 Limitations and future work 99
6.2.1 Improvements to a faster and robust system 99
6.2.2 Explainable and intuitive controllability 101
6.2.3 Extensions to common speech synthesis tools 103
6.2.4 Towards a collaborative and creative tool 104λ°
Karaoker: Alignment-free singing voice synthesis with speech training data
Existing singing voice synthesis models (SVS) are usually trained on singing
data and depend on either error-prone time-alignment and duration features or
explicit music score information. In this paper, we propose Karaoker, a
multispeaker Tacotron-based model conditioned on voice characteristic features
that is trained exclusively on spoken data without requiring time-alignments.
Karaoker synthesizes singing voice following a multi-dimensional template
extracted from a source waveform of an unseen speaker/singer. The model is
jointly conditioned with a single deep convolutional encoder on continuous data
including pitch, intensity, harmonicity, formants, cepstral peak prominence and
octaves. We extend the text-to-speech training objective with feature
reconstruction, classification and speaker identification tasks that guide the
model to an accurate result. Except for multi-tasking, we also employ a
Wasserstein GAN training scheme as well as new losses on the acoustic model's
output to further refine the quality of the model.Comment: Submitted to INTERSPEECH 202