# A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs +
## 6 Conclusions  
We presented a singing synthesizer based on neural networks, which can generate synthetic singing voice given a musical score with lyrics. From a single set of relatively few songs, the system is able to learn both timbre and expression. Separate, but interconnected models learn phonetic timing, pitch and timbre. The core building block of the system is a variant of the WaveNet architecture, modified to allow generating features obtained from a parametric vocoder. This autoregressive approach offers improved reproduction of consonants and a more natural variation of predicted parameters over time, compared to competing approaches such as statistical parametric systems.  
Figure 7: Results of the preference test for systems trained on pseudo singing. The Sinsky-HMM and Sinsky-DNN systems were excluded from this comparison, as the only available models are trained on natural singing.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**System** & **Mean Opinion Score** \\ \hline
**Hidden reference** & 4.76 \(\pm\) 0.04 \\
**IS16** & 2.36 \(\pm\) 0.11 \\
**Sinsy-HMM** & 2.98 \(\pm\) 0.10 \\
**Sinsy-DNN** & 2.77 \(\pm\) 0.10 \\
**NPSS** & **3.43 \(\pm\) 0.11** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean opinion scores for systems trained on natural singing, displayed on a 1–5 scale with their respective 95% confidence intervals. The HTS system is only considered when comparing systems trained on pseudo singing, but should be roughly equivalent to Sinsky-HMM.  
Compared to concatenative approaches, our model allows for greater flexibility and is more robust to small misalignments between phonetic and acoustic features in the training data. In listening test our system was rated to reduce the gap between the second best system and the reference recording by about a third. While correlating this with quantitative metrics is challenging, metrics that take into account variations over time, such as the modulation spectrum, do seem to corroborate this. The relatively small CPU, memory and disk footprint allows for many practical applications of our system. We hope that in the near future we can evaluate our model trained on natural singing for a wider range of languages and datasets. Further exploring the flexibility offered by this neural approach, such as the area of multispeaker training is also promising, as it might help to overcome the issue of limited dataset sizes typical of singing voice.  
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research. We thank Nagoya Institute of Technology for providing the NIT-SONG070-F001 dataset (licensed under CC BY 3.0), Zya for providing the English datasets, and Vectro Labs for providing the Spanish dataset and the implementation of the fast generation algorithm. This work is partially supported by the Spanish Ministry of Economy and Competitiveness under the CASAS project (TIN2015-70816-R).  
[PERSON] and [PERSON] designed and implemented the proposed system; [PERSON] implemented the fast generation algorithm; [PERSON] and [PERSON] designed the experiments; and [PERSON] performed the experiments and wrote the paper.  
The authors declare no conflict of interest.