A hybrid CNN-LSTM model with adaptive instance normalization for one shot singing voice conversion

Abstract

<abstract><p>Singing voice conversion methods encounter challenges in achieving a delicate balance between synthesis quality and singer similarity. Traditional voice conversion techniques primarily emphasize singer similarity, often leading to robotic-sounding singing voices. Deep learning-based singing voice conversion techniques, however, focus on disentangling singer-dependent and singer-independent features. While this approach can enhance the quality of synthesized singing voices, many voice conversion systems still grapple with the issue of singer-dependent feature leakage into content embeddings. In the proposed singing voice conversion technique, an encoder decoder framework was implemented using a hybrid model of convolutional neural network (CNN) accompanied by long short term memory (LSTM). This paper investigated the use of activation guidance and adaptive instance normalization techniques for one shot singing voice conversion. The instance normalization (IN) layers within the auto-encoder effectively separated singer and content representations. During conversion, singer representations were transferred using adaptive instance normalization (AdaIN) layers. This singing voice system with the help of activation function prevented the transfer of singer information while conveying the singing content. Additionally, the fusion of LSTM with CNN can enhance voice conversion models by capturing both local and contextual features. The one-shot capability simplified the architecture, utilizing a single encoder and decoder. Impressively, the proposed hybrid CNN-LSTM model achieved remarkable performance without compromising either quality or similarity. The objective and subjective evaluation assessments showed that the proposed hybrid CNN-LSTM model outperformed the baseline architectures. Evaluation results showed a mean opinion score (MOS) of 2.93 for naturalness and 3.35 for melodic similarity. These hybrid CNN-LSTM techniques allowed it to perform high-quality voice conversion with minimal training data, making it a promising solution for various applications.</p></abstract>