We present a deep learning method for singing voice conversion. The proposed network is not conditioned on the text or on the notes, and it directly converts the audio of one singer to the voice of another. Training is performed without any form of supervision: no lyrics or any kind of phonetic features, no notes, and no matching samples between singers. The proposed network employs a single CNN encoder for all singers, a single WaveNet decoder, and a classifier that enforces the latent representation to be singer-agnostic. Each singer is represented by one embedding vector, which the decoder is conditioned on. In order to deal with relatively small datasets, we propose a new data augmentation scheme, as well as new training losses and protocols that are based on backtranslation. Our evaluation presents evidence that the conversion produces natural signing voices that are highly recognizable as the target singer.
In the field of singing voice conversion, i.e., transforming an audio of a song to a target voice, almost all literature methods have used parallel data @cite_0 @cite_27 @cite_31 , i.e., different singers that are required to perform the same song. None of these existing methods provide code or benchmarks that can be used for a direct comparison of their results (even if supervised) to ours.
@cite_0 @cite_27 @cite_31
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Singing voice conversion is a task to convert a song sang by a source singer to the voice of a target singer. In this paper, we propose using a parallel data free, many-to-one voice conversion technique on singing voices. A phonetic posterior feature is first generated by decoding singing voices through a robust Automatic Speech Recognition Engine (ASR). Then, a trained Recurrent Neural Network (RNN) with a Deep Bidirectional Long Short Term Memory (DBLSTM) structure is used to model the mapping from person-independent content to the acoustic features of the target person. F0 and aperiodic are obtained through the original singing voice, and used with acoustic features to reconstruct the target singing voice through a vocoder. In the obtained singing voice, the targeted and sourced singers sound similar. To our knowledge, this is the first study that uses non parallel data to train a singing voice conversion system. Subjective evaluations demonstrate that the proposed method effectively converts singing voices. Most existing voice conversion systems, particularly those based on Gaussian mixture models, require a set of paired acoustic vectors from the source and target speakers to learn their corresponding transformation function. The alignment of phonetically equivalent source and target vectors is not problematic when the training corpus is parallel, which means that both speakers utter the same training sentences. However, in some practical situations, such as cross-lingual voice conversion, it is not possible to obtain such parallel utterances. With an aim towards increasing the versatility of current voice conversion systems, this paper proposes a new iterative alignment method that allows pairing phonetically equivalent acoustic vectors from nonparallel utterances from different speakers, even under cross-lingual conditions. This method is based on existing voice conversion techniques, and it does not require any phonetic or linguistic information. Subjective evaluation experiments show that the performance of the resulting voice conversion system is very similar to that of an equivalent system trained on a parallel corpus. To convert one speaker's voice to another's, the mapping of the corresponding speech segments from source speaker to target speaker must be obtained first. In parallel voice conversion, normally dynamic time warping (DTW) method is used to align signals of source and target voices. However, for conversion between non-parallel speech data, the DTW based mapping method does not work. In this paper, we propose to use a DNN-HMM recognizer to recognize each frame for both source and target speech signals. The vector of pseudo likelihood is then used to represent the frame. Similarity between two frames is measured with the distance between the vectors. A clustering method is used to group both source and target frames. Frame mapping from source to target is then established based on the clustering result. The experiments show that the proposed method can generate similar conversion results compared to parallel voice conversion. This paper presents techniques to improve the quality of voices generated through statistical singing voice conversion with direct waveform modification based on spectrum differential (DIFFSVC). The DIFFSVC method makes it possible to convert singing voice characteristics of a source singer into those of a target singer without using vocoder-based waveform generation. However, quality of the converted singing voice still degrades compared to that of a natural singing voice due to various factors, such as the over-smoothing of the converted spectral parameter trajectory. To alleviate this over-smoothing, we propose a technique to restore the global variance of the converted spectral parameter trajectory within the framework of the DIFFSVC method. We also propose another technique to specifically avoid over-smoothing at unvoiced frames. Results of subjective and objective evaluations demonstrate that the proposed techniques significantly improve speech quality of the converted singing voice while preserving the conversion accuracy of singer identity compared to the conventional DIFFSVC.