Yuxuan Wu

Ruibin Yuan, Yuxuan Wu, Jacob Li et al.

The widespread adoption of speech-based online services raises security and privacy concerns regarding the data that they use and share. If the data were compromised, attackers could exploit user speech to bypass speaker verification systems or even impersonate users. To mitigate this, we propose DeID-VC, a speaker de-identification system that converts a real speaker to pseudo speakers, thus removing or obfuscating the speaker-dependent attributes from a spoken voice. The key components of DeID-VC include a Variational Autoencoder (VAE) based Pseudo Speaker Generator (PSG) and a voice conversion Autoencoder (AE) under zero-shot settings. With the help of PSG, DeID-VC can assign unique pseudo speakers at speaker level or even at utterance level. Also, two novel learning objectives are added to bridge the gap between training and inference of zero-shot voice conversion. We present our experimental results with word error rate (WER) and equal error rate (EER), along with three subjective metrics to evaluate the generated output of DeID-VC. The result shows that our method substantially improved intelligibility (WER 10% lower) and de-identification effectiveness (EER 5% higher) compared to our baseline. Code and listening demo: https://github.com/a43992899/DeID-VC

Yuxuan Wu, Roger B. Dannenberg, Gus Xia

Music motif, as a conceptual building block of composition, is crucial for music structure analysis and automatic composition. While human listeners can identify motifs easily, existing computational models fall short in representing motifs and their developments. The reason is that the nature of motifs is implicit, and the diversity of motif variations extends beyond simple repetitions and modulations. In this study, we aim to learn the implicit relationship between motifs and their variations via representation learning, using the Siamese network architecture and a pretraining and fine-tuning pipeline. A regularization-based method, VICReg, is adopted for pretraining, while contrastive learning is used for fine-tuning. Experimental results on a retrieval-based task show that these two methods complement each other, yielding an improvement of 12.6% in the area under the precision-recall curve. Lastly, we visualize the acquired motif representations, offering an intuitive comprehension of the overall structure of a music piece. As far as we know, this work marks a noteworthy step forward in computational modeling of music motifs. We believe that this work lays the foundations for future applications of motifs in automatic music composition and music information retrieval.

Yuxuan Wu, Ziyu Wang, Bhiksha Raj et al.

We contribute an unsupervised method that effectively learns disentangled content and style representations from sequences of observations. Unlike most disentanglement algorithms that rely on domain-specific labels or knowledge, our method is based on the insight of domain-general statistical differences between content and style -- content varies more among different fragments within a sample but maintains an invariant vocabulary across data samples, whereas style remains relatively invariant within a sample but exhibits more significant variation across different samples. We integrate such inductive bias into an encoder-decoder architecture and name our method after V3 (variance-versus-invariance). Experimental results show that V3 generalizes across multiple domains and modalities, successfully learning disentangled content and style representations, such as pitch and timbre from music audio, digit and color from images of hand-written digits, and action and character appearance from simple animations. V3 demonstrates strong disentanglement performance compared to existing unsupervised methods, along with superior out-of-distribution generalization under few-shot adaptation compared to supervised counterparts. Lastly, symbolic-level interpretability emerges in the learned content codebook, forging a near one-to-one alignment between machine representation and human knowledge.