Hello there, I’m a first-year PhD student in @aim-qmul at @c4dm, Queen Mary University of London, working on controllable and expressive neural voice synthesis 🎶

I'm interested in signal processing, music information retrieval, binaural audio, machine learning, or any audio-related tech.

PhD Projects

• diffwave-sr: unsupervised speech super-resolution (bandwidth extension) using posterior sampling in diffusion models.
• golf: a light-weight neural vocoder with glottal-flow models and differentiable LPC synthesis.

Tools

• torch_specinv: a collections of spectrogram inversion algorithms.
• torchnmf: a package that can help build complex NMF models.
• kazane: simple sinc interpolation for 1D signal in PyTorch.
• torch-fftconv: FFT-based PyTorch convolution operators.

Re-implementations

• wavenet-like-vocoder: WaveNet and FFTNet re-implementations.
• constant-memory-waveglow: training waveglow with constant memory cost.
• variational-diffwave: training DiffWave with unbiased ELBO.

I’m also the main contributor to making torchaudio lfilter differentiable. Your sponsorship will support my development and maintenance of the above tools, my engagement in open-source science, and my PhD research.