A Statistically Principled and Computationally Efficient Approach to Speech Enhancement using Variational Autoencoders
This work addresses computational bottlenecks in speech enhancement for audio processing applications, representing an incremental improvement over prior methods.
The paper tackles the computational inefficiency of existing variational autoencoder (VAE)-based speech enhancement methods by proposing a variational inference approach that analytically derives steps to estimate clean speech spectrograms. It achieves results comparable to iterative sampling methods while reducing computational cost by a factor of 36.
Recent studies have explored the use of deep generative models of speech spectra based of variational autoencoders (VAEs), combined with unsupervised noise models, to perform speech enhancement. These studies developed iterative algorithms involving either Gibbs sampling or gradient descent at each step, making them computationally expensive. This paper proposes a variational inference method to iteratively estimate the power spectrogram of the clean speech. Our main contribution is the analytical derivation of the variational steps in which the en-coder of the pre-learned VAE can be used to estimate the varia-tional approximation of the true posterior distribution, using the very same assumption made to train VAEs. Experiments show that the proposed method produces results on par with the afore-mentioned iterative methods using sampling, while decreasing the computational cost by a factor 36 to reach a given performance .