End-to-End Probabilistic Inference for Nonstationary Audio Analysis
This work addresses inefficiencies in audio analysis pipelines for researchers and practitioners, though it is incremental as it builds on existing Gaussian process and matrix factorization methods.
The authors tackled the problem of disjoint stages in audio signal processing by jointly formulating time-frequency analysis and nonnegative matrix factorization as a spectral mixture Gaussian process model with nonstationary priors, enabling processing of audio signals with hundreds of thousands of data points and outperforming standard techniques like extended Kalman filtering.
A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based feature analysis. We show how time-frequency analysis and nonnegative matrix factorisation can be jointly formulated as a spectral mixture Gaussian process model with nonstationary priors over the amplitude variance parameters. Further, we formulate this nonlinear model's state space representation, making it amenable to infinite-horizon Gaussian process regression with approximate inference via expectation propagation, which scales linearly in the number of time steps and quadratically in the state dimensionality. By doing so, we are able to process audio signals with hundreds of thousands of data points. We demonstrate, on various tasks with empirical data, how this inference scheme outperforms more standard techniques that rely on extended Kalman filtering.