Controlling the convergence rate to help parameter estimation in a PLCA-based model

Probabilistic Latent Component Analysis (PLCA) is a tool similar to Non-negative Matrix Factorization (NMF), which is used to model non-negative data such as non-negative time-frequency representations of audio. In this paper, we put forward a trick to help the corresponding parameter estimation algorithm to converge toward more meaningful solutions, based on the new concept of brakes. The idea is to control the convergence rate of the parameters of a PLCA-based model within the estimation algorithm: the parameters which are known to be properly initialized are braked in order to stay close to their initial values, whereas the other ones keep a regular convergence rate. This is an effective way to better account for a relevant initialization. In this paper, these brakes are implemented in the framework of PLCA, and they are tested in an application of multipitch estimation. Results show that the use of brakes can significantly influence the decomposition and thus the performance, making them a powerful tool to boost any kind of PLCA-based algorithm.