Polyphonic Sound Event Tracking Using Linear Dynamical Systems

doi:10.1109/TASLP.2017.2690576

Polyphonic Sound Event Tracking Using Linear Dynamical Systems

2017-06-01

6

OA

AI

DOI：10.1109/TASLP.2017.2690576

Original

Save

View PDF

Abstract

En 中文

In this paper, a system for polyphonic sound event detection and tracking is proposed, based on spectrogram factorization techniques and state space models. The system extends probabilistic latent component analysis (PLCA) and is modeled around a four-dimensional spectral template dictionary of frequency, sound event class, exemplar index, and sound state. In order to jointly track multiple overlapping sound events over time, the integration of linear dynamical systems (LDS) within the PLCA inference is proposed. The system assumes that the PLCA sound event activation is the (noisy) observation in an LDS, with the latent states corresponding to the true event activations. LDS training is achieved using fully observed data, making use of ground truth-informed event activations produced by the PLCA-based model. Several LDS variants are evaluated, using polyphonic datasets of office sounds generated from an acoustic scene simulator, as well as real and synthesized monophonic datasets for comparative purposes. Results show that the integration of LDS tracking within PLCA leads to an improvement of +8.5-10.5% in terms of frame-based F-measure as compared to the use of the PLCA model alone. In addition, the proposed system outperforms several state-of-theart methods for the task of polyphonic sound event detection.

Keywords:

Linear dynamical systems

probabilistic latent component analysis

Sound event detection

sound scene analysis

Polyphonic Sound Event Tracking Using Linear Dynamical Systems

Abstract

AI Summary

Journal

Organization