Grégoire Lafay

A Morphological Model for Simulating Acoustic Scenes and Its Application to Sound Event Detection

This paper introduces a model for simulating environmental acoustic scenes that abstracts temporal structures from audio recordings. This model allows us to explicitly control key morphological aspects of the acoustic scene and to isolate their impact on the performance of the system under evaluation. Thus, more information can be gained on the behavior of an evaluated system, providing guidance for further improvements. To demonstrate its potential, this model is employed to evaluate the performance of nine state of the art sound event detection systems submitted to the IEEE DCASE 2013 Challenge. Results indicate that the proposed scheme is able to successfully build datasets useful for evaluating important aspects of the performance of sound event detection systems, such as their robustness to new recording conditions and to varying levels of background audio.

Polyphonic Sound Event Tracking Using Linear Dynamical Systems

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-the-art methods for the task of polyphonic sound event detection.

Alternate level clustering for drum transcription

This paper introduces a clustering-based unsupervised approach to the problem of drum transcription. The proposed method is based on a stack of multiple clustering and segmentation stages that progressively build up meaningful audio events, in a bottom-up fashion. At each level, the inherent redundancy of the repeating events guides the clustering of objects into more complex structures. Comparison with state-of-the-art approaches demonstrate the potential of the proposed approach, both in terms of efficiency and of ability to generalize.