6 research outputs found
ODAS: Open embeddeD Audition System
Artificial audition aims at providing hearing capabilities to machines,
computers and robots. Existing frameworks in robot audition offer interesting
sound source localization, tracking and separation performance, but involve a
significant amount of computations that limit their use on robots with embedded
computing capabilities. This paper presents ODAS, the Open embeddeD Audition
System framework, which includes strategies to reduce the computational load
and perform robot audition tasks on low-cost embedded computing systems. It
presents key features of ODAS, along with cases illustrating its uses in
different robots and artificial audition applications
Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019
Sound event localization and detection is a novel area of research that
emerged from the combined interest of analyzing the acoustic scene in terms of
the spatial and temporal activity of sounds of interest. This paper presents an
overview of the first international evaluation on sound event localization and
detection, organized as a task of the DCASE 2019 Challenge. A large-scale
realistic dataset of spatialized sound events was generated for the challenge,
to be used for training of learning-based approaches, and for evaluation of the
submissions in an unlabeled subset. The overview presents in detail how the
systems were evaluated and ranked and the characteristics of the
best-performing systems. Common strategies in terms of input features, model
architectures, training approaches, exploitation of prior knowledge, and data
augmentation are discussed. Since ranking in the challenge was based on
individually evaluating localization and event classification performance, part
of the overview focuses on presenting metrics for the joint measurement of the
two, together with a reevaluation of submissions using these new metrics. The
new analysis reveals submissions that performed better on the joint task of
detecting the correct type of event close to its original location than some of
the submissions that were ranked higher in the challenge. Consequently, ranking
of submissions which performed strongly when evaluated separately on detection
or localization, but not jointly on both, was affected negatively
Sound Event Localization and Detection Using CRNN on Pairs of Microphones
This paper proposes sound event localization and detection methods from multichannel recording. The proposed system is based on two Convolutional Recurrent Neural Networks (CRNNs) to perform sound event detection (SED) and time difference of arrival (TDOA) estimation on each pair of microphones in a microphone array. In this paper, the system is evaluated with a four-microphone array, and thus combines the results from six pairs of microphones to provide a final classification and a 3-D direction of arrival (DOA) estimate. Results demonstrate that the proposed approach outperforms the DCASE 2019 baseline system.848
Sound Event Localization and Detection Using CRNN on Pairs of Microphones
This paper proposes sound event localization and detection methods from multichannel recording. The proposed system is based on two Convolutional Recurrent Neural Networks (CRNNs) to perform sound event detection (SED) and time difference of arrival (TDOA) estimation on each pair of microphones in a microphone array. In this paper, the system is evaluated with a four-microphone array, and thus combines the results from six pairs of microphones to provide a final classification and a 3-D direction of arrival (DOA) estimate. Results demonstrate that the proposed approach outperforms the DCASE 2019 baseline system