In this paper, we propose a convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in three-dimensional (3D) space. The proposed network takes a sequence of consecutive spectrogram time-frames as input and maps it to two outputs in parallel. As the first output, the sound event detection (SED) is performed as a multi-label classification task on each time-frame producing temporal activity for all the sound event classes. As the second output, localization is performed by estimating the 3D Cartesian coordinates of the direction-of-arrival (DOA) for each sound event class using multi-output regression. The proposed method is able to associate multiple DOAs with respective sound event labels and further track this association with respect to time. The proposed method uses separately the phase and magnitude component of the spectrogram calculated on each audio channel as the feature, thereby avoiding any method- and array-specific feature extraction. The method is evaluated on five Ambisonic and two circular array format datasets with different overlapping sound events in anechoic, reverberant and real-life scenarios. The proposed method is compared with two SED, three DOA estimation, and one SELD baselines. The results show that the proposed method is generic and applicable to any array structures, robust to unseen DOA values, reverberation, and low SNR scenarios. The proposed method achieved a consistently higher recall of the estimated number of DOAs across datasets in comparison to the best baseline. Additionally, this recall was observed to be significantly better than the best baseline method for a higher number of overlapping sound events.

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.

The domain of spatial audio comprises methods for capturing, processing, and reproducing audio content that contains spatial information. Data-based methods are those that operate directly on the spatial information carried by audio signals. This is in contrast to model-based methods, which impose spatial information from, for example, metadata like the intended position of a source onto signals that are otherwise free of spatial information. Signal processing has traditionally been at the core of spatial audio systems, and it continues to play a very important role. The irruption of deep learning in many closely related fields has put the focus on the potential of learning-based approaches for the development of data-based spatial audio applications. This article reviews the most important application domains of data-based spatial audio including well-established methods that employ conventional signal processing while paying special attention to the most recent achievements that make use of machine learning. Our review is organized based on the topology of the spatial audio pipeline that consist in capture, processing/manipulation, and reproduction. The literature on the three stages of the pipeline is discussed, as well as on the spatial audio representations that are used to transmit the content between them, highlighting the key references and elaborating on the underlying concepts. We reflect on the literature based on a juxtaposition of the prerequisites that made machine learning successful in domains other than spatial audio with those that are found in the domain of spatial audio as of today. Based on this, we identify routes that may facilitate future advancement.

This papers presents a parametric method for perceptual sound field recording and reproduction from a small-sized microphone array to arbitrary loudspeaker layouts. The applied parametric model has been found to be effective and well-correlated with perceptual attributes in the context of directional audio coding, and here it is generalized and extended to higher orders of spherical harmonic signals. Higher order recordings are used for estimation of the model parameters inside angular sectors that provide increased separation between simultaneous sources and reverberation. The perceptual synthesis according to the combined properties of these sector parameters is achieved with an adaptive least-squares mixing technique. Furthermore, considerations regarding practical microphone arrays are presented and a frequency-dependent scheme is proposed. A realization of the system is described for an existing spherical microphone array and for a target loudspeaker setup similar to NHK 22.2. It is demonstrated through listening tests that, compared to a reference scene, the perceived difference is greatly reduced with the proposed higher order analysis model. The results further indicate that, on the same task, the method outperforms linear reproduction with the same recordings available.