Sound events often occur in unstructured environments where they exhibit wide variations in
their frequency content and temporal structure. Convolutional neural networks (CNNs) are …

In this paper we present an approach to polyphonic sound event detection in real life
recordings based on bi-directional long short term memory (BLSTM) recurrent neural …

This paper presents and discusses various metrics proposed for evaluation of polyphonic
sound event detection systems used in realistic situations where there are typically multiple …

In this paper, the use of multi label neural networks are proposed for detection of temporally
overlapping sound events in realistic environments. Real-life sound recordings typically …

Sound event detection (SED) methods are tasked with labeling segments of audio
recordings by the presence of active sound sources. SED is typically posed as a supervised …

In this paper, we propose the use of spatial and harmonic features in combination with long
short term memory (LSTM) recurrent neural network (RNN) for automatic sound event …

Methods for detection of overlapping sound events in audio involve matrix factorization
approaches, often assigning separated components to event classes. We present a method …

This paper proposes an acoustic event detection (AED) method using semi-supervised non-
negative matrix factorization (NMF) with a mixture of local dictionaries (MLD). The proposed …

This paper proposes sound event localization and detection methods from multichannel
recording. The proposed system is based on two Convolutional Recurrent Neural Networks …

This paper proposes a new non-negative matrix factorization (NMF) based acoustic event
detection (AED) method with mixtures of local dictionaries (MLD) and activation aggregation …