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As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event detection systems using synthetic mixtures of office sounds. This task, which follows the ‘Event Detection-Office Synthetic’ task of DCASE 2013, studies the behaviour of tested algorithms when facing controlled levels of audio...
This paper presents and compares two algorithms based on artificial neural networks (ANNs) for sound event detection in real life audio. Both systems have been developed and evaluated with the material provided for the third task of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge. For the first algorithm, we make use of an ANN trained on different features extracted...
This paper deals with the acoustic event detection (AED) to improve the detection accuracy of acoustic events. Acoustic event detection task is performed by a regression via classification (RvC) based approach along with the random forest technique. A discretization process is used to convert the continuous frame positions within acoustic events into event duration class labels. Outputs of the category-specific...
This paper deals with random forest regression based acoustic event detection (AED) by combining acoustic features with bottleneck features (BN). The bottleneck features have a good reputation of being inherently discriminative in acoustic signal processing. To deal with the unstructured and complex real-world acoustic events, an acoustic event detection system is constructed using bottleneck features...
In many event detection applications, training data may contain tags with multiple, simultaneous events. This is particularly likely when the definition of “event” is broad and includes events that can persist for an extended period of time. Decomposing a mixed signal into signals corresponding to individual events is non-trivial. In this paper, we propose a non-negative matrix factorization (NMF)...
Sound event detection is the task of detecting the type, starting time, and ending time of sound events in audio streams. Recently, recurrent neural networks (RNNs) have become the mainstream solution for sound event detection. Because RNNs make a prediction at every frame, it is necessary to provide exact starting and ending times of the sound events in the training data, making data annotation an...
Methods for detection of overlapping sound events in audio involve matrix factorization approaches, often assigning separated components to event classes. We present a method that bypasses the supervised construction of class models. The method learns the components as a non-negative dictionary in a coupled matrix factorization problem, where the spectral representation and the class activity annotation...
This paper addresses the problem of detection and recognition of impulsive sounds in surveillance system, such as door slams, footsteps, glass breaks, gunshots and human screams. We build an acoustic event dataset of about 1k sound clips and a ground truth dataset of a surveillance system. We investigate the influence of different frame size in audio feature extraction when classify acoustic events...
In most real-world audio recordings, we encounter several types of audio events. In this paper, we develop a technique for detecting signature audio events, that is based on identifying patterns of occurrences of automatically learned atomic units of sound, which we call Acoustic Unit Descriptors or AUDs. Experiments show that the methodology works as well for detection of individual events and their...
This paper presents new approaches to improve the detection of two key audio events in a sport game (tennis) using contextual information. When analysing a tennis match using only audio information, the sound of the ball being struck and the occurrence of a line judge's shout can be obscured by players' grunts or shouts. Furthermore, if models of these two important events are trained from labelled...
In regions of the world where tuberculosis (TB) poses the greatest disease burden, the lack of access to skilled laboratories is a significant problem. A lab-free method for assessing patient recovery during treatment would be of great benefit, particularly for identifying patients who may have drug-resistant tuberculosis. We hypothesize that cough analysis may provide such a test. In this paper we...
We developed a system that detects abnormal sound from sound signal observed by a surveillance microphone. Our system learns the ldquonormal soundrdquo from observation of the microphone, and then detects sounds never observed before as ldquoabnormal sounds.rdquo To this end, we developed a technique that uses multiple GMMs for modeling different levels of sound events efficiently. We also consider...
Smart homes for the aging population have recently started attracting the attention of the research community. One of the problems of interest is this of monitoring the activities of daily living (ADLs) of the elderly, in order to help identify critical problems, aiming to improve their protection and general well-being. In this paper, we report on our initial attempts to recognize such activities,...
In this paper we propose a speech event detector that segments speech signals in terms of four broad acoustic-phonetic classes of events. Frame-based detection was carried out using support vector machines (SVM). Non-negative matrix deconvolution (NMD) was used in order to switch from a frame-based detection to a segment-based detection. Results obtained using the TIMIT corpus are reported and compared...
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