Acoustic Event Classification Using Convolutional Neural Networks
نویسندگان
چکیده
Acoustic scene classification (ASC) aims to distinguish between different acoustic environments and is a technology which can be used by smart devices for contextualization and personalization. Standard algorithms exploit hand-crafted features which are unlikely to offer the best potential for reliable classification. This paper reports the first application of convolutional neural networks (CNNs) to ASC, an approach which learns discriminant features automatically from spectral representations of raw acoustic data. A principal influence on performance comes from the specific convolutional filters which can be adjusted to capture different spectrotemporal, recurrent acoustic structure. The proposed CNN approach is shown to outperform a Gaussian mixture model baseline for the DCASE 2016 database even though training data is sparse.
منابع مشابه
Hand Gesture Recognition from RGB-D Data using 2D and 3D Convolutional Neural Networks: a comparative study
Despite considerable enhances in recognizing hand gestures from still images, there are still many challenges in the classification of hand gestures in videos. The latter comes with more challenges, including higher computational complexity and arduous task of representing temporal features. Hand movement dynamics, represented by temporal features, have to be extracted by analyzing the total fr...
متن کاملCombining pattern recognition and deep-learning-based algorithms to automatically detect commercial quadcopters using audio signals (Research Article)
Commercial quadcopters with many private, commercial, and public sector applications are a rapidly advancing technology. Currently, there is no guarantee to facilitate the safe operation of these devices in the community. Three different automatic commercial quadcopters identification methods are presented in this paper. Among these three techniques, two are based on deep neural networks in whi...
متن کاملA New Method to Improve Automated Classification of Heart Sound Signals: Filter Bank Learning in Convolutional Neural Networks
Introduction: Recent studies have acknowledged the potential of convolutional neural networks (CNNs) in distinguishing healthy and morbid samples by using heart sound analyses. Unfortunately the performance of CNNs is highly dependent on the filtering procedure which is applied to signal in their convolutional layer. The present study aimed to address this problem by a...
متن کاملVirtual Adversarial Training and Data Augmentation for Acoustic Event Detection with Gated Recurrent Neural Networks
In this paper, we use gated recurrent neural networks (GRNNs) for efficiently detecting environmental events of the IEEE Detection and Classification of Acoustic Scenes and Events challenge (DCASE2016). For this acoustic event detection task data is limited. Therefore, we propose data augmentation such as on-the-fly shuffling and virtual adversarial training for regularization of the GRNNs. Bot...
متن کاملRare Sound Event Detection Using 1d Convolutional Recurrent Neural Networks
Rare sound event detection is a newly proposed task in IEEE DCASE 2017 to identify the presence of monophonic sound event that is classified as an emergency and to detect the onset time of the event. In this paper, we introduce a rare sound event detection system using combination of 1D convolutional neural network (1D ConvNet) and recurrent neural network (RNN) with long shortterm memory units...
متن کامل