Efficient Convolutional Neural Network For Audio Event Detection
Wireless distributed systems as used in sensor networks, Internet-of-Things and cyber-physical systems, impose high requirements on resource efficiency. Advanced preprocessing and classification of data at the network edge can help to decrease the communication demand and to reduce the amount of data to be processed centrally. In the area of distributed acoustic sensing, the combination of algorithms with a high classification rate and resource-constraint embedded systems is essential. Unfortunately, algorithms for acoustic event detection have a high memory and computational demand and are not suited for execution at the network edge. This paper addresses these aspects by applying structural optimizations to a convolutional neural network for audio event detection to reduce the memory requirement by a factor of more than 500 and the computational effort by a factor of 2.1 while performing 9.2% better.
Code (0)
등록된 구현이 없습니다.
Tasks
Event DetectionGeneral ClassificationSimilar Papers 제목 키워드 기반
Unifying Isolated and Overlapping Audio Event Detection with Multi-Label Multi-Task Convolutional Recurrent Neural Networks
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events. The framework leverages the power of convolutional recurrent ne…
Event DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAudio-Based Epileptic Seizure Detection
This paper investigates automatic epileptic seizure detection from audio recordings using convolutional neural net- works. The labeling and analysis of seizure events are necessary in the medical field for patient mon…
Event DetectionSeizure DetectionSound Event DetectionMultichannel Sound Event Detection Using 3D Convolutional Neural Networks for Learning Inter-channel Features
In this paper, we propose a stacked convolutional and recurrent neural network (CRNN) with a 3D convolutional neural network (CNN) in the first layer for the multichannel sound event detection (SED) task. The 3D CNN enab…
Event DetectionSound Event DetectionAENet: Learning Deep Audio Features for Video Analysis
We propose a new deep network for audio event recognition, called AENet. In contrast to speech, sounds coming from audio events may be produced by a wide variety of sources. Furthermore, distinguishing them often require…
Action RecognitionData AugmentationEvent DetectionGPU+3Exploring Text-Queried Sound Event Detection with Audio Source Separation
In sound event detection (SED), overlapping sound events pose a significant challenge, as certain events can be easily masked by background noise or other events, resulting in poor detection performance. To address this …
Audio Source SeparationEvent DetectionSound Event Detection