Open AccessProceedings Article
Data Collection in Real Acoustical Environments for Sound Scene Understanding and Hands-Free Speech Recognition
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EUROSPEECH1999: the 6th European Conference on Speech Communication and Techinology, September 5-9, 1999, Budapest, Hungary.Abstract:
EUROSPEECH1999: the 6th European Conference on Speech Communication and Techinology, September 5-9, 1999, Budapest, Hungary.read more
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Robust sound event classification using deep neural networks
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Robust sound event recognition using convolutional neural networks
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AENet: Learning Deep Audio Features for Video Analysis
TL;DR: In this article, the authors proposed a new deep network for audio event recognition, called AENet, which uses a convolutional neural network (CNN) operating on a large temporal input.
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Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification
TL;DR: DNN based transfer learning is proposed for Acoustic Scene Classification by exploiting VOC DNN's ability of learning beyond its pre-trained environments and its improved performance is verified by comparing it to prominent conventional methods.
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Continuous robust sound event classification using time-frequency features and deep learning
TL;DR: This paper proposes and evaluates a novel Bayesian-inspired front end for the segmentation and detection of continuous sound recordings prior to classification, and benchmarks several high performing isolated sound classifiers to operate with continuous sound data by incorporating an energy-based event detection front end.
References
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Proceedings Article
Use of different microphone array configurations for hands-free speech recognition in noisy and reverberant environment.
TL;DR: In this work hands-free continuous speech recognition based on microphone arrays is investigated and HMM adaptation was used to realign the recognizer acoustic modeling to the given acoustic condition.