Proceedings ArticleDOI
Deep Unsupervised Representation Learning for Abnormal Heart Sound Classification
Shahin Amiriparian,Maximilian Schmitt,Nicholas Cummins,Kun Qian,Fengquan Dong,Björn Schuller +5 more
- Vol. 2018, pp 4776-4779
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TLDR
A comparison of conventional and state-of-theart deep learning based computer audition paradigms for the audio classification task of normal, mild abnormalities, and moderate/severe abnormalities as present in phonocardiogram recordings is presented.Abstract:
Given the world-wide prevalence of heart disease, the robust and automatic detection of abnormal heart sounds could have profound effects on patient care and outcomes. In this regard, a comparison of conventional and state-of-theart deep learning based computer audition paradigms for the audio classification task of normal, mild abnormalities, and moderate/severe abnormalities as present in phonocardiogram recordings, is presented herein. In particular, we explore the suitability of deep feature representations as learnt by sequence to sequence autoencoders based on the auDeep toolkit. Key results, gained on the new Heart Sounds Shenzhen corpus, indicate that a fused combination of deep unsupervised features is well suited to the three-way classification problem, achieving our highest unweighted average recall of 47.9% on the test partition.read more
Citations
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Scalogram based prediction model for respiratory disorders using optimized convolutional neural networks.
TL;DR: A pre-trained optimized Alexnet Convolutional Neural Network (CNN) architecture is proposed for predicting respiratory disorders and achieves significant performance improvement in accuracy compared to the existing state-of- the-art techniques in literature.
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Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
TL;DR: Experimental results of three natural language tasks confirm that the proposed RNN--SVAE yields higher performance than two benchmark models, and the mean and standard deviation of the continuous semantic space are learned to take advantage of the variational method.
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Natural Language Processing Methods for Acoustic and Landmark Event-Based Features in Speech-Based Depression Detection
TL;DR: A framework for analyzing speech as a sequence of acoustic events, which combines acoustic words and speech landmarks, which are articulation-related speech events is proposed, and its application to depression detection is investigated.
Journal ArticleDOI
Machine Listening for Heart Status Monitoring: Introducing and Benchmarking HSS—The Heart Sounds Shenzhen Corpus
Fengquan Dong,Kun Qian,Zhao Ren,Alice Baird,Xinjian Li,Zhenyu Dai,Bo Dong,Florian Metze,Yoshiharu Yamamoto,Björn Schuller +9 more
TL;DR: This paper introduces the publicly accessible database, the Heart Sounds Shenzhen Corpus (HSS), and provides a survey of machine learning work in the area of heart sound recognition, as well as a benchmark for HSS utilising standard acoustic features and machine learning models.
Proceedings ArticleDOI
Predicting Biological Signals from Speech: Introducing a Novel Multimodal Dataset and Results
TL;DR: The BioSpeech Database is presented, a novel database of audio and biological signals - blood volume pulse (BVP) and skin conductance) - from 55 individuals speaking aloud in front of others, whilst having their emotional state annotated in real time.
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