Hidden Markov Model-Based Asthmatic Wheeze Recognition Algorithm Leveraging the Parallel Ultra-Low-Power Processor (PULP)

Oletić, Dinko; Matijaščić, Marko; Bilas, Vedran; Magno, Michele

doi:10.1109/sas.2019.8706033

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…In these systems, the time stream of audio feature vectors is classified by a range of traditional machine learning techniques. These include Logistic Regression (LR) [165], k-Nearest Neighbour (KNN) [163,168], Hidden Markov Models (HMM) [161,169,170],…”

Section: Literature Reviewmentioning

confidence: 99%

“…A more serious issue with this research field has been the difficulty of comparing between techniques due to the lack of standardised datasets used by authors for evaluation. Most publications evaluated on proprietary datasets that are unavailable to others [165,166,169,176,182]. Comparing this to ASC, it is much easier to record an ASC dataset (no ethics approvals required, no access to patients, no infection control issues etc.)…”

Section: Exiting Issues and Proposed Solutionmentioning

confidence: 99%

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Robust Deep Learning Frameworks For Acoustic Scene and Respiratory Sound Classification

Pham¹

2021

Preprint

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Although research on Acoustic Scene Classification (ASC) is very close to, or even overshadowed by different popular research areas known as Automatic Speech Recognition (ASR), Speaker Recognition (SR) or Image Processing (IP), this field potentially opens up several distinct and meaningful application areas based on environment context detection.The challenges of ASC mainly come from different noise resources, various sounds in real-world environments, occurring as single sounds, continuous sounds or overlapping sounds.In comparison to speech, sound scenes are more challenging mainly due to their being unstructured in form and closely similar to noise in certain contexts. Although a wide range of publications have focused on ASC recently, they show task-specific ways that either explore certain aspects of an ASC system or are evaluated on limited acoustic scene datasets. Therefore, the aim of this thesis is to contribute to the development of a robust framework to be applied for ASC, evaluated on various recently published datasets, and to achieve competitive performance compared to the state-of-the-art systems.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Exiting Issues and Proposed Solutionmentioning

confidence: 99%

Robust Deep Learning Frameworks For Acoustic Scene and Respiratory Sound Classification

Pham¹

2021

Preprint

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“…In these systems, the stream of audio feature vectors is classified by a range of traditional machine learning techniques. These include Logistic Regression [9], k-Nearest Neighbour (KNN) [7], [12], Hidden Markov Models [5], [13], [14], Support Vector Machines [7], [10], [12], [15] and decision trees [6], [7], [8], [16].…”

Section: Introductionmentioning

confidence: 99%

“…A more serious issue with this research field has been the difficulty of comparing between techniques due to the lack of standardised datasets for evaluation. Most publications evaluate on proprietary datasets that are unavailable to others [9], [10], [13], [19], [25].…”

Section: Introductionmentioning

confidence: 99%

CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection

Pham

Phan

Palaniappan

et al. 2021

IEEE J. Biomed. Health Inform.

100

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This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from respiratory sound recordings. The framework begins with front-end feature extraction that transforms input sound into a spectrogram representation. Then, a back-end deep learning network is used to classify the spectrogram features into categories of respiratory anomaly cycles or diseases. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, confirm three main contributions towards respiratory-sound analysis. Firstly, we carry out an extensive exploration of the effect of spectrogram types, spectral-time resolution, overlapping/non-overlapping windows, and data augmentation on final prediction accuracy. This leads us to propose a novel deep learning system, built on the proposed framework, which outperforms current state-of-the-art methods. Finally, we apply a Teacher-Student scheme to achieve a trade-off between model performance and model complexity which holds promise for building real-time applications.

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“…Although many machine learning methods participated in this field, here is an inconsistency between dataset and performance comparison among publications. For instance, some authors in [11], [12], [13], [14], [15] evaluated their systems over unpublished datasets. Furthermore, it is hard to compare performance when systems proposed use different ratio for splitting data, especially patient's objects.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds

Ngo¹,

Pham²,

Nguyen³

et al. 2020

Preprint

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This paper proposes a robust deep learning framework used for classifying anomaly of respiratory cycles. Initially, our framework starts with front-end feature extraction step. This step aims to transform the respiratory input sound into a two-dimensional spectrogram where both spectral and temporal features are well presented. Next, an ensemble of C-DNN and Autoencoder networks is then applied to classify into four categories of respiratory anomaly cycles. In this work, we conducted experiments over 2017 Internal Conference on Biomedical Health Informatics (ICBHI) benchmark dataset. As a result, we achieve competitive performances with ICBHI average score of 0.49, ICBHI harmonic score of 0.42.

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Hidden Markov Model-Based Asthmatic Wheeze Recognition Algorithm Leveraging the Parallel Ultra-Low-Power Processor (PULP)

Cited by 4 publications

References 10 publications

Robust Deep Learning Frameworks For Acoustic Scene and Respiratory Sound Classification

Robust Deep Learning Frameworks For Acoustic Scene and Respiratory Sound Classification

CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection

Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds

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