Houman Ghaemmaghami

Queensland University of Technology

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Sridha Sridharan

Queensland University of Technology

David Dean

Queensland University of Technology

Clinton Fookes

Queensland University of Technology

Simon Denman

Queensland University of Technology

Tharindu Fernando

Queensland University of Technology

Theekshana Dissanayake

Queensland University of Technology

Shahram Kalantari

Queensland University of Technology

Ahilan Kanagasundaram

Queensland University of Technology

Robbie Vogt

Queensland University of Technology

M. Barzegar

University of Tehran

Cooperative Institutions

Queensland University of Technology

Speech Pathology Australia

University of Queensland

Vision Australia

Institute of Electrical and Electronics Engineers

Tabriz University of Medical Sciences

Signal Processing (United States)

University of Peradeniya

Idiap Research Institute

South Dakota School of Mines and Technology

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Speech Endpoint Detection Using Gradient Based Edge Detection Techniques

Houman Ghaemmaghami Robert Vogt Sridha Sridharan Michael Mason

This paper proposes a novel method for speech endpoint detection. The developed method utilises gradient based edge detection algorithms, used in image processing, to detect boundaries of continuous speech in noisy conditions. It is simple and has low computational complexity. The accuracy of the proposed method was evaluated and compared to the ITU-T G.729 Annex-B voice activity detection (VAD) algorithm. To do this, the two algorithms were tested using a synthetically produced noisy-speech database, consisting of noisy-speech signals at various lengths and SNR. The results indicated that the developed method outperforms the G.729-B VAD algorithm at various signal-to-noise ratios.

Detection theory

10.1109/icspcs.2008.4813711

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Citations (2)

The utility of the analysis of the gaussianity of snore related sounds in the diagnosis of obstructive sleep apnea

Houman Ghaemmaghami Udantha R. Abeyratne Craig Hukins Brett Duce

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Application of anomaly technique in wind turbine bearing fault detection

IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society (2014)

Afrooz Purarjomandlangrudi Ghavameddin Nourbakhsh Houman Ghaemmaghami Andy Tan

Bearing faults are the most common cause of wind turbine failures. Unavailability and maintenance cost of wind turbines are becoming critically important, with their fast growing in electric networks. Early fault detection can reduce outage time and costs. This paper proposes Anomaly Detection (AD) machine learning algorithms for fault diagnosis of wind turbine bearings. The application of this method on a real data set was conducted and is presented in this paper. For validation and comparison purposes, a set of baseline results are produced using the popular one-class SVM methods to examine the ability of the proposed technique in detecting incipient faults.

Unavailability

Anomaly (physics)

Condition Monitoring

10.1109/iecon.2014.7048774

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Citations (6)

Normal probability testing of snore signals for diagnosis of obstructive sleep apnea

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2009)

Houman Ghaemmaghami Udantha R. Abeyratne Craig Hukins

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The standard method of OSA diagnosis is known as polysomnography (PSG), which requires an overnight stay in a specifically equipped facility, connected to over 15 channels of measurements. PSG requires (i) contact instrumentation and, (ii) the expert human scoring of a vast amount of data based on subjective criteria. PSG is expensive, time consuming and is difficult to use in community screening or pediatric assessment. Snoring is the most common symptom of OSA. Despite the vast potential, however, it is not currently used in the clinical diagnosis of OSA. In this paper, we propose a novel method of snore signal analysis for the diagnosis of OSA. The method is based on a novel feature that quantifies the non-Gaussianity of individual episodes of snoring. The proposed method was evaluated using overnight clinical snore sound recordings of 86 subjects. The recordings were made concurrently with routine PSG, which was used to establish the ground truth via standard clinical diagnostic procedures. The results indicated that the developed method has a detectability accuracy of 97.34%.

Gold standard (test)

10.1109/iembs.2009.5333733

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Citations (32)

Noise robust voice activity detection using normal probability testing and time-domain histogram analysis

IEEE International Conference on Acoustics Speech and Signal Processing (2010)

Houman Ghaemmaghami David Dean Sridha Sridharan Iain McCowan

This paper presents a method of voice activity detection (VAD) suitable for high noise scenarios, based on the fusion of two complementary systems. The first system uses a proposed non-Gaussianity score (NGS) feature based on normal probability testing. The second system employs a histogram distance score (HDS) feature that detects changes in the signal through conducting a template-based similarity measure between adjacent frames. The decision outputs by the two systems are then merged using an open-by-reconstruction fusion stage. Accuracy of the proposed method was compared to several baseline VAD methods on a database created using real recordings of a variety of high-noise environments.

Feature (linguistics)

Similarity (geometry)

10.1109/icassp.2010.5495612

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Understanding the Importance of Heart Sound Segmentation for Heart Anomaly Detection

arXiv (Cornell University) (2020)

Theekshana Dissanayake Tharindu Fernando Simon Denman Sridha Sridharan Houman Ghaemmaghami

Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed. Some researchers in the field argue the segmentation step is an unwanted computational burden, whereas others embrace it as a prior step to feature extraction. When comparing accuracies achieved by studies that have segmented heart sounds before analysis with those who have overlooked that step, the question of whether to segment heart sounds before feature extraction is still open. In this study, we explicitly examine the importance of heart sound segmentation as a prior step for heart sound classification, and then seek to apply the obtained insights to propose a robust classifier for abnormal heart sound detection. Furthermore, recognizing the pressing need for explainable Artificial Intelligence (AI) models in the medical domain, we also unveil hidden representations learned by the classifier using model interpretation techniques. Experimental results demonstrate that the segmentation plays an essential role in abnormal heart sound classification. Our new classifier is also shown to be robust, stable and most importantly, explainable, with an accuracy of almost 100% on the widely used PhysioNet dataset.

Heart sounds

Phonocardiogram

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Citations (16)

Noise robust voice activity detection using features extracted from the time-domain autocorrelation function

Interspeech 2022 (2010)

Houman Ghaemmaghami Brendan Baker Robert Vogt Sridha Sridharan

This paper presents a method of voice activity detection (VAD) for high noise scenarios, using a noise robust voiced speech detection feature. The developed method is based on the fusion of two systems. The first system utilises the maximum peak of the normalised time-domain autocorrelation function (MaxPeak). The second zone system uses a novel combination of cross-correlation and zero-crossing rate of the normalised autocorrelation to approximate a measure of signal pitch and periodicity (CrossCorr) that is hypothesised to be noise robust. The score outputs by the two systems are then merged using weighted sum fusion to create the proposed autocorrelation zero-crossing rate (AZR) VAD. Accuracy of AZR was compared to state of the art and standardised VAD methods and was shown to outperform the best performing system with an average relative improvement of 24.8% in half-total error rate (HTER) on the QUT-NOISE-TIMIT database created using real recordings from high-noise environments.

Autocorrelation technique

Pitch detection algorithm

10.21437/interspeech.2010-776

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Citations (44)

Domain Generalization in Biosignal Classification

arXiv (Cornell University) (2020)

Theekshana Dissanayake Tharindu Fernando Simon Denman Houman Ghaemmaghami Sridha Sridharan

Objective: When training machine learning models, we often assume that the training data and evaluation data are sampled from the same distribution. However, this assumption is violated when the model is evaluated on another unseen but similar database, even if that database contains the same classes. This problem is caused by domain-shift and can be solved using two approaches: domain adaptation and domain generalization. Simply, domain adaptation methods can access data from unseen domains during training; whereas in domain generalization, the unseen data is not available during training. Hence, domain generalization concerns models that perform well on inaccessible, domain-shifted data. Method: Our proposed domain generalization method represents an unseen domain using a set of known basis domains, afterwhich we classify the unseen domain using classifier fusion. To demonstrate our system, we employ a collection of heart sound databases that contain normal and abnormal sounds (classes). Results: Our proposed classifier fusion method achieves accuracy gains of up to 16% for four completely unseen domains. Conclusion: Recognizing the complexity induced by the inherent temporal nature of biosignal data, the two-stage method proposed in this study is able to effectively simplify the whole process of domain generalization while demonstrating good results on unseen domains and the adopted basis domains. Significance: To our best knowledge, this is the first study that investigates domain generalization for biosignal data. Our proposed learning strategy can be used to effectively learn domain-relevant features while being aware of the class differences in the data.

Biosignal

Domain Adaptation

10.48550/arxiv.2011.06207

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Speaker attribution of multiple telephone conversations using a complete-linkage clustering approach

Houman Ghaemmaghami David Dean Robbie Vogt Sridha Sridharan

In this paper we propose and evaluate a speaker attribution system using a complete-linkage clustering method. Speaker attribution refers to the annotation of a collection of spoken audio based on speaker identities. This can be achieved using diarization and speaker linking. The main challenge associated with attribution is achieving computational efficiency when dealing with large audio archives. Traditional agglomerative clustering methods with model merging and retraining are not feasible for this purpose. This has motivated the use of linkage clustering methods without retraining. We first propose a diarization system using complete-linkage clustering and show that it outperforms traditional agglomerative and single-linkage clustering based diarization systems with a relative improvement of 40% and 68%, respectively. We then propose a complete-linkage speaker linking system to achieve attribution and demonstrate a 26% relative improvement in attribution error rate (AER) over the single-linkage speaker linking approach.

Speaker diarisation

Linkage (software)

Word error rate

Hierarchical clustering

Complete-linkage clustering

10.1109/icassp.2012.6288841

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Citations (23)

A study on the effects of using short utterance length development data in the design of GPLDA speaker verification systems

International Journal of Speech Technology (2017)

Ahilan Kanagasundaram David Dean Sridha Sridharan Houman Ghaemmaghami Clinton Fookes

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