@conference {2022,
	title = {Computationally Efficient Physics Approximating Neural Networks for Highly Nonlinear Maps},
	booktitle = {2022 International Conference on Research in Adaptive and Convergent Systems},
	year = {2022},
	publisher = {2022},
	organization = {2022},
	address = {Virtual},
	doi = {10.1145/3538641.3561501},
	url = {https://dl.acm.org/doi/abs/10.1145/3538641.3561501},
	author = {C.J. Clarke and J. Chowdhury and Balamurali BT and P. Priyadarshinee and C.M. Ying Lim and I. Fu Xing Tan and D. Herremans and J.M. Chen}
}
@conference {2022,
	title = {A Gaussian mixture classifier model to differentiate respiratory symptoms using phonated /ɑ:/ sounds},
	booktitle = {The 18th Australasian International Conference on Speech Science and Technology (SST)},
	year = {2022},
	month = {12/2022},
	publisher = {ASSTA},
	organization = {ASSTA},
	address = {Canberra, Australia},
	abstract = {An audio-based classification model that differentiates between healthy vs pathological respiratory symptoms using acoustic features extracted from phonated /ɑ:/ sounds is presented. For this, a new dataset of phonated /ɑ:/ sounds, together with a clinician{\textquoteright}s diagnosis, was compiled and a Gaussian Mixture Model (GMM) using Mel-Frequency Cepstral Coefficients (MFCCs) classifier was used. Despite no significant differences in mean values of the fundamental and formant frequency (F0, F1, F2, and F3) distribution for /ɑ:/ sounds retrieved from healthy vs pathological populations, our /ɑ:/ sound model trained using MFCCs resulted in an accuracy of 81.92\% when compared against clinician{\textquoteright}s diagnosis.},
	url = {https://sst2022.com/a-gaussian-mixture-classifier-model-to-differentiate-respiratory-symptoms-using-phonated-a\%cb\%90-sounds/},
	author = {Balamurali BT and H.I. Hee and C. Ming and Y. Lin and P. Priyadarshinee and C.J. Clarke and D. Herremans and J.M. Chen}
}
@conference {2020,
	title = {Acoustic prediction of flowrate: varying liquid jet stream onto a free surface},
	booktitle = {IEEE International Conference on Signal Processing and Communications (SPCOM)},
	year = {2020},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Blangladesh, India},
	abstract = {Information on liquid jet stream flow is crucial in many real world applications. In a large number of cases, these flows fall directly onto free surfaces (e.g. pools), creating a splash with accompanying splashing sounds. The sound produced is supplied by energy interactions between the liquid jet stream and the passive free surface. In this investigation, we collect the sound of a water jet of varying flowrate falling into a pool of water, and use this sound to predict the flowrate and flowrate trajectory involved. Two approaches are employed: one uses machine-learning models trained using audio features extracted from the collected sound to predict the flowrate (and subsequently the flowrate trajectory). In contrast, the second method directly uses acoustic parameters related to the spectral energy of the liquid-liquid interaction to estimate the flowrate trajectory. The actual flowrate, however, is determined directly using a gravimetric method: tracking the change in mass of the pooling liquid over time. We show here that the two methods agree well with the actual flowrate and offer comparable performance in accurately predicting the flowrate trajectory, and accordingly offer insights for potential real-life applications using sound.},
	author = {Balamurali BT and E. J. Aslim and Yun Shu Lynn Ng and Tricia Li Chuen Kuo and Jacob Shihang Chen and D. Herremans and Lay Guat Ng and J.M. Chen}
}
@article {2020,
	title = {Asthmatic versus healthy child classification based on cough and vocalised /a:/ sounds},
	journal = {The Journal of the Acoustical Society of America (JASA)},
	volume = {148, EL253},
	year = {2020},
	month = {09/2020},
	type = {Jasa Express Letters},
	abstract = {Cough is a common symptom presenting in asthmatic children. In this investigation, an audio-based classification model is presented that can differentiate between healthy and asthmatic children, based on the combination of cough and vocalised /ɑ:/ sounds. A Gaussian mixture model using mel-frequency cepstral coefficients and constant-Q cepstral coefficients was trained. When comparing the predicted labels with the clinician{\textquoteright}s diagnosis, this cough sound model reaches an overall accuracy of 95.3\%. The vocalised /ɑ:/ model reaches an accuracy of 72.2\%, which is still significant because the dataset contains only 333 /ɑ:/ sounds versus 2029 cough sounds.
},
	doi = {10.1121/10.0001933},
	url = {https://asa.scitation.org/doi/10.1121/10.0001933},
	author = {Balamurali BT and H.I. Hee and O.H. Teoh and K.P. Lee and S. Kapoor and D. Herremans and J.M. Chen}
}
@article {9998,
	title = {Development of Machine Learning for asthmatic and healthy voluntary  cough - a proof of concept study},
	journal = {Applied Sciences},
	volume = {9},
	year = {2019},
	month = {07/2019},
	chapter = {2833},
	abstract = {(1) Background: Cough is a major presentation in childhood asthma. Here, we aim to develop a machine-learning based cough sound classifier for asthmatic and healthy children. (2) Methods: Children less than 16 years old were randomly recruited in a Children{\textquoteright}s Hospital, from February 2017 to April 2018, and were divided into 2 cohorts{\textemdash}healthy children and children with acute asthma presenting with cough. Children with other concurrent respiratory conditions were excluded in the asthmatic cohort. Demographic data, duration of cough, and history of respiratory status were obtained. Children were instructed to produce voluntary cough sounds. These clinically labeled cough sounds were randomly divided into training and testing sets. Audio features such as Mel-Frequency Cepstral Coefficients and Constant-Q Cepstral Coefficients were extracted. Using a training set, a classification model was developed with Gaussian Mixture Model{\textendash}Universal Background Model (GMM-UBM). Its predictive performance was tested using the test set against the physicians{\textquoteright} labels. (3) Results: Asthmatic cough sounds from 89 children (totaling 1192 cough sounds) and healthy coughs from 89 children (totaling 1140 cough sounds) were analyzed. The sensitivity and specificity of the audio-based classification model was 82.81\% and 84.76\%, respectively, when differentiating coughs from asthmatic children versus coughs from {\textquoteleft}healthy{\textquoteright} children. (4) Conclusion: Audio-based classification using machine learning is a potentially useful technique in assisting the differentiation of asthmatic cough sounds from healthy voluntary cough sounds in children.},
	doi = {10.3390/app9142833},
	url = {https://www.mdpi.com/2076-3417/9/14/2833},
	author = {H.I. Hee and Balamurali BT and A. Karunakaran and D. Herremans and O.H. Teoh and K.P. Lee and S.S. Teng and S. Lui and J.M. Chen}
}
@article {2019,
	title = {Towards robust audio spoofing detection: a detailed comparison of traditional and learned features},
	journal = {IEEE Access},
	volume = {7},
	year = {2019},
	pages = {84229 - 84241},
	doi = {10.1109/ACCESS.2019.2923806},
	url = {https://arxiv.org/abs/1905.12439},
	author = {Balamurali BT and K.W.E. Lin and S. Lui and J.M. Chen and D. Herremans}
}
@article {119,
	title = {Minimally Simple Binaural Room Modelling Using a Single Feedback Delay Network},
	journal = {Journal of the Audio Engineering Society},
	volume = {66},
	year = {2018},
	month = {10/2018},
	pages = {791-807},
	abstract = {The most efficient binaural acoustic modeling systems use a multi-tap delay to generate accurately modeled early reflections, combined with a feedback delay network that produces generic late reverberation. We present a method of binaural acoustic simulation that uses one feedback delay network to simultaneously model both first-order reflections and late reverberation. The advantages are simplicity and efficiency. We compare the proposed method against the existing method of modeling binaural early reflections using a multi-tap delay line. Measurements of ISO standard evaluators including interaural correlation coefficient, decay time, clarity, definition, and center time, indicate that the proposed method achieves comparable level of accuracy as less-efficient existing methods. This method is implemented as an iOS application, and is able to auralize input signal directly without convolution and update in real time.},
	doi = {https://doi.org/10.17743/jaes.2018.0045},
	author = {N. Agus and H. Anderson and J.M. Chen and S. Lui and D. Herremans}
}
@article {117,
	title = {Perceptual evaluation of measures of spectral variance},
	journal = {Journal of the Acoustical Society of America},
	volume = {143},
	year = {2018},
	pages = {3300{\textendash}3311},
	abstract = {In many applications, it is desirable to achieve a signal that is as close as possible to ideal white noise. One example is in the design of an artificial reverberator, whereby there is a need for its lossless prototype output from an impulse input to be perceptually white as much as possible. The Ljung-Box test, the Drouiche test, and the Wiener Entropy{\textemdash}also called the Spectral Flatness Measure{\textemdash}are three well-known methods for quantifying the similarity of a given signal to ideal
white noise. In this paper, listening tests are conducted to measure the Just Noticeable Difference (JND) on the perception of white noise, which is the JND between ideal Gaussian white noise and noise with a specified deviation from the flat spectrum. This paper reports the JND values using one of these measures of whiteness, which is the Ljung-Box test. This paper finds considerable disagreement between the Ljung-Box test and the other two methods and shows that none of the methods is a significantly better predictor of listeners{\textquoteright} perception of whiteness. This suggests a need for a whiteness test that is more closely correlated to human perception.},
	doi = {10.1121/1.5040484},
	url = {https://asa.scitation.org/doi/10.1121/1.5040484},
	author = {N. Agus and H. Anderson and J.M. Chen and S. Lui and D. Herremans}
}
