@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}
}
@conference {2019,
	title = {A novel music-based game with motion capture to support cognitive and motor function in the elderly},
	booktitle = {IEEE Conference on Games},
	year = {2019},
	publisher = {IEEE },
	organization = {IEEE },
	address = {London, UK},
	abstract = {This paper presents a novel game prototype that uses music and motion detection as preventive medicine for the elderly.
Given the aging populations around the globe, and the limited resources and staff able to care for these populations, eHealth solutions are becoming increasingly important, if not crucial, additions to modern healthcare and preventive medicine. Furthermore, because compliance rates for performing physical exercises are often quite low in the elderly, systems able to motivate and engage this population are a necessity. Our prototype uses music not only to engage listeners, but also to leverage the efficacy of music to improve mental and physical wellness. The game is based on a memory task to stimulate cognitive function, and requires users to perform physical gestures to mimic the playing of different musical instruments. To this end, the Microsoft Kinect sensor is used together with a newly developed gesture detection module in order to process users{\textquoteright} gestures. The resulting prototype system supports both cognitive functioning and physical strengthening in the elderly. },
	author = {K. Agres and S. Lui and D. Herremans}
}
@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}
}
@article {134,
	title = {Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross Entropy},
	journal = {Neural Computing and Applications},
	year = {2018},
	abstract = {Separating a singing voice from its music accompaniment remains an important challenge in the field of music information retrieval. We present a unique neural network approach inspired by a technique that has revolutionized the field of vision: pixel-wise image classification, which we combine with cross entropy loss and pretraining of the CNN as an autoencoder on singing voice spectrograms. The pixel-wise classification technique directly estimates the sound source label for each time-frequency (T-F) bin in our spectrogram image, thus eliminating common pre-and postprocessing tasks. The proposed network is trained by using the Ideal Binary Mask (IBM) as the target output label. The IBM identifies the dominant sound source in each T-F bin of the magnitude spectrogram of a mixture signal, by considering each T-F bin as a pixel with a multi-label (for each sound source). Cross entropy is used as the training objective, so as to minimize the average probability error between the target and predicted label for each pixel. By treating the singing voice separation problem as a pixel-wise classification task, we additionally eliminate one of the commonly used, yet not easy to comprehend, postprocessing steps: the Wiener filter postprocessing. The proposed CNN outperforms the first runner up in the Music Information Retrieval Evaluation eXchange (MIREX) 2016 and the winner of MIREX 2014 with a gain of 2.2702 \~{} 5.9563 dB global normalized source to distortion ratio (GNSDR) when applied to the iKala dataset. An experiment with the DSD100 dataset on the full-tracks song evaluation task also shows that our model is able to compete with cutting-edge singing voice separation systems which use multichannel modeling, data augmentation, and model blending.},
	doi = {10.1007/s00521-018-3933-z},
	url = {https://rdcu.be/bdoTY},
	author = {K.W.E. Lin and Balamurali BT and E. Koh and S. Lui and D. Herremans}
}
