A laboratory-based experiment procedure of reception plate method for structure-borne sound source characterisation is reported in this paper. The method uses the assumption that the input power from the source installed on the plate is equal to the power dissipated by the plate. In this experiment, rectangular plates having high and low mobility relative to that of the source were used as the reception plates and a small electric fan motor was acting as the structure-borne source. The data representing the source characteristics, namely, the free velocity and the source mobility, were obtained and compared with those from direct measurement. Assumptions and constraints employing this method are discussed.
Studies of Irrawaddy dolphins' acoustics assist in understanding the behaviour of the species and thereby conservation of this species. Whistle signals emitted by Irrawaddy dolphin within the Bay of Brunei in Malaysian waters were characterized. A total of 199 whistles were analysed from seven sightings between January and April 2016. Six types of whistles contours named constant, upsweep, downsweep, concave, convex, and sine were detected when the dolphins engaged in traveling, foraging, and socializing activities. The whistle durations ranged between 0.06 and 3.86 s. The minimum frequency recorded was 443 Hz [Mean = 6000 Hz, standard deviation (SD) = 2320 Hz] and the maximum frequency recorded was 16 071 Hz (Mean = 7139 Hz, SD = 2522 Hz). The mean frequency range (F.R.) for the whistles was 1148 Hz (Minimum F.R. = 0 Hz, Maximum F.R. = 4446 Hz; SD = 876 Hz). Whistles in the Bay of Brunei were compared with population recorded from the waters of Matang and Kalimantan. The comparisons showed differences in whistle duration, minimum frequency, start frequency, and number of inflection point. Variation in whistle occurrence and frequency may be associated with surface behaviour, ambient noise, and recording limitation. This will be an important element when planning a monitoring program.
Whistles emitted by Indo-Pacific humpback dolphins in Zhanjiang waters, China, were collected by using autonomous acoustic recorders. A total of 529 whistles with clear contours and signal-to-noise ratio higher than 10 dB were extracted for analysis. The fundamental frequencies and durations of analyzed whistles were in ranges of 1785-21 675 Hz and 30-1973 ms, respectively. Six tonal types were identified: constant, downsweep, upsweep, concave, convex, and sine whistles. Constant type was the most dominant tonal type, accounting for 32.51% of all whistles, followed by sine type, accounting for 19.66% of all whistles. This paper examined 17 whistle parameters, which showed significant differences among the six tonal types. Whistles without inflections, gaps, and stairs accounted for 62.6%, 80.6%, and 68.6% of all whistles, respectively. Significant intraspecific differences in all duration and frequency parameters of dolphin whistles were found between this study and the study in Malaysia. Except for start frequency, maximum frequency and the number of harmonics, all whistle parameters showed significant differences between this study and the study conducted in Sanniang Bay, China. The intraspecific differences in vocalizations for this species may be related to macro-geographic and/or environmental variations among waters, suggesting a potential geographic isolation among populations of Indo-Pacific humpback dolphins.
We describe the work on infusion of emotion into a limited-task autonomous spoken conversational agent situated in the domestic environment, using a need-inspired task-independent emotion model (NEMO). In order to demonstrate the generation of affect through the use of the model, we describe the work of integrating it with a natural-language mixed-initiative HiFi-control spoken conversational agent (SCA). NEMO and the host system communicate externally, removing the need for the Dialog Manager to be modified, as is done in most existing dialog systems, in order to be adaptive. The first part of the paper concerns the integration between NEMO and the host agent. The second part summarizes the work on automatic affect prediction, namely, frustration and contentment, from dialog features, a non-conventional source, in the attempt of moving towards a more user-centric approach. The final part reports the evaluation results obtained from a user study, in which both versions of the agent (non-adaptive and emotionally-adaptive) were compared. The results provide substantial evidences with respect to the benefits of adding emotion in a spoken conversational agent, especially in mitigating users' frustrations and, ultimately, improving their satisfaction.
This paper presents a new application of the Particle Swarm Optimization (PSO) algorithm to optimize Mel Frequency Cepstrum Coefficients (MFCC) parameters, in order to extract an optimal feature set for diagnosis of hypothyroidism in infants using Multi-Layer Perceptrons (MLP) neural network. MFCC features is influenced by the number of filter banks (f(b)) and the number of coefficients (n(c)) used. These parameters are critical in representation of the features as they affect the resolution and dimensionality of the features. In this paper, the PSO algorithm was used to optimize the values of f(b) and n(c). The MFCC features based on the PSO optimization were extracted from healthy and unhealthy infant cry signals and used to train MLP in the classification of hypothyroid infant cries. The results indicate that the PSO algorithm could determine the optimum combination of f(b) and n(c) that produce the best classification accuracy of the MLP.
Cardiovascular disease (CVD) is the leading cause of death worldwide, and due to the lack of early detection techniques, the incidence of CVD is increasing day by day. In order to address this limitation, a knowledge based system with embedded intelligent heart sound analyser (KBHSA) has been developed to diagnose cardiovascular disorders at early stages. The system analyses digitized heart sounds that are recorded from an electronic stethoscope using advanced digital signal processing and artificial intelligence techniques. KBHSA takes into account data including the patient's personal and past medical history, clinical examination, auscultation findings, chest x-ray and echocardiogram, and provides a list of diseases that it has diagnosed. The system can assist the general physician in making more accurate and reliable diagnosis under emergency conditions where expert cardiologists and advanced equipment are not readily available. To test the validity of the system, abnormal heart sound samples and medical data from 40 patients were recorded and analysed. The diagnoses made by the system were counter checked by four senior cardiologists in Malaysia. The results show that the findings of KBHSA coincide with those of cardiologists.