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Originally presented as author's thesis: der Technischen Universität Berlin, 2008.

In the context of location, large amounts of information are available on the Internet to be accessed by people via different devices. However, at times people have to manually search and access it. If the space where location-based services are available can be identified by hand-held devices, people can be prompted with services available around them. This thesis explores the use of ultrasound as a communication medium to tag such spaces and access location-based services with the related information; and demonstrates the indoor implementation of the prototype of a location-based services enabling system for hand-held devices. This system allows users to search and access the available services in their surroundings through their hand-held devices. A beacon generator placed in the service location broadcasts a service code mappable to the services particular to that location encoded in an ultrasound signal. The hand-held device can identify this signal and prompt the user with available services. System design and architecture is demonstrated and the viability of the system is tested through a variety of environments and scenarios showing that potentially this has both a wide range of applications and can enhance the way people access location-based services. / Thesis (Master, Computing) -- Queen's University, 2013-01-30 17:50:20.285

The information age has brought with it a dual problem. In the first place, the ready access to mechanisms to capture and store vast amounts of data in all forms (text, audio, image and video), has resulted in a continued demand for ever more efficient means to store and transmit this data. In the second, the rapidly increasing store demands effective means to structure and access the data in an efficient and meaningful manner. In terms of audio data, the first challenge has traditionally been the realm of audio compression research that has focused on statistical, unstructured audio representations that obfuscate the inherent structure and semantic content of the underlying data. This has only served to further complicate the resolution of the second challenge resulting in access mechanisms that are either impractical to implement, too inflexible for general application or too low level for the average user. Thus, an artificial dichotomy has been created from what is in essence a dual problem. The founding motivation of this thesis is that, although the hypermedia model has been identified as the ideal, cognitively justified method for organising data, existing audio data representations and coding models provide little, if any, support for, or resemblance to, this model. It is the contention of the author that any successful attempt to create hyperaudio must resolve this schism, addressing both storage and information management issues simultaneously. In order to achieve this aim, an audio representation must be designed that provides compact data storage while, at the same time, revealing the inherent structure of the underlying data. Thus it is the aim of this thesis to present a representation designed with these factors in mind. Perhaps the most difficult hurdle in the way of achieving the aims of content-based audio coding and information management is that of auditory source separation. The MPEG committee has noted this requirement during the development of its MPEG-7 standard, however, the mechanics of "how" to achieve auditory source separation were left as an open research question. This same committee proposed that MPEG-7 would "support descriptors that can act as handles referring directly to the data, to allow manipulation of the multimedia material." While meta-data tags are a part solution to this problem, these cannot allow manipulation of audio material down to the level of individual sources when several simultaneous sources exist in a recording. In order to achieve this aim, the data themselves must be encoded in such a manner that allows these descriptors to be formed. Thus, content-based coding is obviously required. In the case of audio, this is impossible to achieve without effecting auditory source separation. Auditory source separation is the concern of computational auditory scene analysis (CASA). However, the findings of CASA research have traditionally been restricted to a limited domain. To date, the only real application of CASA research to what could loosely be classified as information management has been in the area of signal enhancement for automatic speech recognition systems. In these systems, a CASA front end serves as a means of separating the target speech from the background "noise". As such, the design of a CASA-based approach, as presented in this thesis, to one of the most significant challenges facing audio information management research represents a significant contribution to the field of information management. Thus, this thesis unifies research from three distinct fields in an attempt to resolve some specific and general challenges faced by all three. It describes an audio representation that is based on a sinusoidal model from which low-level auditory primitive elements are extracted. The use of a sinusoidal representation is somewhat contentious with the modern trend in CASA research tending toward more complex approaches in order to resolve issues relating to co-incident partials. However, the choice of a sinusoidal representation has been validated by the demonstration of a method to resolve many of these issues. The majority of the thesis contributes several algorithms to organise the low-level primitives into low-level auditory objects that may form the basis of nodes or link anchor points in a hyperaudio structure. Finally, preliminary investigations in the representation’s suitability for coding and information management tasks are outlined as directions for future research.

Audio data compression seeks to reduce the size of sound files, making them easier to store and transfer, and is thus a highly valued tool for those working with large sets of audio data. For example, some biologists work with audio recordings of bats, which are well known for their frequent use of ultrasonic echolocation, and so these biologists can accrue massive amounts of high frequency audio data. However, as many methods of audio compression are designed to specialize in the more common range of frequencies, they are not able to sufficiently compress bat audio, and many bat biologists instead work without compressing their data at all. This paper investigates the desiderata of a data compression method in the context of bat biology, experimentally compares several modern data compression algorithms, and discusses their pros and cons in terms of their potential use across various relevant contexts. The paper concludes by suggesting the algorithm Monkey’s Audio for machines able to handle the higher resource demands it has. Otherwise, FLAC and WavPack yield similar size reduction rates at a significantly faster speed while being less resource intensive. Of note is the interesting result produced by the algorithm 7-ZIP PPMd Solid, which achieved consistently outstanding results within a single dataset, but its generalizability has yet to be determined.

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.