Stephen Marsland

IROS Conference 2022 Conference Paper

Design of a low-cost passive acoustic monitoring system for animal localisation from calls

• Benjamin Yen 0001
• Jemima Prins
• Gian Schmid
• Yusuke Hioka
• Susan Ellis
• Stephen Marsland

The field of bioacoustics is concerned with monitoring wild animals based on their vocalisations. Passive acoustic recorders are now commonly used to collect data of the soundscapes of our wild places. While the data they collect is extremely useful, the majority of the recorders use a single omnidirectional microphone, and thus cannot independently perform localisation of a calling animal. Localisation can be useful to differentiate between multiple calling animals, to improve statistical estimates of abundance, and to locate calling posts, which may be close to nests. In this paper, we consider the design of a low-cost, practical, passive directional acoustic recorder that will facilitate animal localisation, and present and evaluate a prototype system for this purpose.

JMLR Journal 2017 Journal Article

Parallel Symmetric Class Expression Learning

• An C. Tran
• Jens Dietrich
• Hans W. Guesgen
• Stephen Marsland

In machine learning, one often encounters data sets where a general pattern is violated by a relatively small number of exceptions (for example, a rule that says that all birds can fly is violated by examples such as penguins). This complicates the concept learning process and may lead to the rejection of some simple and expressive rules that cover many cases. In this paper we present an approach to this problem in description logic learning by computing partial descriptions (which are not necessarily entirely complete) of both positive and negative examples and combining them. Our Symmetric Parallel Class Expression Learning approach enables the generation of general rules with exception patterns included. We demonstrate that this algorithm provides significantly better results (in terms of metrics such as accuracy, search space covered, and learning time) than standard approaches on some typical data sets. Further, the approach has the added benefit that it can be parallelised relatively simply, leading to much faster exploration of the search tree on modern computers. [abs] [ pdf ][ bib ] &copy JMLR 2017. ( edit, beta )

IJCAI Conference 2011 Conference Paper

Behaviour Recognition in Smart Homes

• Sook-Ling Chua
• Stephen Marsland
• Hans W. Guesgen

Behaviour recognition aims to infer the particular behaviours of the inhabitant in a smart home from a series of sensor readings from around the house. There are many reasons to recognise human behaviours; one being to monitor the elderly or cognitively impaired and detect potentially dangerous behaviours. We view the behaviour recognition problem as the task of mapping the sensory outputs to a sequence of recognised activities. This research focuses on the development of machine learning methods to find an approximation to the mapping between sensor outputs and behaviours. However, learning the mapping raises an important issue, which is that the training data is not necessarily annotated with exemplar behaviours of the inhabitant. This doctoral study takes several steps towards addressing the problem of finding an approximation to this mapping, beginning with separate investigations on current methods proposed in the literature, identifying useful sensory outputs for behaviour recognition, and concluding by proposing two directions: one using supervised learning on annotated sensory stream and one using unsupervised learning on unannotated ones.

AAAI Conference 2011 Conference Paper

Convergence Properties of (μ + λ) Evolutionary Algorithms

• Aram Ter-Sarkisov
• Stephen Marsland

AAAI Conference 2011 Conference Paper

Unsupervised Learning of Human Behaviours

• Sook-Ling Chua
• Stephen Marsland
• Hans Guesgen

Behaviour recognition is the process of inferring the behaviour of an individual from a series of observations acquired from sensors such as in a smart home. The majority of existing behaviour recognition systems are based on supervised learning algorithms, which means that training them requires a preprocessed, annotated dataset. Unfortunately, annotating a dataset is a rather tedious process and one that is prone to error. In this paper we suggest a way to identify structure in the data based on text compression and the edit distance between words, without any prior labelling. We demonstrate that by using this method we can automatically identify patterns and segment the data into patterns that correspond to human behaviours. To evaluate the effectiveness of our proposed method, we use a dataset from a smart home and compare the labels produced by our approach with the labels assigned by a human to the activities in the dataset. We find that the results are promising and show significant improvement in the recognition accuracy over Self-Organising Maps (SOMs).

IJCAI Conference 2011 Conference Paper

Unsupervised Learning of Patterns in Data Streams Using Compression and Edit Distance

• Sook-Ling Chua
• Stephen Marsland
• Hans W. Guesgen

Many unsupervised learning methods for recognising patterns in data streams are based on fixed length data sequences, which makes them unsuitable for applications where the data sequences are of variable length such as in speech recognition, behaviour recognition and text classification. In order to use these methods on variable length data sequences, a pre-processing step is required to manually segment the data and select the appropriate features, which is often not practical in real-world applications. In this paper we suggest an unsupervised learning method that handles variable length data sequences by identifying structure in the data stream using text compression and the edit distance between 'words'. We demonstrate that using this method we can automatically cluster unlabelled data in a data stream and perform segmentation. We evaluate the effectiveness of our proposed method using both fixed length and variable length benchmark datasets, comparing it to the Self-Organising Map in the first case. The results show a promising improvement over baseline recognition systems.

ICRA Conference 2002 Conference Paper

A Tale of Two Filters - On-Line Novelty Detection

• Paul A. Crook
• Stephen Marsland
• Gillian M. Hayes
• Ulrich Nehmzow

For mobile robots, as well as other learning systems, the ability to highlight unexpected features of their environment - novelty detection - is very useful. One particularly important application for a robot equipped with novelty detection is inspection, highlighting potential problems in an environment. In this paper two novelty filters, both of which are capable of on-line and off-line novelty detection, are compared for two robot inspection tasks, one using sonar and the other camera images. The benefits and problems of using each of the filters are discussed and demonstrated.

ICRA Conference 2000 Conference Paper

Learning Globally Consistent Maps by Relaxation

• Tom Duckett
• Stephen Marsland
• Jonathan Shapiro

Mobile robots require the ability to build their own maps to operate in unknown environments. A fundamental problem is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of drift errors caused by wheel slippage. The paper introduces a fast, online method of learning globally consistent maps, using only local metric information. The approach differs from previous work in that it is computationally cheap, easy to implement and is guaranteed to find a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot, and quantitative performance measures are used to assess the quality of the maps obtained.