Improvement of Automatic Genre Classification System for Traditional Malaysian Music Using Beat Features

The increase in processing power and storage of computer has resulted in the growth of digital musical files, which demands some form of organization such as classification of the files. Typically, manual classification is used but it is expensive both in terms of time and money. One alternative solution is to automate musical genre classification. Existing systems have been developed to classify Western musical genres such as pop, rock and classical. However, adapting these systems for traditional Malay music is difficult due to the differences in musical structures and modes. In general, the musical structure of many genres in traditional Malay music is rhythmic and repetitive, which is different than Western music. This study investigates the effects of factors and audio feature set combinations towards the classification of traditional Malay musical genres. Ten traditional Malay musical genres are introduced in this study: Dikir Barat, Etnik Sabah, Gamelan, Ghazal, Inang, Joget, Keroncong, Tumbuk Kalang, Wayang Kulit and Zapin. The study is conducted in three phases. The first phase investigates the factors affecting classification of traditional Malay music: dataset size, track length, track location, number of cross-validation folds, and classifier. The second phase investigates the effect of feature set combinations on the classification result of traditional Malay music. The combinations are STFT, MFCC, STFT and Beat, MFCC and Beat, and STFT, MFCC and Beat. Following this, an automated classification system is developed and named MAGCLAST (Musical Analysis and Genre CLAssification System for Traditional Malay Music). The performance of MAGCLAST against three groups human (expert, trained and untrained) is tested in the final phase of the study. Results show that its classification at 66.3% is comparable to MARSYAS (61%) and trained human (70.6%). Interestingly, MAGCLAST also outperforms classification by average Malaysians, suggesting that an automated system for classifying traditional Malay music is certainly needed. Additionally, a small-scale study on human classification behaviour is also done to understand the factors that affect classification. It is hoped that the information could be exploited to enhance existing automated genre classification system in the near future

An Artificial Intelligence Approach to Concatenative Sound Synthesis

Sound examples are included with this thesisTechnological advancement such as the increase in processing power, hard disk capacity and network bandwidth has opened up many exciting new techniques to synthesise sounds, one of which is Concatenative Sound Synthesis (CSS). CSS uses data-driven method to synthesise new sounds from a large corpus of small sound snippets. This technique closely resembles the art of mosaicing, where small tiles are arranged together to create a larger image. A ‘target’ sound is often specified by users so that segments in the database that match those of the target sound can be identified and then concatenated together to generate the output sound. Whilst the practicality of CSS in synthesising sounds currently looks promising, there are still areas to be explored and improved, in particular the algorithm that is used to find the matching segments in the database. One of the main issues in CSS is the basis of similarity, as there are many perceptual attributes which sound similarity can be based on, for example it can be based on timbre, loudness, rhythm, and tempo and so on. An ideal CSS system needs to be able to decipher which of these perceptual attributes are anticipated by the users and then accommodate them by synthesising sounds that are similar with respect to the particular attribute. Failure to communicate the basis of sound similarity between the user and the CSS system generally results in output that mismatches the sound which has been envisioned by the user. In order to understand how humans perceive sound similarity, several elements that affected sound similarity judgment were first investigated. Of the four elements tested (timbre, melody, loudness, tempo), it was found that the basis of similarity is dependent on humans’ musical training where musicians based similarity on the timbral information, whilst non-musicians rely on melodic information. Thus, for the rest of the study, only features that represent the timbral information were included, as musicians are the target user for the findings of this study. Another issue with the current state of CSS systems is the user control flexibility, in particular during segment matching, where features can be assigned with different weights depending on their importance to the search. Typically, the weights (in some existing CSS systems that support the weight assigning mechanism) can only be assigned manually, resulting in a process that is both labour intensive and time consuming. Additionally, another problem was identified in this study, which is the lack of mechanism to handle homosonic and equidistant segments. These conditions arise when too few features are compared causing otherwise aurally different sounds to be represented by the same sonic values, or can also be a result of rounding off the values of the features extracted. This study addresses both of these problems through an extended use of Artificial Intelligence (AI). The Analysis Hierarchy Process (AHP) is employed to enable order dependent features selection, allowing weights to be assigned for each audio feature according to their relative importance. Concatenation distance is used to overcome the issues with homosonic and equidistant sound segments. The inclusion of AI results in a more intelligent system that can better handle tedious tasks and minimize human error, allowing users (composers) to worry less of the mundane tasks, and focusing more on the creative aspects of music making. In addition to the above, this study also aims to enhance user control flexibility in a CSS system and improve similarity result. The key factors that affect the synthesis results of CSS were first identified and then included as parametric options which users can control in order to communicate their intended creations to the system to synthesise. Comprehensive evaluations were carried out to validate the feasibility and effectiveness of the proposed solutions (timbral-based features set, AHP, and concatenation distance). The final part of the study investigates the relationship between perceived sound similarity and perceived sound interestingness. A new framework that integrates all these solutions, the query-based CSS framework, was then proposed. The proof-of-concept of this study, ConQuer, was developed based on this framework. This study has critically analysed the problems in existing CSS systems. Novel solutions have been proposed to overcome them and their effectiveness has been tested and discussed, and these are also the main contributions of this study.Malaysian Minsitry of Higher Education, Universiti Putra Malaysi

Content-based image retrieval system for marine invertebrates

There are many marine life around the world where it is essential to have proper documentation for future records. Many information retrieval systems for marine science today require text input from user and can only be accessed online. Therefore, people who do not know the name of the marine species or do not have Internet access cannot search using the systems. Responding to this important need, this work aims to develop a Content-based Image Retrieval (CBIR) system for marine invertebrates based on colour and shape features. With the CBIR system for marine invertebrates, users can use the system to look for marine invertebrates' species instead of using traditional methods of searching such as using books and encyclopedias. Users can easily upload the image of marine invertebrate that they want to search into the system and the system will retrieve all the other similar images of marine invertebrates along with their description. All the system interface's buttons, icons and text were designed in a way where any user can easily understand and further learn to operate the system themselves. Based on the retrieval effectiveness experiment and questionnaire-based survey, the proposed CBIR system for marine invertebrates is shown to be effective, help users search similar images of marine invertebrates, provide concise information on marine invertebrate's species for learning purposes, and is reliable and user-friendly

A preliminary framework of measuring music attractiveness based on facial beauty theory

Music is a performing art that put varieties of sound together to form a sequence of sound that people will find it interesting. In past, only those with certain level of musical knowledge were able to compose music as it is very time consuming to learn and practice musical instruments. Since the advent of modern computing, various methods were introduced to help music composition, such as Markov Chain (MC), Genetic Algorithm, Knowledge-Based system, and Cellular Automata. However, previous studies were paying less effort on evaluating the quality of music from human perception. The proposed system will apply a hybrid concept by combining few composition techniques to generate music with musical patterns. A framework on determining characteristics of a good music will be proposed in this paper as well

Using concatenation cost for unit selection of homosonic segments in concatenative sound synthesis

This paper studies the issues surrounding the search and selection process in a general CSS system which may affect the synthesis result, namely the homosonic segments. Homosonic segments are first termed in this study, where it refers to audio files which have one or more of the same sonic properties with each other, but do not sound the same acoustically when played due to the limited audio features extracted during the analysis process. These homosonic segments create confusions within the CSS selection engine. This study proposes a robust solution to overcome this issue by introducing the concatenation cost in addition to the regular target cost. The experiment conducted in this study observes that the use of concatenation cost to help solve the problem is feasible. Further evaluation also suggests that the concatenation cost is an effective solution in solving the challenges involving homosonic segments as the sounds synthesised through concatenation cost function have a better accuracy and possess higher fluency when concatenated from one segment to the next

A review on gesture recognition technology in children's interactive storybook

Over the past few years, gesture recognition has made its debut in education and virtual reality environment. This paper reviews the current literature in gesture recognition technology for interactive storybooks and the existing methods and challenges for this technology. A conceptual framework is proposed to resolve two main challenges that have been reviewed from previous work: to provide a novel interaction to young children and to ensure accuracy of gesture when using gesture based input sensor. The proposed method is the future work which provides the direction towards developing virtual reality storybooks for children

A performance optimization model of task scheduling towards green cloud computing

Cloud computing becomes a powerful trend in the development of ICT services. It allows dynamic resource scaling from infinite resource pool for supporting Cloud users. Such scenario leads to necessity of larger size of computing infrastructure and increases processing power. Demand on the cloud computing is continually growth that makes it changes to scope of green cloud computing. It aims to reduce energy consumption in Cloud computing while maintaining a better performance. However, there is lack of performance metric that analyzing trade-off between energy consumption and performance. Considering high volume of mixed users’ requirements and diversity of services offered; an appropriate performance model for achieving better balance between Cloud performance and energy consumption is needed. In this work, we focus on green Cloud Computing through scheduling optimization model. Specifically, we investigate a relationship between performance metrics that chosen in scheduling approaches with energy consumption for energy efficiency. Through such relationship, we develop an energy-based performance model that provides a clear picture on parameter selection in scheduling for effective energy management. We believed that better understanding on how to model the scheduling performance will lead to green Cloud computing

Using continuous spatial configuration for bezel issues in a multi-mobile system

With the rapid moving technology and innovation, the current digital technology such as smartphones and tabletop system have become vital necessities to accommodate people’s daily activities. As a more robust alternative to tabletop system, the multi-mobile system is also benefiting humans’ interaction by combining multiple mobile devices to become a shared and larger touch surface display. This paper demonstrates the study on effects of bezels on a multi-mobile system which allows users to perform collaborative drawing task with mobile devices in an ad-hoc manner. Unfortunately, gaps and physical design of the mobile devices between the mobile displays cause inherent design problems to the multi-display structure. Before conducting the experiments, two prototypes have been designed; high-fidelity prototype (without solution) and iterative prototype (with the continuous spatial configuration). Two user studies have been conducted with the prototypes by observing groups of students performing an interactive drawing task and the findings were compared. Results from the first user study show gaps and disjointed objects were observed in the drawing outcomes, while in the second user study, where the Continuous Spatial Configuration was implemented as a solution to this bezel issue, the gaps and spaces between the screens were eliminated by 94.8%. From this study, it is believed that implementing the Continuous Spatial Configuration in the prototype designs can improve the user experience in the context of collaboration beyond the use of expensive tabletops systems

Content-based feature fusion representation for marine invertebrates

Marine species representation and retrieval is crucial for its studies and conservation. The images of these animals are usually captured underwater with complex background, at different angle, position, and size, which makes it very hard to provide a good representation with the current methods. Most of the current methods only support content-based representation for marine life images with clear background (taken in laboratory or in environments which have been set up), containing just one animal in an image, or the animal is positioned nicely at the centre of the image. Responding to these important needs, a multi-feature method for Content-based Image Retrieval (CBIR) that employs colour, shape, and texture information of marine life images is proposed. The colour feature vectors are obtained by extracting first and second order of Colour Moments. Shape information is constructed through the implementation of Discrete Wavelet transform up to four sub-bands and the extraction of Canny edge feature. Texture features are obtained with the Zernike Moments (ZM) of order four and the extraction of few Grey Level Co-occurrence Matrix properties. We conducted two experiments to determine the best order of ZM as well as to measure the retrieval performance of the proposed descriptor. Retrieval results based on marine invertebrate and Fish4Knowledge datasets clearly shown that the proposed method has effectively obtained the best precision value at 11 standard recall levels (72.42%) and MAP value (67.7%). The proposed method is further measured based on the statistical two-tailed paired t-test and has revealed a significant improvement in retrieval effectiveness

Artificial immune recognition system with nonlinear resource allocation method and application to traditional Malay music genre classification

Artificial Immune Recognition System (AIRS) has shown an effective performance on several machine learning problems. In this study, the resource allocation method of AIRS was changed with a nonlinear method. This new algorithm, AIRS with nonlinear resource allocation method, was used as a classifier in Traditional Malay Music (TMM) genre classification. Music genre classification has a great important role in music information retrieval systems nowadays. The proposed system consists of three stages: feature extraction, feature selection and finally using proposed algorithm as a classifier. Based on results of conducted experiments, the obtained classification accuracy of proposed system is 88.6 % using 10 fold cross validation for TMM genre classification. The results also show that AIRS with nonlinear allocation method obtains maximum classification accuracy for TMM genre classification