Abstract: In this paper, several improvements are proposed to
previous work of automated classification of alcoholics and nonalcoholics.
In the previous paper, multiplayer-perceptron neural
network classifying energy of gamma band Visual Evoked Potential
(VEP) signals gave the best classification performance using 800
VEP signals from 10 alcoholics and 10 non-alcoholics. Here, the
dataset is extended to include 3560 VEP signals from 102 subjects:
62 alcoholics and 40 non-alcoholics. Three modifications are
introduced to improve the classification performance: i) increasing
the gamma band spectral range by increasing the pass-band width of
the used filter ii) the use of Multiple Signal Classification algorithm
to obtain the power of the dominant frequency in gamma band VEP
signals as features and iii) the use of the simple but effective knearest
neighbour classifier. To validate that these two modifications
do give improved performance, a 10-fold cross validation
classification (CVC) scheme is used. Repeat experiments of the
previously used methodology for the extended dataset are performed
here and improvement from 94.49% to 98.71% in maximum
averaged CVC accuracy is obtained using the modifications. This
latest results show that VEP based classification of alcoholics is
worth exploring further for system development.
Abstract: A lot of research has been done in the past decade in the field of audio content analysis for extracting various information from audio signal. One such significant information is the "perceived mood" or the "emotions" related to a music or audio clip. This information is extremely useful in applications like creating or adapting the play-list based on the mood of the listener. This information could also be helpful in better classification of the music database. In this paper we have presented a method to classify music not just based on the meta-data of the audio clip but also include the "mood" factor to help improve the music classification. We propose an automated and efficient way of classifying music samples based on the mood detection from the audio data. We in particular try to classify the music based on mood for Indian bollywood music. The proposed method tries to address the following problem statement: Genre information (usually part of the audio meta-data) alone does not help in better music classification. For example the acoustic version of the song "nothing else matters by Metallica" can be classified as melody music and thereby a person in relaxing or chill out mood might want to listen to this track. But more often than not this track is associated with metal / heavy rock genre and if a listener classified his play-list based on the genre information alone for his current mood, the user shall miss out on listening to this track. Currently methods exist to detect mood in western or similar kind of music. Our paper tries to solve the issue for Indian bollywood music from an Indian cultural context
Abstract: Rapid progress in audio compression technology has contributed to the explosive growth of music available in digital form today. In a reversal of ideas, this work makes use of a recently proposed efficient audio compression scheme to develop three important applications in the context of Music Information Retrieval (MIR) for the effective manipulation of large music databases, namely automatic music recommendation (AMR), digital rights management (DRM) and audio finger-printing for song identification. The performance of these three applications has been evaluated with respect to a database of songs collected from a diverse set of genres.
Abstract: Renewable energy systems are becoming a topic of
great interest and investment in the world. In recent years wind
power generation has experienced a very fast development in the
whole world. For planning and successful implementations of good
wind power plant projects, wind potential measurements are
required. In these projects, of great importance is the effective choice
of the micro location for wind potential measurements, installation of
the measurement station with the appropriate measuring equipment,
its maintenance and analysis of the gained data on wind potential
characteristics. In this paper, a wavelet transform has been applied to
analyze the wind speed data in the context of insight in the
characteristics of the wind and the selection of suitable locations that
could be the subject of a wind farm construction. This approach
shows that it can be a useful tool in investigation of wind potential.
Abstract: Precise frequency estimation methods for pulseshaped echoes are a prerequisite to determine the relative velocity between sensor and reflector. Signal frequencies are analysed using three different methods: Fourier Transform, Chirp ZTransform and the MUSIC algorithm. Simulations of echoes are performed varying both the noise level and the number of reflecting points. The superposition of echoes with a random initial phase is found to influence the precision of frequency estimation severely for FFT and MUSIC. The standard deviation of the frequency using FFT is larger than for MUSIC. However, MUSIC is more noise-sensitive. The distorting effect of superpositions is less pronounced in experimental data.
Abstract: Currently, many types of no-reversible compressed
sound source, represented by MP3 (MPEG Audio Layer-3) are
popular in the world and they are widely used to make the music file
size smaller. The sound data created in this way has less information as
compared to pre-compressed data. The objective of this study is by
analyzing EEG to determine if people can recognize such difference as
differences in sound. A measurement system that can measure and
analyze EEG when a subject listens to music were experimentally
developed. And ten subjects were studied with this system. In this
experiment, a WAVE formatted music data and a MP3 compressed
music data that is made from the WAVE formatted data were
prepared. Each subject was made to hear these music sources at the
same volume. From the results of this experiment, clear differences
were confirmed between two wound sources.
Abstract: Music Information Retrieval (MIR) and modern data mining techniques are applied to identify style markers in midi music for stylometric analysis and author attribution. Over 100 attributes are extracted from a library of 2830 songs then mined using supervised learning data mining techniques. Two attributes are identified that provide high informational gain. These attributes are then used as style markers to predict authorship. Using these style markers the authors are able to correctly distinguish songs written by the Beatles from those that were not with a precision and accuracy of over 98 per cent. The identification of these style markers as well as the architecture for this research provides a foundation for future research in musical stylometry.
Abstract: The purpose of this study was to investigate the effects of computer–based instructional designs, namely modality and redundancy principles on the attitude and learning of music theory among primary pupils of different Music Intelligence levels. The lesson of music theory was developed in three different modes, audio and image (AI), text with image (TI) and audio with image and text (AIT). The independent variables were the three modes of courseware. The moderator variable was music intelligence. The dependent variables were the post test score. ANOVA was used to determine the significant differences of the pretest scores among the three groups. Analyses of covariance (ANCOVA) and Post hoc were carried out to examine the main effects as well as the interaction effects of the independent variables on the dependent variables. High music intelligence pupils performed significantly better than low music intelligence pupils in all the three treatment modes. The AI mode was found to help pupils with low music intelligence significantly more than the TI and AIT modes.
Abstract: In this paper, a target signal detection method using
multiple signal classification (MUSIC) algorithm is proposed. The
MUSIC algorithm is a subspace-based direction of arrival (DOA)
estimation method. The algorithm detects the DOAs of multiple
sources using the inverse of the eigenvalue-weighted eigen spectra. To
apply the algorithm to target signal detection for GSC-based
beamforming, we utilize its spectral response for the target DOA in
noisy conditions. For evaluation of the algorithm, the performance of
the proposed target signal detection method is compared with that of
the normalized cross-correlation (NCC), the fixed beamforming, and
the power ratio method. Experimental results show that the proposed
algorithm significantly outperforms the conventional ones in receiver
operating characteristics(ROC) curves.
Abstract: In this paper, we propose effective system for digital music retrieval. We divided proposed system into Client and Server. Client part consists of pre-processing and Content-based feature extraction stages. In pre-processing stage, we minimized Time code Gap that is occurred among same music contents. As content-based feature, first-order differentiated MFCC were used. These presented approximately envelop of music feature sequences. Server part included Music Server and Music Matching stage. Extracted features from 1,000 digital music files were stored in Music Server. In Music Matching stage, we found retrieval result through similarity measure by DTW. In experiment, we used 450 queries. These were made by mixing different compression standards and sound qualities from 50 digital music files. Retrieval accurate indicated 97% and retrieval time was average 15ms in every single query. Out experiment proved that proposed system is effective in retrieve digital music and robust at various user environments of web.
Abstract: The way music is interpreted by the human brain is a very interesting topic, but also an intricate one. Although this domain has been studied for over a century, many gray areas remain in the understanding of music. Recent advances have enabled us to perform accurate measurements of the time taken by the human brain to interpret and assimilate a sound. Cognitive computing provides tools and development environments that facilitate human cognition simulation. ACT-R is a cognitive architecture which offers an environment for implementing human cognitive tasks. This project combines our understanding of the music interpretation by a human listener and the ACT-R cognitive architecture to build SINGER, a computerized simulation for listening and recalling songs. The results are similar to human experimental data. Simulation results also show how it is easier to remember short melodies than long melodies which require more trials to be recalled correctly.
Abstract: Music segmentation is a key issue in music information
retrieval (MIR) as it provides an insight into the
internal structure of a composition. Structural information about
a composition can improve several tasks related to MIR such
as searching and browsing large music collections, visualizing
musical structure, lyric alignment, and music summarization.
The authors of this paper present the MTSSM framework, a twolayer
framework for the multi-track segmentation of symbolic
music. The strength of this framework lies in the combination of
existing methods for local track segmentation and the application
of global structure information spanning via multiple tracks.
The first layer of the MTSSM uses various string matching
techniques to detect the best candidate segmentations for each
track of a multi-track composition independently. The second
layer combines all single track results and determines the best
segmentation for each track in respect to the global structure of
the composition.
Abstract: Thai and Vietnamese music had been influenced and inspired by the traditional Chinese music. Whereby the differences of the tuning systems as well as the music modes are obviously known . The research examined the character of musical instruments, songs and culture between Thai and Vietnamese. An analyzing of songs and modes and the study of tone vibration as well as timbre had been done accurately. This qualitative research is based on documentary and songs analysis, field study, interviews and focus group discussion of Thai and Vietnamese masters. The research aims are to examine the musical instruments and songs of both Thai and Vietnamese as well as the comparison of the sounding system between Thailand and Vietnam. The finding of the research has revealed that there are similarities in certain kinds of instruments but differences in the sound systems regarding songs and scale of Thailand and Vietnam. Both cultural musical instruments are diverse and synthetic combining native and foreign inspiring. An integral part of Vietnam has been highly impacted by Chinese musical convention. Korea, Mongolia and Japan music have also play an active and effectively influenced as their geographical related. Whereas Thailand has been influenced by Chinese and Indian traditional music. Both Thai and Vietnamese musical instruments can be divided into four groups: plucked strings, bowed strings, winds and percussion. Songs from both countries have their own characteristics. They are playing a role in touching people heart in ceremonies, social functions and an essential element of the native performing arts. The Vietnamese music melodies have been influenced by Chinese music and taken the same character as Chinese songs. Thai song has specific identity and variety showed in its unique melody. Pentatonic scales have effectively been used in composing Thai and Vietnamese songs, but in different implementing concept.
Abstract: The theatre-auditorium under investigation following
the highly reflective characteristics of materials used in it (marble,
painted wood, smooth plaster, etc), architectural and structural
features of the Protocol and its intended use (very multifunctional:
Auditorium, theatre, cinema, musicals, conference room) from the
analysis of the statement of fact made by the acoustic simulation
software Ramsete and supported by data obtained through a
campaign of acoustic measurements of the state of fact made on the
spot by a Fonomet Svantek model SVAN 957, appears to be
acoustically inadequate. After the completion of the 3D model
according to the specifications necessary software used forecast in
order to be recognized by him, have made three simulations, acoustic
simulation of the state of and acoustic simulation of two design
solutions.
Improved noise characteristics found in the first design solution,
compared to the state in fact consists therefore in lowering
Reverberation Time that you turn most desirable value, while the
Indicators of Clarity, the Baricentric Time, the Lateral Efficiency,
Ratio of Low Tmedia BR and defined the Speech Intelligibility
improved significantly. Improved noise characteristics found instead
in the second design solution, as compared to first design solution, is
finally mostly in a more uniform distribution of Leq and in lowering
Reverberation Time that you turn the optimum values. Indicators of
Clarity, and the Lateral Efficiency improve further but at the expense
of a value slightly worse than the BR. Slightly vary the remaining
indices.
Abstract: Recently many research has been conducted to
retrieve pertinent parameters and adequate models for automatic
music genre classification. In this paper, two measures based upon
information theory concepts are investigated for mapping the features
space to decision space. A Gaussian Mixture Model (GMM) is used
as a baseline and reference system. Various strategies are proposed
for training and testing sessions with matched or mismatched
conditions, long training and long testing, long training and short
testing. For all experiments, the file sections used for testing are
never been used during training. With matched conditions all
examined measures yield the best and similar scores (almost 100%).
With mismatched conditions, the proposed measures yield better
scores than the GMM baseline system, especially for the short testing
case. It is also observed that the average discrimination information
measure is most appropriate for music category classifications and on
the other hand the divergence measure is more suitable for music
subcategory classifications.
Abstract: The physical methods for RNA secondary structure prediction are time consuming and expensive, thus methods for computational prediction will be a proper alternative. Various algorithms have been used for RNA structure prediction including dynamic programming and metaheuristic algorithms. Musician's behaviorinspired harmony search is a recently developed metaheuristic algorithm which has been successful in a wide variety of complex optimization problems. This paper proposes a harmony search algorithm (HSRNAFold) to find RNA secondary structure with minimum free energy and similar to the native structure. HSRNAFold is compared with dynamic programming benchmark mfold and metaheuristic algorithms (RnaPredict, SetPSO and HelixPSO). The results showed that HSRNAFold is comparable to mfold and better than metaheuristics in finding the minimum free energies and the number of correct base pairs.
Abstract: A new analysis of perceptual speech enhancement is
presented. It focuses on the fact that if only noise above the masking
threshold is filtered, then noise below the masking threshold, but
above the absolute threshold of hearing, can become audible after the
masker filtering. This particular drawback of some perceptual filters,
hereafter called the maskee-to-audible-noise (MAN) phenomenon,
favours the emergence of isolated tonals that increase musical noise.
Two filtering techniques that avoid or correct the MAN phenomenon
are proposed to effectively suppress background noise without introducing
much distortion. Experimental results, including objective
and subjective measurements, show that these techniques improve
the enhanced speech quality and the gain they bring emphasizes the
importance of the MAN phenomenon.
Abstract: This paper presents a review on vision aided systems
and proposes an approach for visual rehabilitation using stereo vision
technology. The proposed system utilizes stereo vision, image
processing methodology and a sonification procedure to support
blind navigation. The developed system includes a wearable
computer, stereo cameras as vision sensor and stereo earphones, all
moulded in a helmet. The image of the scene infront of visually
handicapped is captured by the vision sensors. The captured images
are processed to enhance the important features in the scene in front,
for navigation assistance. The image processing is designed as model
of human vision by identifying the obstacles and their depth
information. The processed image is mapped on to musical stereo
sound for the blind-s understanding of the scene infront. The
developed method has been tested in the indoor and outdoor
environments and the proposed image processing methodology is
found to be effective for object identification.
Abstract: This paper presents a new hardware interface using a
microcontroller which processes audio music signals to standard
MIDI data. A technique for processing music signals by extracting
note parameters from music signals is described. An algorithm to
convert the voice samples for real-time processing without complex
calculations is proposed. A high frequency microcontroller as the
main processor is deployed to execute the outlined algorithm. The
MIDI data generated is transmitted using the EIA-232 protocol. The
analyses of data generated show the feasibility of using
microcontrollers for real-time MIDI generation hardware interface.
Abstract: Many Thai movies have been very popular
domestically and internationally. Some movies were box office hits
and receiving awards. However, there has not yet been research
about how Thai movies can sell in international markets
The objectives of the research were 1) To analyze the
characteristics of Thai movies that can sell to world audiences; 2) To
investigate the factors making Thai movies into foreign markets. Thai
film professionals were interviewed. Their ideas were analyzed to
find out what factors contributing to Thai movies widely seen in
worldwide markets. Nine foreign audiences were also interviewed to
reveal what characteristics of Thai movies would be well accepted by
the markets.
The results showed that major characteristics of Thai movies
proving successful worldwide were cultural and exotic Thai movies,
outstanding genres, well-known actors, music and songs. Factors
contributing to global market were marketing, qualities of Thai
movies, and financial support from the government.