Leadership capability of team leaders in construction industry

This research was conducted to identify the important leadership capabilities for Malaysia construction industry team leaders. This research used exploratory sequential mix-method research design which is qualitative followed by quantitative research method. In the qualitative phase, semi-structured in-depth interview was selected and purposive sampling was employed in selecting 15 research participants involving team leaders and Human Resource Managers. Qualitative data was analysed using content and thematic analyses. Quantitative data was collected using survey questionnaire involving 171 randomly selected team leaders as respondents. The data was analyzed using descriptive and inferential statistics consisting of t-test, One-way Analysis of Variance (ANOVA), Pearson Correlation, Multiple Regression and Structured Equation Modeling (SEM). This study found that personal integrity, working within industry, customer focus and quality, communication and interpersonal skill, developing and empowering people and working as a team were needed leadership capabilities among construction industry team leaders. The research was also able to prove that leadership skill is a key element to develop leadership capability. A framework was developed based on the results of this study, which can be used as a guide by employers and relevant agencies in enhancing leadership capability of Malaysia construction industry team leade

An automatic annotation system for audio data containing music

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 51-53).by Janet Marques.S.B.and M.Eng

Musical instrument classification using non-negative matrix factorization algorithms and subset feature selection

In this paper, a class of algorithms for automatic classification of individual musical instrument sounds is presented. Several perceptual features used in sound classification applications as well as MPEG-7 descriptors were measured for 300 sound recordings consisting of 6 different musical instrument classes. Subsets of the feature set are selected using branchand-bound search, obtaining the most suitable features for classification. A class of classifiers is developed based on the non-negative matrix factorization (NMF). The standard NMF method is examined as well as its modifications: the local, the sparse, and the discriminant NMF. The experimental results compare feature subsets of varying sizes alongside the various NMF algorithms. It has been found that a subset containing the mean and the variance of the first mel-frequency cepstral coefficient and the AudioSpectrumFlatness descriptor along with the means of the AudioSpectrumEnvelope and the AudioSpectrumSpread descriptors when is fed to a standard NMF classifier yields an accuracy exceeding 95%

Listening to features

This work explores nonparametric methods which aim at synthesizing audio from low-dimensionnal acoustic features typically used in MIR frameworks. Several issues prevent this task to be straightforwardly achieved. Such features are designed for analysis and not for synthesis, thus favoring high-level description over easily inverted acoustic representation. Whereas some previous studies already considered the problem of synthesizing audio from features such as Mel-Frequency Cepstral Coefficients, they mainly relied on the explicit formula used to compute those features in order to inverse them. Here, we instead adopt a simple blind approach, where arbitrary sets of features can be used during synthesis and where reconstruction is exemplar-based. After testing the approach on a speech synthesis from well known features problem, we apply it to the more complex task of inverting songs from the Million Song Dataset. What makes this task harder is twofold. First, that features are irregularly spaced in the temporal domain according to an onset-based segmentation. Second the exact method used to compute these features is unknown, although the features for new audio can be computed using their API as a black-box. In this paper, we detail these difficulties and present a framework to nonetheless attempting such synthesis by concatenating audio samples from a training dataset, whose features have been computed beforehand. Samples are selected at the segment level, in the feature space with a simple nearest neighbor search. Additionnal constraints can then be defined to enhance the synthesis pertinence. Preliminary experiments are presented using RWC and GTZAN audio datasets to synthesize tracks from the Million Song Dataset.Comment: Technical Repor

Classification of Musical Instruments sounds by Using MFCC and Timbral Audio Descriptors

Identification of the musical instrument from a music piece is becoming area of interest for researchers in recent years. The system for identification of musical instrument from monophonic audio recording is basically performs three tasks: i) Pre-processing of inputted music signal; ii) Feature extraction from the music signal; iii) Classification. There are many methods to extract the audio features from an audio recording like Mel-frequency Cepstral Coefficients (MFCC), Linear Predictive Codes (LPC), Linear Predictive Cepstral Coefficients (LPCC), Perceptual Linear Predictive Coefficients (PLP), etc. The paper presents an idea to identify musical instruments from monophonic audio recordings by extracting MFCC features and timbre related audio descriptors. Further, three classifiers K-Nearest Neighbors (K-NN), Support Vector Machine (SVM) and Binary Tree Classifier (BT) are used to identify the musical instrument name by using feature vector generated in feature extraction process. The analysis is made by studying results obtained by all possible combinations of feature extraction methods and classifiers. Percentage accuracies for each combination are calculated to find out which combinations can give better musical instrument identification results. The system gives higher percentage accuracies of 90.00%, 77.00% and 75.33% for five, ten and fifteen musical instruments respectively if MFCC is used with K-NN classifier and for Timbral ADs higher percentage accuracies of 88.00%, 84.00% and 73.33% are obtained for five, ten and fifteen musical instruments respectively if BT classifier is used. DOI: 10.17762/ijritcc2321-8169.150713