A cartesian ensemble of feature subspace classifiers for music categorization

We present a cartesian ensemble classification system that is based on the principle of late fusion and feature subspaces. These feature subspaces describe different aspects of the same data set. The framework is built on the Weka machine learning toolkit and able to combine arbitrary feature sets and learning schemes. In our scenario, we use it for the ensemble classification of multiple feature sets from the audio and symbolic domains. We present an extensive set of experiments in the context of music genre classification, based on numerous Music IR benchmark datasets, and evaluate a set of combination/voting rules. The results show that the approach is superior to the best choice of a single algorithm on a single feature set. Moreover, it also releases the user from making this choice explicitly.International Society for Music Information Retrieva

Musical Instrument Classification via Low-Dimensional Feature Vectors

Music is a mysterious language that conveys feeling and thoughts via different tones and timbre. For better understanding of timbre in music, we chose music data of 6 representative instruments, analysed their timbre features and classified them. Instead of the current trend of Neural Network for black-box classification, our project is based on a combination of MFCC and LPC, and augmented with a 6-dimensional feature vector designed by ourselves from observation and attempts. In our white-box model, we observed significant patterns of sound that distinguish different timbres, and discovered some connection between objective data and subjective senses. With a totally 32-dimensional feature vector and a naive all-pairs SVM, we achieved improved classification accuracy compared to a single tool. We also attempted to analyze music pieces downloaded from the Internet, found out different performance on different instruments, explored the reasons and suggested possible ways to improve the performance

Optimizing Feature Extraction for Symbolic Music

This paper presents a comprehensive investigation of existing feature extraction tools for symbolic music and contrasts their performance to determine the set of features that best characterizes the musical style of a given music score. In this regard, we propose a novel feature extraction tool, named musif, and evaluate its efficacy on various repertoires and file formats, including MIDI, MusicXML, and **kern. Musif approximates existing tools such as jSymbolic and music21 in terms of computational efficiency while attempting to enhance the usability for custom feature development. The proposed tool also enhances classification accuracy when combined with other sets of features. We demonstrate the contribution of each set of features and the computational resources they require. Our findings indicate that the optimal tool for feature extraction is a combination of the best features from each tool rather than those of a single one. To facilitate future research in music information retrieval, we release the source code of the tool and benchmarks.Comment: Published at ISMIR 202

Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches

Musical genre classification is put into context byexplaining about the structures in music and how it is analyzedand perceived by humans. The increase of the music databaseson the personal collection and the Internet has brought a greatdemand for music information retrieval, and especiallyautomatic musical genre classification. In this research wefocused on combining information from the audio signal thandifferent sources. This paper presents a comprehensivemachine learning approach to the problem of automaticmusical genre classification using the audio signal. Theproposed approach uses two feature vectors, Support vectormachine classifier with polynomial kernel function andmachine learning algorithms. More specifically, two featuresets for representing frequency domain, temporal domain,cepstral domain and modulation frequency domain audiofeatures are proposed. Using our proposed features SVM act asstrong base learner in AdaBoost, so its performance of theSVM classifier cannot improve using boosting method. Thefinal genre classification is obtained from the set of individualresults according to a weighting combination late fusionmethod and it outperformed the trained fusion method. Musicgenre classification accuracy of 78% and 81% is reported onthe GTZAN dataset over the ten musical genres and theISMIR2004 genre dataset over the six musical genres,respectively. We observed higher classification accuracies withthe ensembles, than with the individual classifiers andimprovements of the performances on the GTZAN andISMIR2004 genre datasets are three percent on average. Thisensemble approach show that it is possible to improve theclassification accuracy by using different types of domainbased audio features

Music emotion recognition: a multimodal machine learning approach

Music emotion recognition (MER) is an emerging domain of the Music Information Retrieval (MIR) scientific community, and besides, music searches through emotions are one of the major selection preferred by web users. As the world goes to digital, the musical contents in online databases, such as Last.fm have expanded exponentially, which require substantial manual efforts for managing them and also keeping them updated. Therefore, the demand for innovative and adaptable search mechanisms, which can be personalized according to users’ emotional state, has gained increasing consideration in recent years. This thesis concentrates on addressing music emotion recognition problem by presenting several classification models, which were fed by textual features, as well as audio attributes extracted from the music. In this study, we build both supervised and semisupervised classification designs under four research experiments, that addresses the emotional role of audio features, such as tempo, acousticness, and energy, and also the impact of textual features extracted by two different approaches, which are TF-IDF and Word2Vec. Furthermore, we proposed a multi-modal approach by using a combined feature-set consisting of the features from the audio content, as well as from context-aware data. For this purpose, we generated a ground truth dataset containing over 1500 labeled song lyrics and also unlabeled big data, which stands for more than 2.5 million Turkish documents, for achieving to generate an accurate automatic emotion classification system. The analytical models were conducted by adopting several algorithms on the crossvalidated data by using Python. As a conclusion of the experiments, the best-attained performance was 44.2% when employing only audio features, whereas, with the usage of textual features, better performances were observed with 46.3% and 51.3% accuracy scores considering supervised and semi-supervised learning paradigms, respectively. As of last, even though we created a comprehensive feature set with the combination of audio and textual features, this approach did not display any significant improvement for classification performanc

NMF-based temporal feature integration for acoustic event classification

Proceedings of: 14th Annual Conference of the International Speech Communication Association. Lyon, France, 25-29 August 2013.In this paper, we propose a new front-end for Acoustic Event Classification tasks (AEC) based on the combination of the temporal feature integration technique called Filter Bank Coefficients (FC) and Non-Negative Matrix Factorization (NMF). FC aims to capture the dynamic structure in the short-term features by means of the summarization of the periodogram of each short-term feature dimension in several frequency bands using a predefined filter bank. As the commonly used filter bank has been devised for other tasks (such as music genre classification), it can be suboptimal for AEC. In order to overcome this drawback, we propose an unsupervised method based on NMF for learning the filters which collect the most relevant temporal information in the short-time features for AEC. The experiments show that the features obtained with this method achieve significant improvements in the classification performance of a Support Vector Machine (SVM) based AEC system in comparison with the baseline FC features.This work has been partially supported by the Spanish Government grants TSI-020110-2009-103, IPT-120000-2010-24 and TEC2011-26807Publicad

Automatic Genre Classification of Latin Music Using Ensemble of Classifiers

This paper presents a novel approach to the task of automatic music genre classification which is based on ensemble learning. Feature vectors are extracted from three 30-second music segments from the beginning, middle and end of each music piece. Individual classifiers are trained to account for each music segment. During classification, the output provided by each classifier is combined with the aim of improving music genre classification accuracy. Experiments carried out on a dataset containing 600 music samples from two Latin genres (Tango and Salsa) have shown that for the task of automatic music genre classification, the features extracted from the middle and end music segments provide better results than using the beginning music segment. Furthermore, the proposed ensemble method provides better accuracy than using single classifiers and any individual segment

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

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

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates