Automatic Ontology Generation for Musical Instruments Based on Audio Analysis

• Published in: IEEE transactions on audio, speech, and language processing speech, and language processing, 2013-10, Vol.21 (10), p.2207-2220
• Main Authors: Kolozali, Sefki, Barthet, Mathieu, Fazekas, Gyorgy, Sandler, Mark
• Format: Article
• Language: English
• Subjects: Analysis of variance; Applied sciences; Artificial intelligence; Automatic ontology generation; Automation; Computer science; control theory; systems; Connectionism. Neural networks; Construction; Context; Exact sciences and technology; Feature extraction; Information, signal and communications theory; instrument recognition; Instruments; Miscellaneous; Musical instruments; Ontologies; Ontology; Recognition; Recording; Recreational goods; Semantic Web; semantic web intelligence; Semantics; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Software; Speech processing; Support vector machines; Telecommunications and information theory; Variance analysis; Vectors
• ISSN: 1558-7916; 2329-9290; 1558-7924; 2329-9304
• DOI: 10.1109/TASL.2013.2263801

In this paper we present a novel hybrid system that involves a formal method of automatic ontology generation for web-based audio signal processing applications. An ontology is seen as a knowledge management structure that represents domain knowledge in a machine interpretable format. It describes concepts and relationships within a particular domain, in our case, the domain of musical instruments. However, the different tasks of ontology engineering including manual annotation, hierarchical structuring and organization of data can be laborious and challenging. For these reasons, we investigate how the process of creating ontologies can be made less dependent on human supervision by exploring concept analysis techniques in a Semantic Web environment. In this study, various musical instruments, from wind to string families, are classified using timbre features extracted from audio. To obtain models of the analysed instrument recordings, we use K-means clustering to determine an optimised codebook of Line Spectral Frequencies (LSFs), or Mel-frequency Cepstral Coefficients (MFCCs). Two classification techniques based on Multi-Layer Perceptron (MLP) neural network and Support Vector Machines (SVM) were tested. Then, Formal Concept Analysis (FCA) is used to automatically build the hierarchical structure of musical instrument ontologies. Finally, the generated ontologies are expressed using the Ontology Web Language (OWL). System performance was evaluated under natural recording conditions using databases of isolated notes and melodic phrases. Analysis of Variance (ANOVA) were conducted with the feature and classifier attributes as independent variables and the musical instrument recognition F-measure as dependent variable. Based on these statistical analyses, a detailed comparison between musical instrument recognition models is made to investigate their effects on the automatic ontology generation system. The proposed system is general and also applicable to other research fields that are related to ontologies and the Semantic Web.