Human Information Interaction

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Background and Aim: The current research aims at prototyping query-by-humming music information retrieval systems for smart phones.
Methods: This multi-method research follows simulation technique from mixed models of the operations research methodology, and the documentary research method, simultaneously. Two chromatic harmonica albums comprised the research population. To achieve the purpose of research, 24 homophonic tracks were splitted by using Helium Audio Splitter software. The splits were processed by Sonic Visualiser software; and 168 XML documents were produced. On the other hand, 4 research participants hummed and recorded splits. Hummed tracks were converted by using AMR to MP3 Converter software, processed by Sonic Visualiser, and resulted in 672 XML documents. MATLAB software was learned by the first group of XML documents (168), and then, processed the second group of XML documents (672) for providing desirable outputs. Outputs were compared by using Image Comparer software.
Results: Findings indicated a high degree of similarity (99 %) between outputs of two groups of XML documents. It has also been found that the gender and the music skill do not have any impact on the results.
Conclusion: It could be acknowledged that designing query-by-humming systems based on converting audio to XML documents, and document matching, is an appropriate strategy towards developing music retrieval applications for smart phones.

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Background and Aim: Automatic identification and classification of the Iranian traditional music scales (Dastgāh) and melody models (Gusheh) has attracted the attention of the researchers for more than a decade. The current research aims to review conducted researches on this area and consider its different approached and obstacles.
Method: The research approach is content analysis and data collection method is based on the documentary-library study.
Results: Findings indicated that the main obstacles and reasons for the inefficiency of this area researches are due to performing the researches in parallel and individually, lack of a coherent dataset for Iranian traditional music and also, lack of researcher’s knowledge of the theoretical foundations of Iranian traditional music.
Conclusion: It could be considered two main approaches for researches in automatic identification of the Iranian traditional music. The major researches has been published until now, is conducted based on Macro approach, which merely considers the scales of five Dastgāhs in distinguishing them from each other. Since Dastgāhs division does not have enough authenticity and there is no consensus on the number of Dastgāhs and their boundaries among the Iranian music theorists, Micro approach has been suggested for future researches, which tries to identify short melody models (Gushehs) based of melody sequences of representative phrase of the Gusheh. 

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Purpose: This study investigates the automatic keyword extraction from the table of contents of Persian e-books in the field of science using LDA topic modeling, evaluating their similarity with the golden standard, and users' viewpoints of the model keywords.
Methodology: This is mixed text-mining research in which LDA topic modeling is used to extract keywords from the table of contents of scientific e-books. The evaluation of the used approach has been done by two methods of cosine similarity computing and qualitative evaluation by users.
Findings: Table of contents are medium-length texts with a trimmed mean of 260.02 words, about 20% of which are stop-words. The cosine similarity between the golden standard keywords and the output keywords is 0.0932 thus very low. The full agreement of users showed that the extracted keywords with the LDA topic model represent the subject field of the whole corpus, but the golden standard keywords, the keywords extracted using the LDA topic model in sub-domains of the corpus, and the keywords extracted from the whole corpus were respectively successful in subject describing of each document.
Conclusion: The keywords extracted using the LDA topic model can be used in unspecified and unknown collections to extract hidden thematic content of the whole collection, but not to accurately relate each topic to each document in large and heterogeneous themes. In collections of texts in one subject field, such as mathematics or physics, etc., with less diversity and more uniformity in terms of the words used in them, more coherent and relevant keywords are obtained, but in these cases, the control of the relevance of keywords to each document is required. In formal subject analysis procedures and processes of individual documents, this approach can be used as a keyword suggestion system for indexing and analytical workforce.