Analyzing and enhancing music mood classification : an empirical study

In the computer age, managing large data repositories is one of the common challenges, especially for music data. Categorizing, manipulating, and refining music tracks are among the most complex tasks in Music Information Retrieval (MIR). Classification is one of the core functions in MIR, which classifies music data from different perspectives, from genre to instrument to mood. The primary focus of this study is on music mood classification. Mood is a subjective phenomenon in MIR, which involves different considerations, such as psychology, musicology, culture, and social behavior. One of the most significant prerequisitions in music mood classification is answering these questions: what combination of acoustic features helps us to improve the accuracy of classification in this area? What type of classifiers is appropriate in music mood classification? How can we increase the accuracy of music mood classification using several classifiers? To find the answers to these questions, we empirically explored different acoustic features and classification schemes on the mood classification in music data. Also, we found the two approaches to use several classifiers simultaneously to classify music tracks using mood labels automatically. These methods contain two voting procedures; namely, Plurality Voting and Borda Count. These approaches are categorized into ensemble techniques, which combine a group of classifiers to reach better accuracy. The proposed ensemble methods are implemented and verified through empirical experiments. The results of the experiments have shown that these proposed approaches could improve the accuracy of music mood classification

Learning Contextualized Semantics from Co-occurring Terms via a Siamese Architecture

One of the biggest challenges in Multimedia information retrieval and understanding is to bridge the semantic gap by properly modeling concept semantics in context. The presence of out of vocabulary (OOV) concepts exacerbates this difficulty. To address the semantic gap issues, we formulate a problem on learning contextualized semantics from descriptive terms and propose a novel Siamese architecture to model the contextualized semantics from descriptive terms. By means of pattern aggregation and probabilistic topic models, our Siamese architecture captures contextualized semantics from the co-occurring descriptive terms via unsupervised learning, which leads to a concept embedding space of the terms in context. Furthermore, the co-occurring OOV concepts can be easily represented in the learnt concept embedding space. The main properties of the concept embedding space are demonstrated via visualization. Using various settings in semantic priming, we have carried out a thorough evaluation by comparing our approach to a number of state-of-the-art methods on six annotation corpora in different domains, i.e., MagTag5K, CAL500 and Million Song Dataset in the music domain as well as Corel5K, LabelMe and SUNDatabase in the image domain. Experimental results on semantic priming suggest that our approach outperforms those state-of-the-art methods considerably in various aspects

Transfer learning: bridging the gap between deep learning and domain-specific text mining

Inspired by the success of deep learning techniques in Natural Language Processing (NLP), this dissertation tackles the domain-specific text mining problems for which the generic deep learning approaches would fail. More specifically, the domain-specific problems are: (1) success prediction in crowdfunding, (2) variants identification in biomedical literature, and (3) text data augmentation for domains with low-resources. In the first part, transfer learning in a multimodal perspective is utilized to facilitate solving the project success prediction on the crowdfunding application. Even though the information in a project profile can be of different modalities such as text, images, and metadata, most existing prediction approaches leverage only the text modality. It is promising to utilize the visual images in project profiles to find out how images could contribute to the success prediction. An advanced neural network scheme is designed and evaluated combining information learned from different modalities for project success prediction. In the second part, transfer learning is combined with deep learning techniques to solve genomic variants Named Entity Recognition (NER) problems in biomedical literature. Most of the advanced generic NER algorithms can fail due to the restricted training corpus. However, those generic deep learning algorithms are capable of learning from a canonical corpus, without any effort on feature engineering. This work aims to build an end-to-end deep learning approach to transfer the domain-specific knowledge to those advanced generic NER algorithms, addressing the challenges in low-resource training and requiring neither hand-crafted features nor post-processing rules. For the last part, transfer learning with knowledge distillation and active learning are utilized to solve text augmentation for domains with low-resources. Most of the recent text augmentation methods heavily rely on large external resources. This work is dedicates to solving the text augmentation problem adaptively and consistently with minimal resources for token-level tasks like NER. The solution can also assure the reliability of machine labels for noisy data and can enhance training consistency with noisy labels. All the works are evaluated on different domain-specific benchmarks, respectively. Experimental results demonstrate the effectiveness of those proposed methods. The advantages also indicate promising potential for transfer learning in domain-specific applications

Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection

Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks (CNN) are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks (RNNs) are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in various sound recognition tasks. We combine these two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it on a polyphonic sound event detection task. We compare the performance of the proposed CRNN method with CNN, RNN, and other established methods, and observe a considerable improvement for four different datasets consisting of everyday sound events.Comment: Accepted for IEEE Transactions on Audio, Speech and Language Processing, Special Issue on Sound Scene and Event Analysi

Exploratory Browsing

In recent years the digital media has influenced many areas of our life. The transition from analogue to digital has substantially changed our ways of dealing with media collections. Today‟s interfaces for managing digital media mainly offer fixed linear models corresponding to the underlying technical concepts (folders, events, albums, etc.), or the metaphors borrowed from the analogue counterparts (e.g., stacks, film rolls). However, people‟s mental interpretations of their media collections often go beyond the scope of linear scan. Besides explicit search with specific goals, current interfaces can not sufficiently support the explorative and often non-linear behavior. This dissertation presents an exploration of interface design to enhance the browsing experience with media collections. The main outcome of this thesis is a new model of Exploratory Browsing to guide the design of interfaces to support the full range of browsing activities, especially the Exploratory Browsing. We define Exploratory Browsing as the behavior when the user is uncertain about her or his targets and needs to discover areas of interest (exploratory), in which she or he can explore in detail and possibly find some acceptable items (browsing). According to the browsing objectives, we group browsing activities into three categories: Search Browsing, General Purpose Browsing and Serendipitous Browsing. In the context of this thesis, Exploratory Browsing refers to the latter two browsing activities, which goes beyond explicit search with specific objectives. We systematically explore the design space of interfaces to support the Exploratory Browsing experience. Applying the methodology of User-Centered Design, we develop eight prototypes, covering two main usage contexts of browsing with personal collections and in online communities. The main studied media types are photographs and music. The main contribution of this thesis lies in deepening the understanding of how people‟s exploratory behavior has an impact on the interface design. This thesis contributes to the field of interface design for media collections in several aspects. With the goal to inform the interface design to support the Exploratory Browsing experience with media collections, we present a model of Exploratory Browsing, covering the full range of exploratory activities around media collections. We investigate this model in different usage contexts and develop eight prototypes. The substantial implications gathered during the development and evaluation of these prototypes inform the further refinement of our model: We uncover the underlying transitional relations between browsing activities and discover several stimulators to encourage a fluid and effective activity transition. Based on this model, we propose a catalogue of general interface characteristics, and employ this catalogue as criteria to analyze the effectiveness of our prototypes. We also present several general suggestions for designing interfaces for media collections

The Taming of the Shrew - non-standard text processing in the Digital Humanities

Natural language processing (NLP) has focused on the automatic processing of newspaper texts for many years. With the growing importance of text analysis in various areas such as spoken language understanding, social media processing and the interpretation of text material from the humanities, techniques and methodologies have to be reviewed and redefined since so called non-standard texts pose challenges on the lexical and syntactic level especially for machine-learning-based approaches. Automatic processing tools developed on the basis of newspaper texts show a decreased performance for texts with divergent characteristics. Digital Humanities (DH) as a field that has risen to prominence in the last decades, holds a variety of examples for this kind of texts. Thus, the computational analysis of the relationships of Shakespeare’s dramatic characters requires the adjustment of processing tools to English texts from the 16th-century in dramatic form. Likewise, the investigation of narrative perspective in Goethe’s ballads calls for methods that can handle German verse from the 18th century. In this dissertation, we put forward a methodology for NLP in a DH environment. We investigate how an interdisciplinary context in combination with specific goals within projects influences the general NLP approach. We suggest thoughtful collaboration and increased attention to the easy applicability of resulting tools as a solution for differences in the store of knowledge between project partners. Projects in DH are not only constituted by the automatic processing of texts but are usually framed by the investigation of a research question from the humanities. As a consequence, time limitations complicate the successful implementation of analysis techniques especially since the diversity of texts impairs the transferability and reusability of tools beyond a specific project. We answer to this with modular and thus easily adjustable project workflows and system architectures. Several instances serve as examples for our methodology on different levels. We discuss modular architectures that balance time-saving solutions and problem-specific implementations on the example of automatic postcorrection of the output text from an optical character recognition system. We address the problem of data diversity and low resource situations by investigating different approaches towards non-standard text processing. We examine two main techniques: text normalization and tool adjustment. Text normalization aims at the transformation of non-standard text in order to assimilate it to the standard whereas tool adjustment concentrates on the contrary direction of enabling tools to successfully handle a specific kind of text. We focus on the task of part-of-speech tagging to illustrate various approaches toward the processing of historical texts as an instance for non-standard texts. We discuss how the level of deviation from a standard form influences the performance of different methods. Our approaches shed light on the importance of data quality and quantity and emphasize the indispensability of annotations for effective machine learning. In addition, we highlight the advantages of problem-driven approaches where the purpose of a tool is clearly formulated through the research question. Another significant finding to emerge from this work is a summary of the experiences and increased knowledge through collaborative projects between computer scientists and humanists. We reflect on various aspects of the elaboration and formalization of research questions in the DH and assess the limitations and possibilities of the computational modeling of humanistic research questions. An emphasis is placed on the interplay of expert knowledge with respect to a subject of investigation and the implementation of tools for that purpose and the thereof resulting advantages such as the targeted improvement of digital methods through purposeful manual correction and error analysis. We show obstacles and chances and give prospects and directions for future development in this realm of interdisciplinary research