Clustering of Musical Pieces through Complex Networks: an Assessment over Guitar Solos

Musical pieces can be modeled as complex networks. This fosters innovative ways to categorize music, paving the way towards novel applications in multimedia domains, such as music didactics, multimedia entertainment and digital music generation. Clustering these networks through their main metrics allows grouping similar musical tracks. To show the viability of the approach, we provide results on a dataset of guitar solos.Comment: to appear in IEEE Multimedia magazin

PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network

On the Modeling of Musical Solos as Complex Networks

Notes in a musical piece are building blocks employed in non-random ways to create melodies. It is the "interaction" among a limited amount of notes that allows constructing the variety of musical compositions that have been written in centuries and within different cultures. Networks are a modeling tool that is commonly employed to represent a set of entities interacting in some way. Thus, notes composing a melody can be seen as nodes of a network that are connected whenever these are played in sequence. The outcome of such a process results in a directed graph. By using complex network theory, some main metrics of musical graphs can be measured, which characterize the related musical pieces. In this paper, we define a framework to represent melodies as networks. Then, we provide an analysis on a set of guitar solos performed by main musicians. Results of this study indicate that the presented model can have an impact on audio and multimedia applications such as music classification, identification, e-learning, automatic music generation, multimedia entertainment.Comment: to appear in Information Science, Elsevier. Please cite the paper including such information. arXiv admin note: text overlap with arXiv:1603.0497

On the Complex Network Structure of Musical Pieces: Analysis of Some Use Cases from Different Music Genres

This paper focuses on the modeling of musical melodies as networks. Notes of a melody can be treated as nodes of a network. Connections are created whenever notes are played in sequence. We analyze some main tracks coming from different music genres, with melodies played using different musical instruments. We find out that the considered networks are, in general, scale free networks and exhibit the small world property. We measure the main metrics and assess whether these networks can be considered as formed by sub-communities. Outcomes confirm that peculiar features of the tracks can be extracted from this analysis methodology. This approach can have an impact in several multimedia applications such as music didactics, multimedia entertainment, and digital music generation.Comment: accepted to Multimedia Tools and Applications, Springe

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

Acoustic feature-based sentiment analysis of call center data

With the advancement of machine learning methods, audio sentiment analysis has become an active research area in recent years. For example, business organizations are interested in persuasion tactics from vocal cues and acoustic measures in speech. A typical approach is to find a set of acoustic features from audio data that can indicate or predict a customer's attitude, opinion, or emotion state. For audio signals, acoustic features have been widely used in many machine learning applications, such as music classification, language recognition, emotion recognition, and so on. For emotion recognition, previous work shows that pitch and speech rate features are important features. This thesis work focuses on determining sentiment from call center audio records, each containing a conversation between a sales representative and a customer. The sentiment of an audio record is considered positive if the conversation ended with an appointment being made, and is negative otherwise. In this project, a data processing and machine learning pipeline for this problem has been developed. It consists of three major steps: 1) an audio record is split into segments by speaker turns; 2) acoustic features are extracted from each segment; and 3) classification models are trained on the acoustic features to predict sentiment. Different set of features have been used and different machine learning methods, including classical machine learning algorithms and deep neural networks, have been implemented in the pipeline. In our deep neural network method, the feature vectors of audio segments are stacked in temporal order into a feature matrix, which is fed into deep convolution neural networks as input. Experimental results based on real data shows that acoustic features, such as Mel frequency cepstral coefficients, timbre and Chroma features, are good indicators for sentiment. Temporal information in an audio record can be captured by deep convolutional neural networks for improved prediction accuracy

Self-supervised learning in natural language processing

Most natural language processing (NLP) learning algorithms require labeled data. While this is given for a select number of (mostly English) tasks, the availability of labeled data is sparse or non-existent for the vast majority of use-cases. To alleviate this, unsupervised learning and a wide array of data augmentation techniques have been developed (Hedderich et al., 2021a). However, unsupervised learning often requires massive amounts of unlabeled data and also fails to perform in difficult (low-resource) data settings, i.e., if there is an increased distance between the source and target data distributions (Kim et al., 2020). This distributional distance can be the case if there is a domain drift or large linguistic distance between the source and target data. Unsupervised learning in itself does not exploit the highly informative (labeled) supervisory signals hidden in unlabeled data. In this dissertation, we show that by combining the right unsupervised auxiliary task (e.g., sentence pair extraction) with an appropriate primary task (e.g., machine translation), self-supervised learning can exploit these hidden supervisory signals more efficiently than purely unsupervised approaches, while functioning on less labeled data than supervised approaches. Our self-supervised learning approach can be used to learn NLP tasks in an efficient manner, even when the amount of training data is sparse or the data comes with strong differences in its underlying distribution, e.g., stemming from unrelated languages. For our general approach, we applied unsupervised learning as an auxiliary task to learn a supervised primary task. Concretely, we have focused on the auxiliary task of sentence pair extraction for sequence-to-sequence primary tasks (i.e., machine translation and style transfer) as well as language modeling, clustering, subspace learning and knowledge integration for primary classification tasks (i.e., hate speech detection and sentiment analysis). For sequence-to-sequence tasks, we show that self-supervised neural machine translation (NMT) achieves competitive results on high-resource language pairs in comparison to unsupervised NMT while requiring less data. Further combining self-supervised NMT with unsupervised NMT-inspired augmentation techniques makes the learning of low-resource (similar, distant and unrelated) language pairs possible. Further, using our self-supervised approach, we show how style transfer can be learned without the need for parallel data, generating stylistic rephrasings of highest overall performance on all tested tasks. For sequence-to-label tasks, we underline the benefit of auxiliary task-based augmentation over primary task augmentation. An auxiliary task that showed to be especially beneficial to the primary task performance was subspace learning, which led to impressive gains in (cross-lingual) zero-shot classification performance on similar or distant target tasks, also on similar, distant and unrelated languages.Die meisten Lernalgorithmen der Computerlingistik (CL) benötigen gelabelte Daten. Diese sind zwar für eine Auswahl an (hautpsächlich Englischen) Aufgaben verfügbar, für den Großteil aller Anwendungsfälle sind gelabelte Daten jedoch nur spärrlich bis gar nicht vorhanden. Um dem gegenzusteuern, wurde eine große Auswahl an Techniken entwickelt, welche sich das unüberwachte Lernen oder Datenaugmentierung zu eigen machen (Hedderich et al., 2021a). Unüberwachtes Lernen benötigt jedoch massive Mengen an ungelabelten Daten und versagt, wenn es mit schwierigen (resourcenarmen) Datensituationen konfrontiert wird, d.h. wenn eine größere Distanz zwischen der Quellen- und Zieldatendistributionen vorhanden ist (Kim et al., 2020). Eine distributionelle Distanz kann zum Beispiel der Fall sein, wenn ein Domänenunterschied oder eine größere sprachliche Distanz zwischen der Quellenund Zieldaten besteht. Unüberwachtes Lernen selbst nutzt die hochinformativen (gelabelten) Überwachungssignale, welche sich in ungelabelte Daten verstecken, nicht aus. In dieser Dissertation zeigen wir, dass selbstüberwachtes Lernen, durch die Kombination der richtigen unüberwachten Hilfsaufgabe (z.B. Satzpaarextraktion) mit einer passenden Hauptaufgabe (z.B. maschinelle Übersetzung), diese versteckten Überwachsungssignale effizienter ausnutzen kann als pure unüberwachte Lernalgorithmen, und dabei auch noch weniger gelabelte Daten benötigen als überwachte Lernalgorithmen. Unser selbstüberwachter Lernansatz erlaubt es uns, CL Aufgaben effizient zu lernen, selbst wenn die Trainingsdatenmenge spärrlich ist oder die Daten mit starken distributionellen Differenzen einher gehen, z.B. weil die Daten von zwei nicht verwandten Sprachen stammen. Im Generellen haben wir unüberwachtes Lernen als Hilfsaufgabe angewandt um eine überwachte Hauptaufgabe zu erlernen. Konkret haben wir uns auf Satzpaarextraktion als Hilfsaufgabe für Sequenz-zu-Sequenz Hauptaufgaben (z.B. maschinelle Übersetzung und Stilübertragung) konzentriert sowohl als auch Sprachmodelierung, Clustern, Teilraumlernen und Wissensintegration zum erlernen von Klassifikationsaufgaben (z.B. Hassredenidentifikation und Sentimentanalyse). Für Sequenz-zu-Sequenz Aufgaben zeigen wir, dass selbstüberwachte maschinelle Übersetzung (MÜ) im Vergleich zur unüberwachten MÜ wettbewerbsfähige Ergebnisse auf resourcenreichen Sprachpaaren erreicht und währenddessen weniger Daten zum Lernen benötigt. Wenn selbstüberwachte MÜ mit Augmentationstechniken, inspiriert durch unüberwachte MÜ, kombiniert wird, wird auch das Lernen von resourcenarmen (ähnlichen, entfernt verwandten und nicht verwandten) Sprachpaaren möglich. Außerdem zeigen wir, wie unser selbsüberwachter Lernansatz es ermöglicht Stilübertragung ohne parallele Daten zu erlernen und dabei stylistische Umformulierungen von höchster Qualität auf allen geprüften Aufgaben zu erlangen. Für Sequenz-zu-Label Aufgaben unterstreichen wir den Vorteil, welchen hilfsaufgabenseitige Augmentierung über hauptaufgabenseitige Augmentierung hat. Eine Hilfsaufgabe welche sich als besonders hilfreich für die Qualität der Hauptaufgabe herausstellte ist das Teilraumlernen, welches zu beeindruckenden Leistungssteigerungen für (sprachübergreifende) zero-shot Klassifikation ähnlicher und entfernter Zielaufgaben (auch für ähnliche, entfernt verwandte und nicht verwandte Sprachen) führt

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

Internet-based communication systems have become an increasing tool for spreading misinformation and propaganda. Even though there exist mechanisms that are able to track unwarranted information and messages, users made up different ways to avoid their scrutiny and detection. An example is represented by the mixed-code language, that is text written in an unconventional form by combining different languages, symbols, scripts and shapes. It aims to make more difficult the detection of specific content, due to its custom and ever changing appearance, by using special characters to substitute for alphabet letters. Indeed, such substitute combinations of symbols, which tries to resemble the shape of the intended alphabet’s letter, makes it still intuitively readable to humans, however nonsensical to machines. In this context, the paper explores the possibility of identifying propaganda in such mixed-code texts over the Internet, centred on a machine learning based approach. In particular, an algorithm in combination with a deep learning models for character identification is proposed in order to detect and analyse whether an element contains propaganda related content. The overall approach is presented, the results gathered from its experimentation are discussed and the achieved performances are compared with the related works