MoodyLyrics: A Sentiment Annotated Lyrics Dataset

Music emotion recognition and recommendations today are changing the way people find and listen to their preferred musical tracks. Emotion recognition of songs is mostly based on feature extraction and learning from available datasets. In this work we take a different approach utilizing content words of lyrics and their valence and arousal norms in affect lexicons only. We use this method to annotate each song with one of the four emotion categories of Russell's model, and also to construct MoodyLyrics, a large dataset of lyrics that will be available for public use. For evaluation we utilized another lyrics dataset as ground truth and achieved an accuracy of 74.25 %. Our results confirm that valence is a better discriminator of mood than arousal. The results also prove that music mood recognition or annotation can be achieved with good accuracy even without subjective human feedback or user tags, when they are not available

Music Mood Classification Based on Lyrics and Audio Tracks

Music mood classification has always been an intriguing topic. Lyrics and audio tracks are two major sources of evidence for music mood classification. This paper compares the performance between feature representations extracted from lyrics and feature representations extracted from audio tracks. Evaluation results suggest text-based classifier and audio-feature-based classifier have similar performance for certain moods.Master of Science in Information Scienc

Korean- English Code Mixing and Code Switching Of New Jeans’s Song

This paper reviewed code-mixing and code-switching along with the worldwide recognition of K-Pop songs by analyzing the English usage of "Hype Boy" and "OMG" by New Jeans, a fourth-generation K-Pop group. All songs were examined using Muysken's (2000) code-mixing theory, Stockwell's (2007) code-switching theory, and Fernandez-Martinez et al.'s (2014) and Kwon's (2019) comment analysis. The results revealed that the majority of code-mixing in "Hype Boy" was insertion and the majority of code-mixing in "OMG" was alternation, but there was no congruent lexicalization found in the two songs. However, the majority of code switches in "Hype Boy" were inter-sentential, whereas "OMG" was intra-sentential, and two tag-switching data were found. Aside from that, based on audience responses in the comment section, the audience responses showed up that they were impacted by the singers' word pronunciation errors, however, a lot were also impressed with their easy-to-listen-to songs. The study finds that the accurate pronunciation and the new English language switching function seem to have played a significant role in the success of "Hype Boy" and "OMG" among bilingual audiences

Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction

Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music.  This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm.  In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process.  For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data.  The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious.  Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021.  Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models

Perbandingan Kinerja Algoritma K-NN dan SVM dalam Sistem Klasifikasi Genre Musik Gamelan Bali

Klasifikasi musik secara otomatis telah banyak dikembangkan dalam berbagai bentuk aplikasi dan penelitian pada bidang Music Information Retrieval (MIR). Sebagian besar tantangan dalam penelitian bidang MIR ini adalah bagaimana menggunakan fitur-fitur unik yang ada pada file musik untuk mendapatkan metadata konvensional seperti style/gaya, similarity/kemiripan, genre/jenis musik, dan mood/suasana hati. Istilah genre dalam musik merujuk pada kategori atau klasifikasi yang digunakan untuk menggambarkan gaya, karakteristik, dan elemen musik tertentu. Setiap genre memiliki ciri khas yang membedakannya dari genre lainnya. Seni musik gamelan Bali telah menjadi bagian yang tidak terpisahkan sebagai kearifan lokal yang selalu ada dalam kegiatan berkesenian, adat istiadat, hingga kagiatan keagamaan. Bagi milenials untuk mengetahui hingga membedakan suatu genre musik gamelan Bali antara satu dengan lainnya dengan waktu yang singkat bukanlah hal yang mudah. Penelitian ini membangun suatu system yang dapat mengklasifikasikan genre musik gamelan Bali berdasarkan golongan musik tua, madya, dan baru dan dibatasi pada 13 genre meliputi 260 data latih dan 130 data uji. Tahapan pre-processing dengan menggunakan transformasi FFT, kemudian ekstraksi fitur menggunakan 5 jenis spectral analysis, untuk selanjutnya diklasifikasi menggunakan algoritma K-NN dan SVM. Hasil pengujian sistem menghasilkan persentase akurasi terbaik pada algoritma K-NN yaitu 85,38%, dan algoritma SVM memperoleh akurasi sebesar 66,9%. K-NN menghasilkan waktu tercepat dalam pemrosesan klasifikasi yaitu 0,008511023 detik, sedangkan algoritma SVM memerlukan waktu 0,12942049 detik

An Analysis on Contextual Meaning of Selected Songs in Rex Orange County's Album "Pony" and Its Pedagogical Implication

Even though several previous studies discuss meaning analysis in song lyrics, limited studies also conveyed its pedagogical implication in learning. It is essential since songs and music have been widely used in the educational field, especially teaching and learning. Therefore, this research aimed to find out the type of context in the selected songs of Rex Orange County's album entitled Pony, to analyze the contextual meaning found in the songs, and to describe the pedagogical implication of the songs in English learning. The data in this research were in the form of documents, and they were obtained from Rex Orange County's selected songs in Pony album. In analyzing the data, the researchers used content analysis regarding the contextual meaning of Rex Orange County's song lyrics from the album Pony. The results showed that the Pony album contains many contexts and contextual meanings, and there were 40 contexts and 7 context types and contextual meaning in the selected songs of Pony album. These songs can be used as authentic materials to teach "Meaning Through Music" material using the Contextual Teaching and Learning (CTL) approach and Gap Song Filling strategy for the pedagogical implication

Combining Metadata, Inferred Similarity of Content, and Human Interpretation for Managing and Listening to Music Collections

Music services, media players and managers provide support for content classification and access based on filtering metadata values, statistics of access and user ratings. This approach fails to capture characteristics of mood and personal history that are often the deciding factors when creating personal playlists and collections in music. This dissertation work presents MusicWiz, a music management environment that combines traditional metadata with spatial hypertext-based expression and automatically extracted characteristics of music to generate personalized associations among songs. MusicWiz’s similarity inference engine combines the personal expression in the workspace with assessments of similarity based on the artists, other metadata, lyrics and the audio signal to make suggestions and to generate playlists. An evaluation of MusicWiz with and without the workspace and suggestion capabilities showed significant differences for organizing and playlist creation tasks. The workspace features were more valuable for organizing tasks, while the suggestion features had more value for playlist creation activities

Developing a Model for Clinical Song Analysis, or Why Music Therapists Still Need Music Theory and Musicology

In the music therapy literature, there is a distinct lack of research on clinical song analysis. Analyzing songs can be beneficial for music therapists when choosing songs to use in a session, when discussing songs with a client, and when arranging songs to play with or for clients. In this thesis, I start to bridge the fields of music therapy, music theory, and musicology to create a language of analysis upon which music therapists can draw for clinical song analysis. I focus first on foundational concepts such as timbre, style, and form, which I explain through the analysis of four different covers of the song “I’ll Fly Away.” Then, I conduct an in-depth study on the topics of persona theory and music and disability studies, including literature reviews and example analyses. I conclude by proposing pedagogical and research implications of this thesis in the fields of music therapy, music theory, and musicology

Rekayasa Sistem Pengelompokan Suasana Hati Terhadap Musik Menggunakan Algoritma K-Nearest Neighbor

Musik erat kaitannya dengan psikologi manusia, kenyataan ini mengindikasikan bahwa musik dapat terkait dengan emosi dan mood/ suasana hati tertentu pada manusia. setiap musik yang telah tercipta memiliki energi emosi tersendiri yang terpancar maka dari itu mulai banyak penelitian yang telah dilakukan pada pengenalan emosi musik tersebut. Penelitian mengangkat rekayasa sistem pengelompokan suasana hati terhadap musik dengan menggunakan algoritma K-NN. Rekayasa sistem ini mendeskripsikan alur proses diawali dengan sistem menerima masukan (input data) berupa file musik format mono .wav, yang selanjutnya dilakukan ekstraksi fitur menggunakan Fast Fourier Transform dan untuk spectral analysis, menggunakan spectral centroid, spectral kurtosis, spectral slope, spectral skewness dan spectral rolloff, seperangkat nilai feature set ini selanjutnya dilakukan proses pengelompokan terhadap musik dengan mengggunakan klasifikasi K-NN, Kemudian sistem menghasilkan output berupa jenis mood yaitu exuberance/ gembira, contentment/ kepuasan, anxious/ cemas; kalut, dan depression/ depresi. Hasil klasifikasi secara umum mencapai 65% untuk k=1, 53,3% untuk k=3, dan 48,3% untuk k=5. &nbsp