Search Engine and Recommendation System for the Music Industry built with JinaAI

One of the most intriguing debates regarding a novel task is the development of search engines and recommendation-based systems in the music industry. Studies have shown a drastic depression in the search engine fields, due to concerning factors such as speed, accuracy and the format of data given for querying. Often people face difficulty in searching for a song solely based on the title, hence a solution is proposed to complete a search analysis through a single query input and is matched with the lyrics of the songs present in the database. Hence it is essential to incorporate cutting-edge technology tools for developing a user-friendly search engine. Jina AI is an MLOps framework for building neural search engines that are utilized, in order for the user to obtain accurate results. Jina AI effectively helps to maintain and enhance the quality of performance for the search engine for the query given. An effective search engine and a recommendation system for the music industry, built with JinaAI

Controlled Generation of Stylized Text Using Semantic and Phonetic Representations

Neural networks are a popular choice of models for the purpose of text generation. Variational autoencoders have been shown to be good at reconstructing text and generating novel text. However, controlling certain aspects of the generated text (e.g., length, semantics, cadence) has proven a more difficult task. The objectives of disentanglement and controlled text generation have thus become areas of interest, with various approaches depending on the aspects we desire to control. In this work we study controllable generation of lyric text based on semantic and phonetic criteria. The phonetic information takes the form of generalized phonetic patterns. A Bag-of-Words Variational Autoencoder (VAE) extracts and models the semantic information, while a phonetic pattern VAE handles the phonetic information. Each uses several regularization techniques for its respective latent space and the information from each is fed to a lyrics decoder to generate novel lyric lines that would satisfy both the Bag-of-Words and phonetic constraints. The experiments show that our model can learn to reconstruct phonetic patterns extracted from text and use them with the Bag-of-Words representations to reconstruct the original lyric lines. Together, the learned representations of phonetic patterns and Bag-of-Words constraints can be used to generate new lyrics

Music emotion recognition: a multimodal machine learning approach

Music emotion recognition (MER) is an emerging domain of the Music Information Retrieval (MIR) scientific community, and besides, music searches through emotions are one of the major selection preferred by web users. As the world goes to digital, the musical contents in online databases, such as Last.fm have expanded exponentially, which require substantial manual efforts for managing them and also keeping them updated. Therefore, the demand for innovative and adaptable search mechanisms, which can be personalized according to users’ emotional state, has gained increasing consideration in recent years. This thesis concentrates on addressing music emotion recognition problem by presenting several classification models, which were fed by textual features, as well as audio attributes extracted from the music. In this study, we build both supervised and semisupervised classification designs under four research experiments, that addresses the emotional role of audio features, such as tempo, acousticness, and energy, and also the impact of textual features extracted by two different approaches, which are TF-IDF and Word2Vec. Furthermore, we proposed a multi-modal approach by using a combined feature-set consisting of the features from the audio content, as well as from context-aware data. For this purpose, we generated a ground truth dataset containing over 1500 labeled song lyrics and also unlabeled big data, which stands for more than 2.5 million Turkish documents, for achieving to generate an accurate automatic emotion classification system. The analytical models were conducted by adopting several algorithms on the crossvalidated data by using Python. As a conclusion of the experiments, the best-attained performance was 44.2% when employing only audio features, whereas, with the usage of textual features, better performances were observed with 46.3% and 51.3% accuracy scores considering supervised and semi-supervised learning paradigms, respectively. As of last, even though we created a comprehensive feature set with the combination of audio and textual features, this approach did not display any significant improvement for classification performanc

ReLyMe: Improving Lyric-to-Melody Generation by Incorporating Lyric-Melody Relationships

Lyric-to-melody generation, which generates melody according to given lyrics, is one of the most important automatic music composition tasks. With the rapid development of deep learning, previous works address this task with end-to-end neural network models. However, deep learning models cannot well capture the strict but subtle relationships between lyrics and melodies, which compromises the harmony between lyrics and generated melodies. In this paper, we propose ReLyMe, a method that incorporates Relationships between Lyrics and Melodies from music theory to ensure the harmony between lyrics and melodies. Specifically, we first introduce several principles that lyrics and melodies should follow in terms of tone, rhythm, and structure relationships. These principles are then integrated into neural network lyric-to-melody models by adding corresponding constraints during the decoding process to improve the harmony between lyrics and melodies. We use a series of objective and subjective metrics to evaluate the generated melodies. Experiments on both English and Chinese song datasets show the effectiveness of ReLyMe, demonstrating the superiority of incorporating lyric-melody relationships from the music domain into neural lyric-to-melody generation.Comment: Accepted by ACMMM 2022, ora

歌詞の談話構造のモデル化

Tohoku University乾健太郎課

THE TREATMENT OF KOREAN TRADITIONAL MUSICAL ELEMENTS IN WESTERN MUSICAL COMPOSITION: A BRIEF ANALYSIS OF \u3cem\u3eFOLKSONG REVISITED\u3c/em\u3e FOR SOLO PIANO BY JEAN AHN

Jean Ahn (b. 1976) is one of the active Korean woman composers in the U.S. Ahn’s goal is to introduce her works in the U.S. by composing pieces that combine Korean musical elements with Western compositional techniques. The purpose of this study is to provide an introduction to and analysis of Folksong Revisited for solo piano by Jean Ahn. This work demonstrates how Jean Ahn integrates Korean traditional musical elements and Western musical compositional techniques. For better understanding of Ahn’s three Korean folksong arrangements in the Folksong Revisited, this document provides brief information about Korean traditional music and explores elements of it. This document also examines the folksong sources of each piece and Ahn’s compositional approaches to them, and then provides performance suggestions

“The Normal Order of Things”: Propriety, Standardisation and the Making of Tin Pan Alley

PhD ThesisThis thesis employs a variegated approach that considers demographics, institutions, business practices, and dominant lyrical themes and imagery, in order to establish the pervasiveness of an ideology of propriety within early twentieth-century Tin Pan Alley and its songwriting output. The thesis proposes that this pervasiveness ultimately contributed to the standardisation of song structure within Tin Pan Alley song itself. For the most part, the first generation of Tin Pan Alley, prior to 1920, is considered, in an account of the commercial and aesthetic foundations that led to the ‘Golden Age’ – the period for which the Alley has been elevated into national myth. Specifically, it is proposed that in the context of a nation constituted of exilic narratives, and one constantly engaged in a process of identity formation, Tin Pan Alley’s institutions, personnel, practices and products engendered a ‘structure of feeling’ (after Raymond Williams) that amounted to an ideology of propriety, realised through a multivalent aesthetic of Exile/Home. An account of the material and social processes of mass-standardisation for which Tin Pan Alley is well-known is developed, and situated within a broader historical context. The sectional song structure 32-bar AABA is figured as the standardised product of an industrial context, shaped by this ideology of propriety. Furthermore, the dominant themes and lyrical content of the sentimental song are investigated, in order to establish the resonances between these and 32-bar AABA and how they may share ideological import. Finally, an account of the pragmatic, ideological and cognitive affordances of 32-bar AABA is developed, and a statement on how such a study relates to Adorno’s views on mass-culture and Tin Pan Alley concludes the work

Musical linguistics: How music and artistic creativity when delivered as a linguistic practice, help students master academic skills in English language arts

This Masters project consists of two elements: 1) an integrated after-school program to improve student English language reading and academic outcomes for third graders\u27 vocabulary development by incorporating music, artistic creativity and linguistics; 2) a pilot sample curriculum that demonstrates the approach for building student comprehension through musical theater and Science, Technology, Engineering, and Mathematics (STEM) content experiences. Called Water Buddy , this is an after-school program uses singing, dancing, writing, and play to build reading and vocabulary skills. The goal is to improve learner academic outcomes by mastering the elemental building blocks of words, letters, symbols by making memorable the sound units, idioms, print conventions, and concepts that they were previously lacking

Rejecting the rejecters: The latent effect of policy on subculture

Specifically, this thesis is a look into rap lyrics, subculture, policy, reflexivity and the formation of the social self. In a broader vision, this thesis attempts to mold a theoretical pathway that illuminates where our cultural products come from, not historically, but socially. Through the vehicle of rap lyrics I attempt to show that there is a historical and social structure that molds, limits and contains the very possibility of what music and lyrics can come to be. I try to show that the decisions we make on a national scale effects groups which have little political power, effectively recreating their realities, cultures and their value systems. Policy becomes a mechanism, which I call rejection, that forces people to live certain ways consequently reforming their social mapping, and by extension, their social selves. I then utilize auto-ethnography to show that, perhaps, rejection is a part of all us, and that it never quite escapes our cultural products, our work and those things we create