15 research outputs found
Generating Music from Literature
We present a system, TransProse, that automatically generates musical pieces
from text. TransProse uses known relations between elements of music such as
tempo and scale, and the emotions they evoke. Further, it uses a novel
mechanism to determine sequences of notes that capture the emotional activity
in the text. The work has applications in information visualization, in
creating audio-visual e-books, and in developing music apps
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
Computer-Generated Music for Tabletop Role-Playing Games
In this paper we present Bardo Composer, a system to generate background
music for tabletop role-playing games. Bardo Composer uses a speech recognition
system to translate player speech into text, which is classified according to a
model of emotion. Bardo Composer then uses Stochastic Bi-Objective Beam Search,
a variant of Stochastic Beam Search that we introduce in this paper, with a
neural model to generate musical pieces conveying the desired emotion. We
performed a user study with 116 participants to evaluate whether people are
able to correctly identify the emotion conveyed in the pieces generated by the
system. In our study we used pieces generated for Call of the Wild, a Dungeons
and Dragons campaign available on YouTube. Our results show that human subjects
could correctly identify the emotion of the generated music pieces as
accurately as they were able to identify the emotion of pieces written by
humans.Comment: To be published in the 16th AAAI Conference ON Artificial
Intelligence and Interactive Digital Entertainmen
Transcoding as a Compositional Paradigm
This paper focuses on one of the author’s compositions, Outer Space, conceived using transcoding techniques between musical and visual parameters, such as the relative percentage of black and white dots per frame and the difference between the position of the dots from one frame to another
A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends
Currently available reviews in the area of artificial intelligence-based music generation do not provide a wide range of publications and are usually centered around comparing very specific topics between a very limited range of solutions. Best surveys available in the field are bibliography sections of some papers and books which lack a systematic approach and limit their scope to only handpicked examples In this work, we analyze the scope and trends of the research on artificial intelligence-based music generation by performing a systematic review of the available publications in the field using the Prisma methodology. Furthermore, we discuss the possible implementations and accessibility of a set of currently available AI solutions, as aids to musical composition. Our research shows how publications are being distributed globally according to many characteristics, which provides a clear picture of the situation of this technology.
Through our research it becomes clear that the interest of both musicians and computer scientists in AI-based automatic music generation has increased significantly in the last few years with an increasing participation of mayor companies in the field whose works we analyze. We discuss several generation architectures, both from a technical and a musical point of view and we highlight various areas were further research is needed
Bajki robotów : przegląd wybranych projektów generujących sztuczną literaturę
Artykuł zawiera przegląd wybranych projektów sztucznej twórczości w dziedzinie literatury.
Zdefiniowana została sztuczna twórczość oraz aspekty prawne w zakresie prawa autorskiego i praw pokrewnych
Bajki robotów. Przegląd wybranych projektów generujących sztuczną literaturę
The article provides an overview of selected projects of artificial creations in the field of literature. We defined the concept of artificial creation and legal aspects of copyright and related rights.Artykuł zawiera przegląd wybranych projektów sztucznej twórczości w dziedzinie literatury. Zdefiniowana została sztuczna twórczość oraz aspekty prawne w zakresie prawa autorskiego i praw pokrewnych
Research, development and evaluation of a practical model for sentiment analysis
Sentiment Analysis is the task of extracting subjective information from input sources
coming from a speaker or writer. Usually it refers to identifying whether a text holds a
positive or negative polarity. The main approaches to carry out Sentiment Analysis are
lexicon or dictionary-based methods and machine learning schemes. Lexicon-based models
make use of a prede ned set of words, where each of the words composing the set has an
associated polarity. Document polarity will depend on the feature selection method, and how
their scores are combined. Machine-learning approaches usually rely on supervised classifiers.
Although classifiers offer adaptability for specific contexts, they need to be trained with huge
amounts of labelled data which may not be available, specially for upcoming topics.
This project, contrary to most scientific researches over this field, aims to go further in
emotion detection and puts its efforts on identifying the actual sentiment of documents,
instead of focusing on whether it may have a positive or negative connotation. The set of
sentiments used for this approach have been extracted from Plutchik's wheel of emotions,
which defines eight basic bipolar sentiments and another eight advanced emotions composed
of two basic ones. Moreover, in this project we have created a new scheme for SA combining
a lexicon-based model for getting term emotions and a statistical approach to identify the
most relevant topics in the document which are the targets of the sentiments. By taking this
approach we have tried to overcome the disadvantages of simple Bag-of-words models that
do not make any distinctions between parts of speech (POS) and weight all words commonly
using the tf-idf scheme which leads to overweight most frequently used words. Furthermore,
in order to improve knowledge, this projects presents a heuristic learning method that
allows improving initial knowledge by converging to human-like sensitivity.
In order to test proposed scheme's performance, an Android application for mobile devices
has been developed. This app allows users taking photos and introducing descriptions which
are processed and classi ed with emotions. Classi cation that may be corrected by the user
so that system performance statistics can be extracted.El Análisis de Sentimientos consiste en extraer información subjetiva de lenguaje escrito
u oral. Habitualmente se basa en identificar si un texto es positivo o negativo, es decir,
extraer su polaridad. Las principales formas de llevar a cabo el Análisis de Sentimientos son
los métodos basados en dictionarios y en aprendizaje automático. Los modelos basados en
léxicos hacen uso de un conjunto predefinido de palabras que tienen asociada una polaridad.
La polaridad del texto dependerá los elementos analizados y la forma en la que se combinan
sus valores. Las aproximaciones basadas en aprendizaje automático, por el contrario, normalmente
se apoyan en clasificadores supervisados. A pesar de que los claificadores ofrecen
adaptabilidad para contextos muy específicos, necesitan gran cantidad de datos para ser
entrenados no siempre disponibles, como por ejemplo en temas muy novedosos.
Este proyecto, al contrario que la mayoría de investigaciones en este campo, intenta ir
m as allá en la detección de emociones y pretende identificar los sentimientos del texto en
vez de centrarse en su polaridad. El conjunto de sentimientos usados para este proyecto
esrá basado en la Rueda de las Emociones de Plutchik, que define ocho sentimientos
básicos y ocho complejos formados por dos básicos. Además, en este proyecto se ha creado
un nuevo modelo de AS combinando léxicos para extraer las emociones de las palabras con
otro estadístico que trata de identificar los temas más importantes del texto. De esta forma,
se ha intentado superar las desventajas de los modelos Bag-of-words que no diferencian
entre clases de palabras y ponderan todas las palabras usando el esquema tf-idf, que
conlleva sobreponderar las palabras más usadas. Asimismo, para mejorar el conocimiento
del proyecto, se ha implementado un método de aprendizaje heurístico que permite mejorar
el conocimiento inicial para converger con la sensibilidad real de los humanos.
Para evaluar el rendimiento del modelo propuesto, una aplicación Android para móviles
ha sido desarrollada. Esta app permite a los usuarios tomar fotos e introducir descripciones
que son procesadas y clasificadas por emociones. Clasificación que puede ser corregida por
el usuario permitiendo así extraer estadísticas del rendimiento del sistema.Ingeniería Informátic