1,434 research outputs found
Crowdsourcing Emotions in Music Domain
An important source of intelligence for music emotion recognition today comes from user-provided
community tags about songs or artists. Recent crowdsourcing approaches such as harvesting social tags,
design of collaborative games and web services or the use of Mechanical Turk, are becoming popular in
the literature. They provide a cheap, quick and efficient method, contrary to professional labeling of songs
which is expensive and does not scale for creating large datasets. In this paper we discuss the viability of
various crowdsourcing instruments providing examples from research works. We also share our own
experience, illustrating the steps we followed using tags collected from Last.fm for the creation of two
music mood datasets which are rendered public. While processing affect tags of Last.fm, we observed that
they tend to be biased towards positive emotions; the resulting dataset thus contain more positive songs
than negative ones
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
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Retrieval and Annotation of Music Using Latent Semantic Models
PhDThis thesis investigates the use of latent semantic models for annotation and
retrieval from collections of musical audio tracks. In particular latent semantic
analysis (LSA) and aspect models (or probabilistic latent semantic analysis,
pLSA) are used to index words in descriptions of music drawn from hundreds
of thousands of social tags. A new discrete audio feature representation is introduced
to encode musical characteristics of automatically-identified regions
of interest within each track, using a vocabulary of audio muswords. Finally a
joint aspect model is developed that can learn from both tagged and untagged
tracks by indexing both conventional words and muswords. This model is
used as the basis of a music search system that supports query by example and
by keyword, and of a simple probabilistic machine annotation system. The
models are evaluated by their performance in a variety of realistic retrieval
and annotation tasks, motivated by applications including playlist generation,
internet radio streaming, music recommendation and catalogue searchEngineering and Physical Sciences
Research Counci
Social software for music
Tese de mestrado integrado. Engenharia Informåtica e Computação. Faculdade de Engenharia. Universidade do Porto. 200
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
Musicolinguistic documentation: Tone & tune in Tlahuapa TĂš'un SĂ vĂ songs
This study introduces a new methodology for integrating musical and linguistic data in language documentation, using ABC notation and open-source tools like ELAN and MuseScore. Designed for portability and exportability, and to facilitate both linguistic analysis and community-oriented material development, this methodology is used here to explore the link between linguistic tone and musical tune in Tlahuapa TĂš'un SĂ vĂ, a Mixtec language of Guerrero, Mexico.
Through a multimodal analysis of three Tlahuapa TĂš'un SĂ vĂ songs, this study illuminates several interactions between tone and tune, including a strong preference for melodic lines to move in parallel with the tone melody of the lyrics and associations between musical ornamentation and specific tonemes. The results of this study not only increase our understanding of the tonal system of Tlahuapa TĂš'un SĂ vĂ and its interaction with musical style but also help illustrate the rich potential of musical data in linguistic research and documentation. More than simply language data with a melody, the combination of music and language in song offers a unique opportunity for analysis not otherwise possible, and the methodologies demonstrated here aim to make this combination as accessible as possible for researchers, archivers, and community members alike.National Foreign Language Resource Cente
Sequential decision making in artificial musical intelligence
Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science
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