Music4All: A New Music Database and Its Applications

Santana, Igor André Pegoraro; Pinhelli, Fabio; Donini, Juliano; Catharin, Leonardo Gabiato; Mangolin, Rafael B.; Costa, Yandre M. G.; Feltrim, Valéria Delisandra; Domingues, Marcos Aurélio

doi:10.1109/iwssip48289.2020.9145170

Cited by 23 publications

(4 citation statements)

References 20 publications

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“…Each auditory stimuli are a fragment of instrumental music. We selected the eight types of music pieces from the database of Santana et al [3], which are a length of 30 seconds. The set of musical stimuli has enough diversity of musical impressions and objective features (i.e., valence, popularity, and so on).…”

Section: Methods 21 Stimulimentioning

confidence: 99%

Observation of Boredom during Repetitive Listening of the Same Music in Various Time Spans

Kojima

Nakata

Sakamoto

et al. 2023

ISASE

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Streaming services repetitively provide similar kinds of music, and listeners might get tired of them quickly. This is one of the serious problems for streaming services today. Boredom is developed with repetition, and it depends on personal preference. Therefore, boredom development depends on the time spans of the repetition and personal preferences when people listen to the same music over days. This study conducts an experiment that the participants repetitively listen to one music piece 294 times for seven days in a row to observe boredom development. We measured the boredom level as the reactional factor of the failure to keep the concentration and motivation of listening. As a result, the concentration decreased in the short-term, whereas the motivation decreased in the long-term. Therefore, we could distinguish the state of boredom by the length of the time span. In addition, compared to repetitive listening with a day as the short-term, repetitive listening every day in a week as the long-term had an equal or slightly higher power to generate boredom, but there is a weak correlation between boredom and preference for a music piece. In conclusion, the results showed that the amount of repetition had a stronger effect than personal preference for music, and people may get bored even with their favorite music when they listen too many times. Moreover, the behavior of the low-frequency alpha wave in the brain had only a weak relationship to the amount of listening within a day, not in the week.

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Section: Methods 21 Stimulimentioning

confidence: 99%

Observation of Boredom during Repetitive Listening of the Same Music in Various Time Spans

Kojima

Nakata

Sakamoto

et al. 2023

ISASE

View full text Add to dashboard Cite

show abstract

“…The Music4All dataset comprises a collection of 109,269 songs gathered from the MusicMatch application. The dataset encompasses a wealth of information, including metadata, tags, genre details, 30-second audio clips, lyrics, and more [11].…”

Section: Music4all Datasetmentioning

confidence: 99%

A Comprehensive Survey of Music Genre Classification Using Audio Files

Shirol,

Kathiresan R S

2023

IJERSTE

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This survey extensively studies music genre classification, a critical task in music information retrieval, to automatically categorize audio recordings into various genres. It provides a comprehensive review of approaches, methodologies, and recent advancements in genre classification from audio data. Scholars and practitioners in the field will find this study to be a valuable resource as it covers various aspects of the discipline, including feature extraction, classification methods, dataset exploration, evaluation metrics, and recent developments. The survey aims to enhance the understanding of music genre classification and foster further research and progress in the field by critically evaluating state-of-the-art techniques discussed in research papers, discussing their strengths and limitations, and providing a comprehensive overview of the field.

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“…Menggunakan fitur Spotify API kita bisa mengekstrak karakteristik dari suatu lagu yang dinyanyikan oleh seorang penyanyi. Karakteristik yang bisa kita peroleh diantaranya: Acousticness, Danceability, Energy, Loudness, Liveness, Speechiness, Tempo, dan Valence (Santana et al, 2020). Berdasarkan hasil ekstrak yang diperoleh, kita bisa membangun suatu model klasifikasi untuk memberikan label artist music.…”

Section: Pendahuluanunclassified

Analisis Klasifikasi Artist Music Menggunakan Model Regresi Logistik Biner Dan Analisis Diskriminan

Dani

RATNASARI²,

NI'MATUZZAHROH³

et al. 2022

Jambura J. Probab. Stat.

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Characteristics of a song are an important aspect that must be kept authentic by a singer. Using the Spotify API feature, we can extract the characteristics or elements of a song sung by a singer. There are eight (8) elements that we can get from the extraction of a song, namely: Danceability, Energy, Loudness, Speechiness, Acousticness, Liveness, Valence, and Tempo. Based on the extraction results, we can label the music artist using the classification analysis method. In this study, the labels are music artists, namely Ariana Grande and Taylor Swift. This study aims to obtain the classification of music artist labels using binary logistic regression methods and discriminant analysis. The response variable used in this study is Artist Music (Y) which is categorized into two categories, namely Ariana Grande (Y=0) and Taylor Swift (Y=1). The data will be divided into training and testing data with the proportion of data 90:10 and 80:20. Based on the results of the analysis, the binary regression model that was built, with the proportion of training testing data that is 90:10 has a classification accuracy for data testing of 90.00%.

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Music4All: A New Music Database and Its Applications

Cited by 23 publications

References 20 publications

Observation of Boredom during Repetitive Listening of the Same Music in Various Time Spans

Observation of Boredom during Repetitive Listening of the Same Music in Various Time Spans

A Comprehensive Survey of Music Genre Classification Using Audio Files

Analisis Klasifikasi Artist Music Menggunakan Model Regresi Logistik Biner Dan Analisis Diskriminan

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