Relevant bibliographies by topics / Spotify API

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Spotify API'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Spotify API"

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Sumayya, Qatun, Sandhya K., and Navya Sree K. "Spotify API: Fetching Artist Details like Popularity, Followers, Top Tracks, and its Details." Recent Trends in Information Technology and its Application 8, no. 2 (2025): 14–22. https://doi.org/10.5281/zenodo.14998645.

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<em>In a fast digital music situation, personalized recommendations play a --key role in improving user experience and satisfaction. This paper examines the development of a personalized music discovery system. This is used by the Spotify -Web -API to interact with user preferences and provide tailor-made content. The system has features such as searching for artists, related artists, related artists covered, persecution of choices based on user input, and pagination of music. The system can invoke detailed information about artists, tracks, and albums, allowing users to explore top tracks bas
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KOTE, Amanda. "Advancing Music Streaming Personalization Developing a Mood-Aware Playlist Generator." INGENIOUS 5, no. 1 (2025): 7–19. https://doi.org/10.58944/vply6157.

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In recent years, music streaming services have transformed the way audiences experience and interact with music, with platforms like Spotify leading the charge. As of 2024, Spotify's user base has continued to expand, reaching millions of listeners globally who have access to an extensive library of songs, playlists and podcasts. This shift toward digital music consumption has been accompanied by an increased demand for personalization, where users expect curated experiences that resonate with their individual tastes, moods and preferences. The motivation behind this research is to enhance Spo
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Chopade, Pranali Balkrushna. "Music Recommendation System on Spotify Using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49675.

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Abstract In the era of personalized digital experiences, music recommendation systems play a vital role in enhancing user satisfaction by delivering contextually relevant content. This project presents a deep learning-based music recommendation system integrated with Spotify that tailors music suggestions based on user age and current weather conditions. The system leverages computer vision techniques for age detection through facial analysis using Convolutional Neural Networks (CNNs), while weather data is retrieved from an external API based on the user's location. By combining these inputs,
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Machmudin, Daffa Adra Ghifari, Mila Novita, and Gianinna Ardaneswari. "Analysis of Spotify's Audio Features Trends using Time Series Decomposition and Vector Autoregressive (VAR) Model." Proceedings of The International Conference on Data Science and Official Statistics 2023, no. 1 (2023): 613–27. http://dx.doi.org/10.34123/icdsos.v2023i1.375.

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&#x0D; Streaming is the most popular music consumption method of the current times. As the biggest streaming platform based on subscriber number, Spotify stores miscellaneous information regarding the music in the platform, including audio features. Spotify’s audio features are descriptions of songs features in form of variables such as danceability, duration, and tempo. These features are accessible via Application Programming Interface (API). On the other hand, Spotify also publishes their own charts consisting of 200 most streamed songs on the platform (based on regions) which are updated d
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Condit-Schultz, Nathaniel. "Are The Beatles Really Different? Commentary on North and Krause (2023)." Empirical Musicology Review 18, no. 2 (2024): 161–74. http://dx.doi.org/10.18061/emr.v18i2.9784.

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This article is a commentary on "Are The Beatles Different? A Computerized Psychological Analysis of Their Music," by North and Krause (2023), in which they analyze features extracted from the Spotify API and ultimately claim that The Beatles' music is statistically "innovative" compared to other music. In this commentary, I explore potential methodological issues with some of their analyses. Chiefly, I show that applying their analysis to other artists results in similar results in most cases. I conclude that The Beatles' innovativeness, whether real or imaginary, cannot be statistically dete
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Supraja, Ch. "Create a Remote-Control Application with Spotify." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4405–11. http://dx.doi.org/10.22214/ijraset.2021.36079.

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Spotify is a music streaming service that was developed in Stockholm, Sweden. The first version was released back in 2008 and today it doesn't only provide music, but video and podcasts as well. Growing rapidly from a startup in Sweden to the biggest music service in the world, Spotify has apps running on video game consoles and mobile phones, and has integration with many social networks. Luckily, Spotify is also a great platform for developers and provides a really nice and well-documented REST API where it's possible to make searches by artists, search by albums, search by tracks and play s
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Nugroho, Atmoko, Danny Manongga, Hindriyanto Dwi Purnomo, and Hendry Hendry. "Analysis Of Spotify Top Songs During Covid-19 Pandemic." International Journal of Marketing and Digital Creative 1, no. 2 (2023): 1–14. http://dx.doi.org/10.31098/ijmadic.v1i2.1565.

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During the COVID-19 pandemic, many behaviors or habits have changed, especially in the internet audio-visual field which has increased significantly, one example is Spotify as an audio service provider. Not all songs on Spotify are popular or in the Top Songs. This study aims to examine whether there were differences in popular songs during the pandemic and before the pandemic and to determine the relationship between factors of popular songs on Spotify during the COVID-19 pandemic. The method used is to fetch Spotify songs via the API (Application Programming Interface) with the Spotify Pytho
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П'ятикоп, О. Є., та А. Є. Шевченко. "Інтеграція API у вебдодаток для збирання музичних даних з відкритих джерел". COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, № 58 (26 березня 2025): 159–64. https://doi.org/10.36910/6775-2524-0560-2025-58-19.

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Популяризація української музики у світі є важливим інструментом збереження культурної ідентичності та формування позитивного іміджу країни. У цій роботі розглядається питання автоматизованого збору та аналізу музичних даних українських виконавців із використанням Spotify API. Розроблено вебдодаток на основі фреймворку Remix та мови TypeScript, що дозволяє здійснювати авторизацію, отримувати інформацію про композиції, виконавців та альбоми, а також зберігати їх у зручному форматі для подальшої обробки. У межах дослідження було сформовано вибірку, що включає 1,106 записів про композиції від 568
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Razlogova, Elena. "Provincializing Spotify: Radio, algorithms and conviviality." Radio Journal:International Studies in Broadcast & Audio Media 18, no. 1 (2020): 29–42. http://dx.doi.org/10.1386/rjao_00014_1.

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Focusing on early experiments with algorithms and music streaming at WFMU, the longest-running US freeform radio station, and the Free Music Archive (FMA), a curated open music website, this article shows how commercial streaming services have been indebted to independent, open music infrastructures but have then erased and denied that history. The article ‘provincializes’ music streaming platforms such as Spotify by focusing not on their commercial aims but instead on the ‘convivial’, collaborative practices and spaces that their software engineers and users inhabited. I analyse an experiment
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Otuokere, Tobechukwu Okechukwu, Agbotiname Lucky Imoize, and Aderemi Aaron-Anthony Atayero. "Analysis of Sonic Effects of Music from a Comprehensive Datasets on Audio Features." ELEKTRIKA- Journal of Electrical Engineering 20, no. 1 (2021): 43–53. http://dx.doi.org/10.11113/elektrika.v20n1.233.

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Music, for the longest time, has impacted human lives tremendously. The ability of music to access and activate a wide range of human emotions is sensational. Toward this end, audio features provide a variety of information necessary for sound engineers, music producers, and artists to improve their craft to excite the vast majority of music listeners across the globe. In this paper, analysis of audio features derived using the Spotify web API endpoint and Spotify (Python module for Spotify web servers) is presented. The dataset was curated from audio features of over 160,000 songs released fr
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Dissertations / Theses on the topic "Spotify API"

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Strandberg, Aron, and Patrik Karlström. "Processing Natural Language for the Spotify API : Are sophisticated natural language processing algorithms necessary when processing language in a limited scope?" Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186867.

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Knowing whether you can implement something complex in a simple way in your application is always of interest. A natural language interface is some- thing that could theoretically be implemented in a lot of applications but the complexity of most natural language processing algorithms is a limiting factor. The problem explored in this paper is whether a simpler algorithm that doesn’t make use of convoluted statistical models and machine learning can be good enough. We implemented two algorithms, one utilizing Spotify’s own search and one with a more accurate, o✏ine search. With the best precis
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Bygdeson, Mattias. "Utvecklingen av Spotalike." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36530.

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The goal with this assignment has been to study the product Spotalike and develop a new version to make the product more attractive. The studying of the product was done with the help of user data, such as how Spotalike is being used, what target audience it has, why it's being used, etc. The new version of Spotalike was planned by making design sketches and prototypes which were created as a first step in order to get a better picture of what the result would be. The new version is not available to the public, but it is fully functional and works locally. The solution that was concluded was t
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Yeh, Chan-Rong, and 葉展榕. "A Novel Content-Based Music Similarity Measuring Method: A Study of Spotify API Audio Analysis Dataset." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/kgpq7a.

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碩士<br>國立交通大學<br>科技管理研究所<br>106<br>Due to the continued expansion of the music market and the popularity of streaming music services, it's becoming increasingly important to highlight personalized recommendation systems. And the music similarity measures in content-based recommendation systems is often incorporated into the mainstream recommendations system to provide personalized music recommendations. In the context of the world's largest music streaming service provider: Spotify released audio features API, there is no literature to explain the audio features and to propose a similarity meas
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Book chapters on the topic "Spotify API"

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Chiyonobu, Miho, and Masami Takata. "Improved Method for Similar Music Recommendation Using Spotify API." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85638-9_14.

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Hafeez, Yasir, Syed Hasan Adil, Mansoor Ebrahim, and Mirzan Izzfitri Bin Mahadir. "Speech Emotion Recognition-Based Music Recommender." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9057-3.ch008.

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The objective of this chapter is to provide details implementation of a research project; song recommendations based on speech emotions through Speech Emotion Recognition (SER). This involves developing a Speech Emotion Recognition model utilizing neural network algorithms or deep learning techniques. The selected algorithms include a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, a Dense Neural Network (DNN), and a custom hybrid algorithm combining CNN and LSTM. A PyQT5 application framework was implemented to facilitate song recommendations. Users can record the
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Sharma, Ankita, and Varun Nayyar. "Case Study on AI and the Future of Market Research in the FMCG Sector." In Generative AI in Food Systems. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-5182-7.ch001.

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Artificial intelligence has made great strides in recent years, and this development has been felt deeply across many industries, the market research sector included. AI technology has emerged as one of the most disruptive technologies in recent years, changing how businesses function and engage with client. In marketing, AI is now playing a larger role tactically: companies are using it to instruct their platforms and boost performance. (Hicham et al., 2023) Business use of AI in marketing decision-making is important for any business looking to position itself favourably within a changing ma
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Conference papers on the topic "Spotify API"

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Neeli, Jyoti, Vijaya Shetty S, Shreya Rao, Srujana S N, and Nidhi Narayan. "Music Recommendation System Based on Facial Emotion Detection Using Spotify API." In 2025 International Conference on Computing for Sustainability and Intelligent Future (COMP-SIF). IEEE, 2025. https://doi.org/10.1109/comp-sif65618.2025.10969890.

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Shalini, V. Baby, Rajkumar Pandiarajan, K. Brundha, S. M. Vedha Varshini, P. Rishikumar, and N. Vaishnavy. "A Modular Music Recommendation System using Unsupervised Learning and Spotify API Integration." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10985648.

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B M, Karthik Balaji, Sujithra J, and Yashwanth J. "Real- Time Emotion-Based Music Recommendation System Using YOLOv5 and Spotify API." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026210.

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Natev, Andrej. "Hit Song Prediction Through Machine Learning and Spotify Data." In 10th Student Computing Research Symposium. University of Maribor Press, 2024. https://doi.org/10.18690/um.feri.6.2024.13.

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This study predicts hit songs using metadata from the Spotify API[8]. The dataset includes over 20 genres, each with 40 songs, equally divided between hits and flops, gathered using spotipy[7]. Prediction is based on the popularity feature, rated from 0-100. Models were trained on features like danceability, energy, loud-ness, speechiness, valence, and tempo. The dataset was split using train_test_split (10%, 20%, 33%) and kfold cross-validation with k val-ues of 2, 5, and 10. Models were trained, evaluated, and tested, with kfold cross-validation showing the best accuracy and the least over-f
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Paula, Bruna C. M., Gabriel P. Oliveira, and Mirella M. Moro. "Mood Analysis during the COVID-19 Pandemic in Brazil through Music." In Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/webmedia_estendido.2022.227063.

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In this paper, we investigate the oscillation in the general feelings of the Brazilian population during the Pandemic through the songs consumed. We analyze Brazilian streaming musical consumption between 2019 and 2021. In special, we focus on special dates that have changed history, such as the beginning of the pandemic in the country, the dates of increase in cases, milestone dates in deaths, the beginning of vaccination, among others. Data was collected through Spotify API and made publicly available. Our results show people have preferred more danceable and positive songs during the period
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Fontes, Flaviano Dias, Giordano Ribeiro Eulalio Cabral, and Geber Lisboa Ramalho. "An open source platform to assist the creation of group playlists through artificial intelligence algorithms." In Simpósio Brasileiro de Computação Musical. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbcm.2021.19442.

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Recommendation systems are a constantly expanding study area, with applications in various fields such as e-commerce, films, music to promote the user’s suggestions. When we talk about music, we have more than 20 years of studies trying to solve the problem of a good generation of playlists that maximizes the satisfaction of a larger number of listeners. For automated automatic playlist generation methods focusing on a user group, we have the collaborative filter as a more assertive method to get the user’s not likely, to improve the performance of group recommendation algorithms we store the
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Misael, Luan, Carlos Forster, Emanuel Fontelles, Vinicius Sampaio, and Mardônio França. "Temporal Analysis and Visualisation of Music." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eniac.2020.12155.

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This paper proposes a temporal analysis for music metadata using a generative probabilistic model for collections the discrete datasets such as text corpora. This method is also a topic model that is used for discovering abstract topics from a collection of documents. The method is then applied to audio metadata and song lyrics extracted with Echo Nest® engine, Spotify® Lyrics Genius® API. Song data time series are generated by grouping data items by release date, genre and dominant topics (from LDA analysis). Using a technique from Network Theory we visualise how these topics, in this case, g
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Bachal, Tarush, Ashwin Kumar, Anish Bachal, and Vitthal S. Gutte. "Tuning in to Personalized Music: A Spotify API-Based Hybrid Recommendation System Integrating Content-Based and Popularity-Based Approaches." In 2023 2nd International Conference on Futuristic Technologies (INCOFT). IEEE, 2023. http://dx.doi.org/10.1109/incoft60753.2023.10425661.

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Moreira Júnior, Luiz Delando Santos, Guilherme Ribeiro Eulálio Cabral, and Giordano Ribeiro Eulálio Cabral. "Correlating Big Five Primary Personality Dimensions with Musical Preferences." In Simpósio Brasileiro de Computação Musical. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbcm.2021.19433.

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According to J Clement in 2019 an estimated 2.95 billion people were using social media worldwide, with a projection of 3.43 billion in 2023. Social networks increasingly play a fundamental role in the way of expressing our-selves and relating to others, in which it is directly linked to our personality. Several studies relate a persons personality to the most diverse areas, such as predicting political orientation, classifying gender, or even measuring the level of success at work. Wu Youyou, 2015 proposes an algorithm that can allegedly estimate the personality of a Facebook user better than
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Bhowmick, Anusha, Kankshini Shamkuwar, J. D. Dorathi Jayaseeli, and D. Malathi. "Song Recommendation System based on Mood Detection using Spotify's Web API." In 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC). IEEE, 2022. http://dx.doi.org/10.1109/iihc55949.2022.10060806.

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