Sökning: "music curation"

Hittade 5 uppsatser innehållade orden music curation.

1. 1. The TikTok effect: How TikTok is shaping the way we consume music

   Kandidat-uppsats, Lunds universitet/Företagsekonomiska institutionen

   Författare :Greta Ranff; Loui Degener; [2023]
   Nyckelord :consumer behavior; music consumption; social media; TikTok; recommendation algorithms; marketing platforms; Business and Economics;

   Sammanfattning : Digitalization has in recent years transformed the music industry and how people consume music. Simultaneously, the social media app TikTok has emerged as a major player in marketing, using its unique algorithmic structure and user-generated content to become a powerful marketing tool for musicians and record labels alike. LÄS MER

3. 2. Streamingtjänster och plattformsbevakning : En studie om musiklivets nya portvakt i en tid av streamad musik

   Kandidat-uppsats, Uppsala universitet/Institutionen för musikvetenskap

   Författare :Philip Grunditz; [2023]
   Nyckelord :gatekeeping; streaming; music curation; algorithms; playlists; digital platforms;

   Sammanfattning : This paper examines how streaming services, with a particular focus on Spotify, have influenced music consumption through the implementation of algorithmically and humanly curated playlists and recommendation features. By placing the role of streaming services in a historical context, this paper explores streaming services as an extension of previous gatekeeping functions in the music industry. LÄS MER

4. 3. Me, my Shelf and I : Designing Meaningful Digital Collections

   Kandidat-uppsats, Malmö universitet/Institutionen för konst, kultur och kommunikation (K3)

   Författare :Theo Nordahl; [2020]
   Nyckelord :Interaction design; Digital collections; Digital collecting; E-books; E-book; Media; Collection; Collecting; Interaktionsdesign;

   Sammanfattning : Personal media collections are becoming increasingly digitised with physical representations of music, film, games and books being replaced by virtual counterparts. Through qualitative fieldwork, this thesis examines the relationship that people have with traditional collections, and therein seeks to outline the aspects of which we find to be meaningful and enjoyable. LÄS MER

5. 4. A comparative analysis of CNN and LSTM for music genre classification

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Gabriel Gessle; Simon Åkesson; [2019]
   Nyckelord :Bachelor thesis; music genre classification; GTZAN; FMA; CNN; LSTM; Kandidatexamensarbete; klassificering av musikgenrer; GTZAN; FMA; CNN; LSTM;

   Sammanfattning : The music industry has seen a great influx of new channels to browse and distribute music. This does not come without drawbacks. As the data rapidly increases, manual curation becomes a much more difficult task. Audio files have a plethora of features that could be used to make parts of this process a lot easier. LÄS MER

7. 5. Deep Neural Networks for Context Aware Personalized Music Recommendation : A Vector of Curation

   Master-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)

   Författare :Oktay Bahceci; [2017]
   Nyckelord :Information Filtering; Information Retrieval; Search Engine; Search Engines; Recommendation; Music Recommendation; Personalized Recommendation; Personalised Recommendation; Context Aware Recommendation; Recommender Systems; Statistical Learning; Artificial Intelligence; Machine Learning; Deep Learning; Neural Networks; Artificial Neural Networks; Feed Forward Neural Networks; Convolutional Neural Networks; Recurrent Neural Networks; Deep Neural Networks; Embedding;

   Sammanfattning : Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. LÄS MER