Fumbling to the future? Socio-technical regime change in the recorded music industry
دست و پا می زنید به آینده؟ تغییر رژیم اجتماعی - فنی در صنعت موسیقی ضبط شده-2020
In this paper, I draw on the institutional entrepreneurship and sociotechnical imaginaries literature to develop a prospective and actor-centric approach to understanding technological transitions. Empirically, I examine the initiatives that newcomers and incumbents engaged in between 1990 and 2005 to transition the socio-technical regime associated with recorded music. My account reveals the limited ability of these actors to effectively migrate the regime despite initiating several efforts to do so – a pattern of behavior I term the fragility of in- stitutional entrepreneurship. I identify underlying factors for why this is the case and suggest that these can contribute to a regime remaining in a state of flux for an extended period of time. I also demonstrate the emergence of provisional regimes or temporary settlements between actors that either gain traction or are themselves transformed over time. In specifying the micro-processes that unfold as part of such transitions, I provide a complementary perspective to the current theorizing around socio-technical regime migration, and contribute fresh insights to the institutional entrepreneurship and sociotechnical imaginaries literature.
Keywords: Socio-technical regime transition | Technological change | Institutional entrepreneurship | Sociotechnical imaginaries | Digital music
Music Recommender System Based on Genre using Convolutional Recurrent Neural Networks
سیستم موسیقی توصیه گر بر اساس ژانر با استفاده از شبکه های عصبی همگرای بازرخدادگر-2019
With commercial music streaming service which can be accessed from mobile devices, the availability of digital music currently is abundant compared to previous era. Sorting out all this digital music is a very time-consuming and causes information fatigue. Therefore, it is very useful to develop a music recommender system that can search in the music libraries automatically and suggest suitable songs to users. By using music recommender system, the music provider can predict and then offer the appropriate songs to their users based on the characteristics of the music that has been heard previously. Our research would like to develop a music recommender system that can give recommendations based on similarity of features on audio signal. This study uses convolutional recurrent neural network (CRNN) for feature extraction and similarity distance to look similarity between features. The results of this study indicate that users prefer recommendations that consider music genres compared to recommendations based solely on similarity.
Keywords: Music Recommender System | Convolutional Recurrent Neural Network | Similarity Distance