Music feature extraction and analysis through Python

En l'era digital, plataformes com Spotify s'han convertit en els principals canals de consum de música, ampliant les possibilitats per analitzar i entendre la música a través de les dades. Aquest projecte es centra en un examen exhaustiu d'un conjunt de dades obtingut de Spotify, utilitzant Python com a eina per a l'extracció i anàlisi de dades. L'objectiu principal es centra en la creació d'aquest conjunt de dades, emfatitzant una àmplia varietat de cançons de diversos subgèneres. La intenció és representar tant el panorama musical més tendenciós i popular com els nínxols, alineant-se amb el concepte de distribució de Cua Llarga, terme popularitzat com a "Long Tail" en anglès, que destaca el potencial de mercat de productes de nínxols amb menor popularitat. A través de l'anàlisi, es posen de manifest patrons en l'evolució de les característiques musicals al llarg de les dècades passades. Canvis en característiques com l'energia, el volum, la capacitat de ball, el positivisme que desprèn una cançó i la seva correlació amb la popularitat sorgeixen del conjunt de dades. Paral·lelament a aquesta anàlisi, es concep un sistema de recomanació musical basat en el contingut del conjunt de dades creat. L'objectiu és connectar cançons, especialment les menys conegudes, amb possibles oients. Aquest projecte ofereix perspectives beneficioses per a entusiastes de la música, científics de dades i professionals de la indústria. Les metodologies implementades i l'anàlisi realitzat presenten un punt de convergència de la ciència de dades i la indústria de la música en el context digital actualEn la era digital, plataformas como Spotify se han convertido en los principales canales de consumo de música, ampliando las posibilidades para analizar y entender la música a través de los datos. Este proyecto se centra en un examen exhaustivo de un conjunto de datos obtenido de Spotify, utilizando Python como herramienta para la extracción y análisis de datos. El objetivo principal se centra en la creación de este conjunto de datos, enfatizando una amplia variedad de canciones de diversos subgéneros. La intención es representar tanto el panorama musical más tendencioso y popular como los nichos, alineándose con el concepto de distribución de Cola Larga, término popularizado como Long Tail en inglés, que destaca el potencial de mercado de productos de nichos con menor popularidad. A través del análisis, se evidencian patrones en la evolución de las características musicales a lo largo de las décadas pasadas. Cambios en características como la energía, el volumen, la capacidad de baile, el positivismo que desprende una canción y su correlación con la popularidad surgen del conjunto de datos. Paralelamente a este análisis, se concibe un sistema de recomendación musical basado en el contenido del conjunto de datos creado. El objetivo es conectar canciones, especialmente las menos conocidas, con posibles oyentes. Este proyecto ofrece perspectivas beneficiosas para entusiastas de la música, científicos de datos y profesionales de la industria. Las metodologías implementadas y el análisis realizado presentan un punto de convergencia de la ciencia de datos y la industria de la música en el contexto digital actualIn the digital era, platforms like Spotify have become the primary channels of music consumption, broadening the possibilities for analyzing and understanding music through data. This project focuses on a comprehensive examination of a dataset sourced from Spotify, with Python as the tool for data extraction and analysis. The primary objective centers around the creation of this dataset, emphasizing a diverse range of songs from various subgenres. The intention is to represent both mainstream and niche musical landscapes, aligning with the Long Tail distribution concept, which highlights the market potential of less popular niche products. Through analysis, patterns in the evolution of musical features over past decades become evident. Shifts in features such as energy, loudness, danceability, and valence and their correlation with popularity emerge from the dataset. Parallel to this analysis is the conceptualization of a music recommendation system based on the content of the data set. The aim is to connect tracks, especially lesser-known ones, with potential listeners. This project provides insights beneficial for music enthusiasts, data scientists, and industry professionals. The methodologies and analyses present a convergence of data science and the music industry in today's digital contex

Finding video on the web

At present very little is known about how people locate and view videos. This study draws a rich picture of everyday video seeking strategies and video information needs, based on an ethnographic study of New Zealand university students. These insights into the participants’ activities and motivations suggest potentially useful facilities for a video digital library

Feature Augmentation for Improved Topic Modeling of Youtube Lecture Videos using Latent Dirichlet Allocation

Application of Topic Models in text mining of educational data and more specifically, the text data obtained from lecture videos, is an area of research which is largely unexplored yet holds great potential. This work seeks to find empirical evidence for an improvement in Topic Modeling by pre- extracting bigram tokens and adding them as additional features in the Latent Dirichlet Allocation (LDA) algorithm, a widely-recognized topic modeling technique. The dataset considered for analysis is a collection of transcripts of video lectures on Machine Learning scraped from YouTube. Using the cosine similarity distance measure as a metric, the experiment showed a statistically significant improvement in topic model performance against the baseline topic model which did not use extra features, thus confirming the hypothesis. By introducing explainable features before modeling and using deep learning based text representation only at the post-modeling evaluation stage, the overall model interpretability is retained. This empowers educators and researchers alike to not only benefit from the LDA model in their own fields but also to play a substantial role in eorts to improve model performance. It also sets the direction for future work which could use the feature augmented topic model as the input to other more common text mining tasks like document categorization and information retrieval

Song Recommendation for Automatic Playlist Continuation

The goal of this project is to develop a recommender system that derives song recommendations from an implicit music dataset provided by the streaming service Spotify. We implemented current baseline systems and then two advancements over the baselines: Feature Enhanced Matrix Factorization and Non-Linear Matrix Factorization. To compare these systems, we took the predicted songs for a given playlist and calculated the performance score based on the accuracy of those results. We then compared the results from these NDCG scores to determine which system performed the best for the given Spotify dataset. Based off of the results, we were able to draw conclusions regarding the design process for an effective recommender system for music data

Determinants in the on-line distribution of digital content: an exploratory analysis

This article shows the phases – and discusses the results – of an empirical analysis addressing the legal business models that are adopted online to distribute digital content

Carousel Personalization in Music Streaming Apps with Contextual Bandits

Media services providers, such as music streaming platforms, frequently leverage swipeable carousels to recommend personalized content to their users. However, selecting the most relevant items (albums, artists, playlists...) to display in these carousels is a challenging task, as items are numerous and as users have different preferences. In this paper, we model carousel personalization as a contextual multi-armed bandit problem with multiple plays, cascade-based updates and delayed batch feedback. We empirically show the effectiveness of our framework at capturing characteristics of real-world carousels by addressing a large-scale playlist recommendation task on a global music streaming mobile app. Along with this paper, we publicly release industrial data from our experiments, as well as an open-source environment to simulate comparable carousel personalization learning problems.Comment: 14th ACM Conference on Recommender Systems (RecSys 2020, Best Short Paper Candidate

Exploratory Browsing

In recent years the digital media has influenced many areas of our life. The transition from analogue to digital has substantially changed our ways of dealing with media collections. Today‟s interfaces for managing digital media mainly offer fixed linear models corresponding to the underlying technical concepts (folders, events, albums, etc.), or the metaphors borrowed from the analogue counterparts (e.g., stacks, film rolls). However, people‟s mental interpretations of their media collections often go beyond the scope of linear scan. Besides explicit search with specific goals, current interfaces can not sufficiently support the explorative and often non-linear behavior. This dissertation presents an exploration of interface design to enhance the browsing experience with media collections. The main outcome of this thesis is a new model of Exploratory Browsing to guide the design of interfaces to support the full range of browsing activities, especially the Exploratory Browsing. We define Exploratory Browsing as the behavior when the user is uncertain about her or his targets and needs to discover areas of interest (exploratory), in which she or he can explore in detail and possibly find some acceptable items (browsing). According to the browsing objectives, we group browsing activities into three categories: Search Browsing, General Purpose Browsing and Serendipitous Browsing. In the context of this thesis, Exploratory Browsing refers to the latter two browsing activities, which goes beyond explicit search with specific objectives. We systematically explore the design space of interfaces to support the Exploratory Browsing experience. Applying the methodology of User-Centered Design, we develop eight prototypes, covering two main usage contexts of browsing with personal collections and in online communities. The main studied media types are photographs and music. The main contribution of this thesis lies in deepening the understanding of how people‟s exploratory behavior has an impact on the interface design. This thesis contributes to the field of interface design for media collections in several aspects. With the goal to inform the interface design to support the Exploratory Browsing experience with media collections, we present a model of Exploratory Browsing, covering the full range of exploratory activities around media collections. We investigate this model in different usage contexts and develop eight prototypes. The substantial implications gathered during the development and evaluation of these prototypes inform the further refinement of our model: We uncover the underlying transitional relations between browsing activities and discover several stimulators to encourage a fluid and effective activity transition. Based on this model, we propose a catalogue of general interface characteristics, and employ this catalogue as criteria to analyze the effectiveness of our prototypes. We also present several general suggestions for designing interfaces for media collections

Social software for music

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200