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

Ambient Intelligence with Wireless Grid Enabled Applications: A Case Study of the Launch and First Use Experience of WeJay Social Radio in Education

Wireless grid and ambient intelligent (AmI) environments are characterized as supportive of collaboration, interaction, and sharing. The conceptual framework advanced for this study incorporated the constructs of innovation, creativity and context awareness while offering emergence theory -- emergent properties, structures, patterns and behaviors -- to frame and investigate a wireless grid enabled social radio application which was theorized to be potentially transformative and disruptive. The unintended consequences and unexpected possibilities of wireless grid and smart environments were also addressed. Using a single case study, drawing upon multiple data collection methods, this research investigated the deployment and use experience of WeJay, an application incubated through the Wireless Grids Innovation Testbed (WiGiT), from the perspective of beta trial participants. Guided by the broad research question -- Do wireless grid enabled applications, such as WeJay social radio, add to the potential for new and transformative outcomes for people, information and technology when deployed in an academic setting? -- this empirical study sought to: a) learn more about the launch experience of this first pre-standards wireless grid enabled application among WiGiT members and selected Syracuse University students and faculty; b) understand how this application was interpreted for use; c) determine whether novel and unexpected uses emerged; d) investigate whether wireless grid enabled environments fostered innovation and creativity; and e) elicit whether a conceptual relationship was emerging between wireless grid and AmI environments, focusing on context-awareness and ambient learning. While this early stage of diffusion and first user sample was a key limitation of the study it was also the core strength. Although challenged by the state of readiness of WeJay, study findings supported the propositions that WeJay fosters innovation and creativity; that novel and unexpected uses were generated; and that the theorized relationship between wireless grid applications and embedded awareness does exist. Recommendations for enhanced tool readiness were made and embedded smartness was found to be both desirable and beneficial. This research makes a contribution as a bridge study for future research while having theoretical and methodological implications for research and practice. Social, emotion/affect, and human-centered computing (HCC) dimensions emerged as rich areas for further research

Human-AI complex task planning

The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. In trip planning, sub-tasks are points of interest (POIs) and constraints represent time and monetary budget, or user-specified requirements. Needless to say, task plans are to be individualized and designed considering uncertainty. When done manually, the process is human-intensive and tedious, and unlikely to scale. The goal of this dissertation is to present computational frameworks that synthesize the capabilities of human and AI algorithms to enable task planning at scale while satisfying multiple objectives and complex constraints. This dissertation makes significant contributions in four main areas, (i) proposing novel models, (ii) designing principled scalable algorithms, (iii) conducting rigorous experimental analysis, and (iv) deploying designed solutions in the real-world. A suite of constrained and multi-objective optimization problems has been formalized, with a focus on their applicability across diverse domains. From an algorithmic perspective, the dissertation proposes principled algorithms with theoretical guarantees adapted from discrete optimization techniques, as well as Reinforcement Learning based solutions. The memory and computational efficiency of these algorithms have been studied, and optimization opportunities have been proposed. The designed solutions are extensively evaluated on various large-scale real-world and synthetic datasets and compared against multiple baseline solutions after appropriate adaptation. This dissertation also presents user study results involving human subjects to validate the effectiveness of the proposed models. Lastly, a notable outcome of this dissertation is the deployment of one of the developed solutions at the Naval Postgraduate School. This deployment enables simultaneous route planning for multiple assets that are robust to uncertainty under multiple contexts

The Harkive Project: Popular Music, Data & Digital Technologies

This thesis is about research around Harkive, an online project designed by this researcher, which gathers stories, reflections, and other data from people about their everyday engagement with popular music. Since 2013, over 1,000 people have contributed to the project, producing around 8,000 texts and highlighting the music reception activities of contemporary music listeners. The thesis presents an analysis of the texts and other data generated, answering a key research question: What can an analysis of the data generated by The Harkive Project reveal about the music reception practices of respondents? To answer this question, the researcher developed an experimental, innovative approach that conceives of Harkive as a space in which people can reflect upon their engagement with music, whilst simultaneously acting as a place that is able to replicate many of the commercial practices related to data collection and processing that have recently emerged as influential factors in the ways that popular music is produced, distributed and consumed. By focusing on a set of findings about the way people reflect on their engagement with music within the Harkive space, this thesis engages practically and critically with these new conditions. Simultaneously, the research explores how the systems of data collection and analysis that facilitate this are technologically complex, subject to rapid change, and often hidden behind commercial and legal firewalls, making the study of them particularly difficult. This then enables us to explore how the use of digital, data and Internet technologies by many people during the course of their everyday lives is providing scholars with new opportunities and methods for undertaking research in the humanities, and how this in turn is leading to questions about the role of the researcher in popular music studies, and how the discipline may take into account the new technologies and practices that have so changed the field. Ultimately, the thesis makes the argument that a greater practical understanding and critical engagement with digital, data and Internet technologies is essential, both for music consumers and popular music scholars, and demonstrates how this work represents a significant contribution to this task

Binaural virtual auditory display for music discovery and recommendation

Emerging patterns in audio consumption present renewed opportunity for searching or navigating music via spatial audio interfaces. This thesis examines the potential benefits and considerations for using binaural audio as the sole or principal output interface in a music browsing system. Three areas of enquiry are addressed. Specific advantages and constraints in spatial display of music tracks are explored in preliminary work. A voice-led binaural music discovery prototype is shown to offer a contrasting interactive experience compared to a mono smartspeaker. Results suggest that touch or gestural interaction may be more conducive input modes in the former case. The limit of three binaurally spatialised streams is identified from separate data as a usability threshold for simultaneous presentation of tracks, with no evident advantages derived from visual prompts to aid source discrimination or localisation. The challenge of implementing personalised binaural rendering for end-users of a mobile system is addressed in detail. A custom framework for assessing head-related transfer function (HRTF) selection is applied to data from an approach using 2D rendering on a personal computer. That HRTF selection method is developed to encompass 3D rendering on a mobile device. Evaluation against the same criteria shows encouraging results in reliability, validity, usability and efficiency. Computational analysis of a novel approach for low-cost, real-time, head-tracked binaural rendering demonstrates measurable advantages compared to first order virtual Ambisonics. Further perceptual evaluation establishes working parameters for interactive auditory display use cases. In summation, the renderer and identified tolerances are deployed with a method for synthesised, parametric 3D reverberation (developed through related research) in a final prototype for mobile immersive playlist editing. Task-oriented comparison with a graphical interface reveals high levels of usability and engagement, plus some evidence of enhanced flow state when using the eyes-free binaural system

A mood-based music classification and exploration system

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 89-93).Mood classification of music is an emerging domain of music information retrieval. In the approach presented here features extracted from an audio file are used in combination with the affective value of song lyrics to map a song onto a psychologically based emotion space. The motivation behind this system is the lack of intuitive and contextually aware playlist generation tools available to music listeners. The need for such tools is made obvious by the fact that digital music libraries are constantly expanding, thus making it increasingly difficult to recall a particular song in the library or to create a playlist for a specific event. By combining audio content information with context-aware data, such as song lyrics, this system allows the listener to automatically generate a playlist to suit their current activity or mood.by Owen Craigie Meyers.S.M

Prediction, evolution and privacy in social and affiliation networks

In the last few years, there has been a growing interest in studying online social and affiliation networks, leading to a new category of inference problems that consider the actor characteristics and their social environments. These problems have a variety of applications, from creating more effective marketing campaigns to designing better personalized services. Predictive statistical models allow learning hidden information automatically in these networks but also bring many privacy concerns. Three of the main challenges that I address in my thesis are understanding 1) how the complex observed and unobserved relationships among actors can help in building better behavior models, and in designing more accurate predictive algorithms, 2) what are the processes that drive the network growth and link formation, and 3) what are the implications of predictive algorithms to the privacy of users who share content online. The majority of previous work in prediction, evolution and privacy in online social networks has concentrated on the single-mode networks which form around user-user links, such as friendship and email communication. However, single-mode networks often co-exist with two-mode affiliation networks in which users are linked to other entities, such as social groups, online content and events. We study the interplay between these two types of networks and show that analyzing these higher-order interactions can reveal dependencies that are difficult to extract from the pair-wise interactions alone. In particular, we present our contributions to the challenging problems of collective classification, link prediction, network evolution, anonymization and preserving privacy in social and affiliation networks. We evaluate our models on real-world data sets from well-known online social networks, such as Flickr, Facebook, Dogster and LiveJournal

Trick or Heat? Manipulating Critical Temperature-Based Control Systems Using Rectification Attacks

Temperature sensing and control systems are widely used in the closed-loop control of critical processes such as maintaining the thermal stability of patients, or in alarm systems for detecting temperature-related hazards. However, the security of these systems has yet to be completely explored, leaving potential attack surfaces that can be exploited to take control over critical systems. In this paper we investigate the reliability of temperature-based control systems from a security and safety perspective. We show how unexpected consequences and safety risks can be induced by physical-level attacks on analog temperature sensing components. For instance, we demonstrate that an adversary could remotely manipulate the temperature sensor measurements of an infant incubator to cause potential safety issues, without tampering with the victim system or triggering automatic temperature alarms. This attack exploits the unintended rectification effect that can be induced in operational and instrumentation amplifiers to control the sensor output, tricking the internal control loop of the victim system to heat up or cool down. Furthermore, we show how the exploit of this hardware-level vulnerability could affect different classes of analog sensors that share similar signal conditioning processes. Our experimental results indicate that conventional defenses commonly deployed in these systems are not sufficient to mitigate the threat, so we propose a prototype design of a low-cost anomaly detector for critical applications to ensure the integrity of temperature sensor signals.Comment: Accepted at the ACM Conference on Computer and Communications Security (CCS), 201