Musiac Adeegga Talo Bixinta Muusikada oo uu sameeyey Chia-Min Lin

Naqshadda Musiac Adeegga Talo Bixinta Muusikada oo uu sameeyey Chia-Min Lin waa mid aad u sharaf badan oo ku guulaysatay tartanka qaybta Naqshadeynta Isdhexgalka, Iskaashiga iyo Waayo-aragnimada Isticmaalaha ee 2020 Abaalmarinta Lacagta ah ee A' Design Award.

LacagIsdhexgal· 2020

Musiac Adeegga Talo Bixinta Muusikada

Chia-Min Lin uu sameeyay

Macmiilka: LinStudio

Ku Guuleystaha Abaalmarinta Lacagta ah ee A' Design Award ee Qaybta Isdhexgal

57,305

Ku Guuleystaha Abaalmarinta La Xaqiijiyey

Xaqiiji Xaaladda Abaalmarinta #91014

Ka Eeg A' Design Award War-saxaafadeed Calaamadda Abaalmarinta

Midabada

Naqshadeynta

Wax ku saabsan Musiac Adeegga Talo Bixinta Muusikada

Musiac waa matoor talo bixin muusig ah, ka faa'iideyso kaqeybgal firfircoon si aad u hesho ikhtiyaar sax ah adeegsadeyaasheeda. Waxay ujeeddadeedu tahay inay soo jeediso meelo kale oo lagu beddelo si looga hortago is-qoritaanka algorithm. Shaandheynta macluumaadka ayaa noqotay qaab raadis lama huraan ah. Si kastaba ha noqotee, waxay abuurtaa saamaynta aqalada waxayna ku xayirtaa isticmaalayaasha aaggaga raaxada iyagoo si adag u raacaaya doorbidooda. Isticmaalayaashu waxay noqdaan kuwa aan toos ahayn oo joojiya su'aalaha ay kala dooran karaan mashiinku Waqti u luminta dib u eegista ikhtiyaaraadka ayaa kordhin kara qiimaha noolaha, laakiin waa dadaalka abuuraya khibrad macno leh.

Aqoonsiga

Wax ku saabsan Abaalmarinta Lacagta ah ee A' Design Award

Musiac Adeegga Talo Bixinta Muusikada waa guuleystaha Abaalmarinta Lacagta ah ee A' Design Award, aqoonsi sharaf aad u sarreeya oo la siiyo naqshado heerka ugu sarreeya oo quruxsan oo soo jiidasho leh. Guuleystayaasha Lacagta waa shaqooyin aad u dhalaalaya iyo hal-abuur sare leh oo muujiya khibrad, hibo iyo hufnaan naqshadeeyayaashooda. Musiac Adeegga Talo Bixinta Muusikada waa shaqo la filayo inay noqoto shaqo quruxsan oo tusaale ah, xirfad cad leh oo hal-abuur qoto dheer leh. Naqshadahan ku guuleystay Abaalmarinta Lacagta ah ee A' Design Award waa hal-abuur aad loo dareemo oo la ammaano, waxtar leh oo hormariya xuduudaha farshaxanka, sayniska, naqshadaynta iyo tignoolajiyada. Musiac Adeegga Talo Bixinta Muusikada waa hal-abuur dad badan ay doonayaan, kicisa rabitaanno, keena dareenno wanaagsan oo abuura yaab.

Gadaasha Naqshadeynta

Xirmada Warbaahinta

Shahaadooyinka, xubnaha kooxda iyo faahfaahinta farsamada ee uu bixiyay naqshadeeluhu.

Sifooyinka Gaarka ah: Musiac is a music recommendation service aims to provide better selection mechanism. It uses a contour map to represent all musical genres in a single interface, showing the relationship across songs and genres in a 2D space. It maps out a taste bubble from the user's playlists, separating the user's prior musical taste field from the unexplored musical territory. The user can change the outline of the bubble by dragging it and expanding the scope of the recommendation algorithm.
Cabbirka & Qaabaynta: Musiac is a desktop application. It requires a user to import his/her playlists of third-party apps to build the "Taste Bubble" at the beginning. The bubble sits on an algorithmically-generated genres map, with a horizontal (electric, acoustic) and a vertical (atmospheric, bouncier) indication. The bubble separates the familiar and unexplored territory for a user so he/she could expand the recommendation (temporarily), without changing their behavior.
Summado: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Tiknoloojiyada Soo Saarka: Musiac learns from "Every Noise at Once", has a virtual database, a theoretical possible algorithmically-generated map, and an ethical interface to reduce the bio-cost of finding liked songs from enormous options. It starts with a study of recommendation engines and filters, interviews, and a pre-testing, then ideates with "what if" questions along the generative design process, run through 2 round of user-testing to validate initial concepts and iterates. The project took a year to finalize.
Caqabadda Naqshadda: The most difficult part of this project is to find an industry professional to talk to me. Online streaming service is a very competitive field. Hence, business and advertising dominate the final result of music recommendations, which creates intransparent and unethical algorithms. Academics and scholars provide enthusiastic feedback, but one of the business executives cancel his help after he saw my questions list.
Another major research constrains is the fuzzy relationships of million users' playlists. Single users' taste bubble might not accommodate with the overarching dimensions, even though the machine could easily map his or her playlists into an individual lump. After I consulted with the famous founder of EveryNoise, he gives me great advice that I should have customized dimensions for different users to solve this issue. The system could map out the contour map from each individual users perspective, based on what's the location of the taste bubble, then decide how to show the rest of the genres. Despite the computation power is not efficient enough for this project, but this solution is definitely feasible at the nearby future.
Cilmi-baadhis: Musiac ran with a waterfall and agile hybrid methodology: research, framing, prototype, test, and reflecting. Feedback was collected along the way and therefore, multiple iterations. I did solid case studies, interviewed active and passive music online streaming users, and consulted with programmers to finalize the proper frame of this project. Multiple user testings with a clickable prototype were performed in-person, as well as online.
Dhiirrigelinta: Musiac started with a simple notion. Interface is the tangent point between our perception and reality. I believe interface reflects a subtle world of why we make choices. I ran into a website called Every Noise at Once and found it's a perfect database to test my theory. Interface has a higher-level goal of helping us to process information better. I want to design something that goes beyond data visualization and empower users to be more proactive upon the world they're seeing.
Muddada Mashruuca: Musiac was a one year project done in 2018, Detroit.
Socodka Hawlgalka: Musiac is a desktop application with an unusual interface, allows users to control the scope of recommendations and to manipulate the filtered results. It uses a bubble to separate the unexplored genres from the familiar genres. A user could easily change the shape of the bubble to expand the recommendation source. It also provides multiple filters, such as themes with different adjectives; numbers of views, finding songs with specific traits, and a guiding system to go beyond the comfort zone.
Amaanta Sawirrada: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.