Musiac Ntchito Yovomerezera Nyimbo ndi Chia-Min Lin

Dizayini ya Musiac Ntchito Yovomerezera Nyimbo ndi Chia-Min Lin ndi wopambana wotamandika kwambiri pa mphotho yaulemu ya 2020 Mphoto ya Siliva ya A' Design Award mu Gawo la Mpikisano la Mapangidwe a Mawonekedwe, Kulumikizana ndi Zokumana ndi Ogwiritsa Ntchito.

SilivaMawonekedwe· 2020

Musiac Ntchito Yovomerezera Nyimbo

ndi Chia-Min Lin

Kasitomala: LinStudio

Wopambana pa Mphoto ya Siliva ya A' Design Award mu Gawo la Mawonekedwe

57,305

Wopambana Wotsimikizidwa

Tsimikizani Mkhalidwe wa Mphotho ya #91014

Onani ku A' Design Award Nkhani Yofalitsidwa Chizindikiro cha Mphotho

Mitundu

Kapangidwe

Za Musiac Ntchito Yovomerezera Nyimbo

Musiac ndi injini yotsatsira nyimbo, gwiritsani ntchito zoyeserera kuti mupeze zosankha zowona za ogwiritsa ntchito. Cholinga changa ndikufotokozera njira zina zomwe zingatsutsane ndi algorithm autocracy. Kufufuza zidziwitso tsopano kwakhala njira yosakira. Komabe, zimapangitsa zotsatira za chipinda cha echo ndipo zimapangitsa ogwiritsa ntchito kukhala m'malo awo achitetezo mwakutsata zomwe amakonda. Ogwiritsa ntchito amangokhala osasiya kufunsa zomwe makina amapereka. Kuwononga nthawi yowunikira njira zina kumathandizira kuwonjezera mtengo wa bio, koma ndi kuyesetsa komwe kumapangitsa chidziwitso chofunikira.

Chidziwitso

Za Mphoto ya Siliva ya A' Design Award

Musiac Ntchito Yovomerezera Nyimbo ndi mmodzi mwa opambana a Mphoto ya Siliva ya A' Design Award, kuzindikirika kolemekezeka modabwitsa komwe kumaperekedwa kwa mapangidwe a pamwamba, okongola ndi ochititsa chidwi. Opambana a Siliva ndi ntchito zowala kwambiri ndi zolenga kwambiri zomwe zimaonetsa ukatswiri, luso ndi nzeru zakuthwa za opanga awo. Musiac Ntchito Yovomerezera Nyimbo akuyembekezeka kukhala ntchito yodabwitsa yoonetsedwa, yaukatswiri wapadera ndi yatsopano kwambiri. Mapangidwe opambana Mphoto ya Siliva ya A' Design Award awa ndi chopangidwa chodziwika kwambiri ndi choyamikiridwa chomwe chimapindulitsa ndi kukulitsa malire a luso, sayansi, kapangidwe ndi ukadaulo. Musiac Ntchito Yovomerezera Nyimbo ndi cholengedwa chomwe anthu ambiri amachichitira nsanje, chomwe chimadzutsa zilakolako, kubweretsa maganizo abwino ndi kuyatsa kudabwa ndi chidwi.

Kumbuyo kwa Kapangidwe

Katundu wa Atolankhani

Umboni, mamembala a gulu ndi zambiri zapadera zoperekedwa ndi wopanga.

Makhalidwe Apadera: 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.
Miyezo ndi Kuphimba: 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.
Zizindikiro: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Teknoloji Yopangira: 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.
Vuto la Dizayini: 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.
Kafukufuku: 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.
Chilimbikitso: 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.
Nthawi ya Polojekiti: Musiac was a one year project done in 2018, Detroit.
Mdondomeko ya Ntchito: 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.
Mwini Zithunzi: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.