KQED61%

Muni Music Turns Buses and Trains Into a Unique Musical Composition 6%

By Azul Dahlstrom-Eckman30%

6/15/2026, 4:00:32 AM

BS Summary: This article contains 26 faulty reasoning types, including Hasty Generalization, Biased Writer Voice, and Availability Heuristic, with Framing Effect as the most egregious example at 21.6% saturation with 168 hits. Analysis detected 1,007 faulty-reasoning hits from 776 analyzed words, generating a BS Score of 22.4% and a BS Rank of 6% (20,579 of 21,887 articles). This article is better (less manipulative) than 94.00% of the article peer group.

In Robert Burns’ world, the Powell-Mason Cable Car is heralded by a flute and a tubular bell. 
The M-Ocean View carries a soft mallet and a sub bass. 
The N-Judah is a marimba and a bass pizzicato. 
Taken altogether, the generative composition creates a lo-fi, sonic interpretation of the Bay Area’s most-ridden transit service, San Francisco’s Muni. 
And it’s available for anyone to listen to. 
“I thought to myself, what if I turned Muni into an instrument?” 
said Burns, creator of the site, munimusic.com 
The site shows a map of San Francisco, and the real-time location of the more than 500 Muni trains, buses and cable cars that could be on the street at any one time. 
Each vehicle plays a unique pair of sounds based on its position and route and a chime when they arrive at a stop. 
Visitors can watch and listen to Muni vehicles plug along in real time, hear when they arrive and revel in an ambient interpretation of public transit. 
For Burns, an IT professional, and a more than 30-year San Franciscan and a Muni rider, the project is part tribute, part natural inclination to experiment with technology. 
For fans of Muni, it’s the latest manifestation of local pride in the transit service that’s taken varied forms, from branded merchandise to trivia nights to riding routes for fun. 
Burns used publicly available data to create the map and then made digital instruments to pair with the routes. 
He said he’s had the Muni Music domain since 2002, but only launched the website in April, after “many, many iterations.” 
An initial version was rhythm-based and sounded more like a drum circle. 
And the sheer volume of Muni’s buses broke his browser. 
The site currently logs about five visits a week. 
“If this actually becomes something that people used, I would be amazed,” Burns said. 
Burns isn’t the first person to look at a transit map and think: Could this be music? 
Take Train Jazz  a similar website, created by a New York City resident, which turns that city’s transit agency into a jazz ensemble. 
Another website based on New York City’s transit map, called MTA.me, only plays notes when trains cross paths, like plucking strings. 
And last month a group of artists debuted a sculpture that converts BART’s train data into sound using a tube and a heating element. 
For the Bay Area-based composer Mason Bates, these kinds of projects, where people convert data into music, might best be called public sound art. 
“It’s not really about whether the resulting artwork is particularly good or beautiful; it’s more about finding fun ways for the public to learn about some kind of initiative, whether it be NASA space data, or in this case, Muni data,” Bates said. 
Bates said rather than getting hung up on the quality of the music, the purpose of these sites is to use digital tools to make data more digestible. 
By sonifying transit data, these projects allow listeners to experience the entirety of a transit system all at once. 
“We are swimming in data these days, right? 
So translating it in some way that can be fun or artistic is a new thing that’s happening,” he said. 
“This brings the public in to engage with a non-artistic enterprise in an artistic way.” 
In Muni Music, each moment is different from the next, as the number of Muni vehicles on the road  and their position  fluctuate throughout the day. 
If trains are predominantly in the west end of the city, like the L-Taraval, sound will come predominantly out of the left side of a pair of headphones. 
The opposite is true for the T-Third Street, which runs on the east side of the city. 
“Seeing the volume of vehicles that are out there at any given moment shows people how active the system is and how frequent service is. 
And when it’s all played together, we’re really picking people up and dropping them off at a really quick rate,” SFMTA spokesperson Michael Roccaforte said. 
Burns said he sees a relationship between his job in IT and managing a public transit agency: two fields that don’t get much praise, but get a lot of attention when things go wrong. 
“It’s an homage. 
It’s kinda like, ‘Hey, thanks, Muni, thanks for being there, and here’s my little attempt at giving something back,’” Burns said. 
There’s some utility to the website as well. 
Burns used it the other day to check when the next train was coming, and then he rode home with his own Muni soundtrack. 
Article reasoning-pattern comparisonThis article: 3.7%Azul Dahlstrom-Eckman: 1.6%CalMatters: 1.9%Confirmation Bias3.7%This article: 0.0%Azul Dahlstrom-Eckman: 1.2%CalMatters: 0.9%Anchoring Bias0.0%This article: 6.4%Azul Dahlstrom-Eckman: 3.0%CalMatters: 3.0%Availability Heuristic6.4%This article: 2.2%Azul Dahlstrom-Eckman: 0.8%CalMatters: 1.0%Representativeness Heuristic2.2%This article: 0.0%Azul Dahlstrom-Eckman: 0.3%CalMatters: 0.5%Hindsight Bias0.0%This article: 2.4%Azul Dahlstrom-Eckman: 1.1%CalMatters: 1.2%Overconfidence Bias2.4%This article: 21.6%Azul Dahlstrom-Eckman: 5.2%CalMatters: 6.3%Framing Effect21.6%This article: 0.0%Azul Dahlstrom-Eckman: 1.4%CalMatters: 1.0%Loss Aversion0.0%This article: 0.0%Azul Dahlstrom-Eckman: 1.1%CalMatters: 0.7%Status Quo Bias0.0%This article: 2.7%Azul Dahlstrom-Eckman: 0.3%CalMatters: 0.2%Sunk Cost Effect2.7%This article: 3.6%Azul Dahlstrom-Eckman: 3.1%CalMatters: 3.5%Optimism Bias3.6%This article: 1.8%Azul Dahlstrom-Eckman: 1.0%CalMatters: 1.4%Pessimism Bias1.8%This article: 5.7%Azul Dahlstrom-Eckman: 4.6%CalMatters: 6.4%Negativity Bias5.7%This article: 2.7%Azul Dahlstrom-Eckman: 1.3%CalMatters: 1.7%Self-Serving Bias2.7%This article: 0.0%Azul Dahlstrom-Eckman: 0.7%CalMatters: 0.7%Fundamental Attribution Error0.0%This article: 3.6%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.2%Actor-Observer Bias3.6%This article: 3.9%Azul Dahlstrom-Eckman: 1.1%CalMatters: 1.7%In-Group Bias3.9%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.4%Out-Group Homogeneity Bias0.0%This article: 4.0%Azul Dahlstrom-Eckman: 1.1%CalMatters: 2.7%Halo Effect4.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.1%Horn Effect0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.0%Dunning-Kruger Effect0.0%This article: 5.7%Azul Dahlstrom-Eckman: 0.7%CalMatters: 0.9%Recency Bias5.7%This article: 0.0%Azul Dahlstrom-Eckman: 0.2%CalMatters: 0.3%Primacy Effect0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.0%Blind-Spot Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.6%Ad Hominem0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.2%Straw Man0.0%This article: 3.1%Azul Dahlstrom-Eckman: 2.5%CalMatters: 3.1%Appeal to Authority3.1%This article: 5.5%Azul Dahlstrom-Eckman: 1.1%CalMatters: 1.1%False Dilemma5.5%This article: 0.0%Azul Dahlstrom-Eckman: 0.4%CalMatters: 0.8%Slippery Slope0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.2%CalMatters: 0.1%Circular Reasoning0.0%This article: 12.2%Azul Dahlstrom-Eckman: 2.5%CalMatters: 3.6%Hasty Generalization12.2%This article: 0.0%Azul Dahlstrom-Eckman: 0.3%CalMatters: 0.2%Red Herring0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.3%CalMatters: 0.7%Bandwagon0.0%This article: 2.7%Azul Dahlstrom-Eckman: 4.6%CalMatters: 5.3%Appeal to Emotion2.7%This article: 0.0%Azul Dahlstrom-Eckman: 0.5%CalMatters: 0.6%Begging the Question0.0%This article: 0.0%Azul Dahlstrom-Eckman: 1.5%CalMatters: 2.0%Post Hoc (False Cause)0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.1%Tu Quoque0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.2%CalMatters: 0.3%Burden of Proof0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.2%Appeal to Nature0.0%This article: 2.4%Azul Dahlstrom-Eckman: 0.2%CalMatters: 0.2%Composition/Division2.4%This article: 6.3%Azul Dahlstrom-Eckman: 4.5%CalMatters: 3.1%Anecdotal6.3%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.0%No True Scotsman0.0%This article: 3.6%Azul Dahlstrom-Eckman: 1.0%CalMatters: 1.2%Ambiguity (Equivocation)3.6%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.2%CalMatters: 0.1%Middle Ground0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.1%Personal Incredulity0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.1%Special Pleading0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.1%Genetic Fallacy0.0%This article: 1.5%Azul Dahlstrom-Eckman: 1.4%CalMatters: 0.8%Unattributed Quote1.5%This article: 1.5%Azul Dahlstrom-Eckman: 0.7%CalMatters: 0.7%Quote-first Misdirection1.5%This article: 9.3%Azul Dahlstrom-Eckman: 2.5%CalMatters: 3.1%Biased Writer Voice9.3%This article: 5.5%Azul Dahlstrom-Eckman: 1.1%CalMatters: 1.9%Indoctrination5.5%This article: 0.0%Azul Dahlstrom-Eckman: 0.4%CalMatters: 1.1%Politically Left Leaning Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.1%Politically Right Leaning Bias0.0%This article: 5.8%Azul Dahlstrom-Eckman: 1.0%CalMatters: 1.2%Attempt to Sell a Product or S…5.8%

776 words analyzed.

Speakers

3speakers33%attributed speech521writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 100.0% coverageWriter's voice • 17 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageRobert Burns • 12 words • 100.0% coverageRobert Burns • 7 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 26 words • 100.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 30 words • 100.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageRobert Burns • 14 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageMason Bates • 43 words • 0.0% coverageMason Bates • 28 words • 100.0% coverageWriter's voice • 19 words • 0.0% coverageMason Bates • 8 words • 0.0% coverageMason Bates • 20 words • 0.0% coverageMason Bates • 15 words • 100.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageMichael Roccaforte • 25 words • 0.0% coverageMichael Roccaforte • 25 words • 100.0% coverageRobert Burns • 34 words • 0.0% coverageRobert Burns • 3 words • 0.0% coverageRobert Burns • 21 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverage
Selected voice

Mason Bates

100%flagged-word coverage
114 attributed words45% of attributed speech69% writer coverage
0%20.0%40.0%Indoctrination+37.7 ptsWriter: 0.0%Mason Bates: 37.7%37.7%Biased Writer Voice-9.0 ptsWriter: 9.0%Mason Bates: 0.0%0.0%Attempt to Sell a Product -8.6 ptsWriter: 8.6%Mason Bates: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.