Small Fire In the Night Madrid Driftwood Hungry Ghost Salt Spring Island On Forgetting Polaris Castaways From the Shore Moving Closer The Factory Midnight Sun The Seamstress When the Sun Sighs Golden Like a Soldier The Butterfly Solid Ground Shelter A Thousand Empty Rooms Solitude Currency Mirage Thirst Upon the Banks of That River Song from the Void Tracks When the Last Bell Tolls Sensation of a Moment Life on the Wing
Happy New Year y’all. I hope this is a year of sanctuary, and shared values, surrounded by friends and loved ones.
So one of my goals last year was to read 1000 poems. I…I didn’t make it. I didn’t meet that goal.
So I’m going to try it again this year, but instead of posting the poems, I’ll just list them.
I don’t know what the year is going to hold, so the goal for my books is modest, just 2 a month. I set a goal of 16 books for 2024. I set it on November 6th. But I also had other goals. I was conflicted.I think I reached 12 books in the last 7 weeks of the year.
Anyhow. Watch this space for those 1000 poems and 24 books.
That said, the first poem of the year:
The Road Not Taken Robert Frost
Two roads diverged in a yellow wood, And sorry I could not travel both And be one traveler, long I stood And looked down one as far as I could To where it bent in the undergrowth;
Then took the other, as just as fair, And having perhaps the better claim, Because it was grassy and wanted wear; Though as for that the passing there Had worn them really about the same,
And both that morning equally lay In leaves no step had trodden black. Oh, I kept the first for another day! Yet knowing how way leads on to way, I doubted if I should ever come back.
I shall be telling this with a sigh Somewhere ages and ages hence: Two roads diverged in a wood, and I— I took the one less traveled by, And that has made all the difference.
Long, long ago, before bitcoin, X, and ChatGTP, say 2014 or 2015, there was a crowdsourced project that was working on collecting and aggregating pop chart data into a single source document. I acquired a copy at that time, and the project was subsequently forced to shut down – or underground – by copyright holders.
I’ve held on to the spreadsheet, and recently realized I could use it to generate Spotify playlists using Soundiiz.com – an online playlist creator/converter.
And so I did.
Filtering the list by decade, say, the 1970s, I was able to create a list of the songs/artists that I could then import into Soundiiz. After ingesting the list, Soundiiz allowed me to save it as a Spotify playlist:
A playlist capturing songs that charted during the 1970s. At the time of this post, there are 4,873 songs, and it would take almost two weeks to listen to the entire playlist.
It took me a while to come up with a reasonably efficient process. The original file contained a unique key for each track, which I included in the sheet I used to create the lists for import. It was helpful to be able to save the results of the initial import from Soundiiz that showed whether or not it was able to find a specific track.
After generating the playlist, I used Soundiiz to download it as a .csv file, which provided a couple of song IDs, as well as the track time, in seconds, and the Spotify track url. Dropping this into the spreadsheet provided me three sets of data – the original artist/track listing, with the key, whether or not the track was successfully added to the playlist, and the additional data from Spotify.
Of course, songs that weren’t found weren’t included in the playlist, and I didn’t have a piece of data to use to match Spotify track directly to the chart entry. Not being a programmer, I just pasted the data into my spreadsheet, and turned to brute force manual adjustments to line up the playlist tracks with their entries.
Once I completed this for a particular decade, I was able to easily generate additional playlists, using the Spotify URLs, which Soundiiz easily digests. I was able to quickly create a playlist for each year of the decade:
A Spotify playlist containing songs that charted in 1976, generated from the list of Spotify URLs in the enriched spreadsheet of chart data.
Sorting by the year and the top ten chart positions, I was able to create a playlist of the top ten hits of the 1970s:
Only songs that reached the top ten during the 1970s. This playlist only contains 930 songs, and it would only take about 2 and a half days to listen to them all.
Working my way through the decades, from the 1960s through the 2000s, I was able to create a playlist for each decade, which can later be used to easily generate playlists by year and chart position. For example, in a matter or minutes, I generated a playlist of all the number 1 hits from the 1960s through 2009:
A playlist of number one hits from January 1960 through December 2009.
Issues:.
In some cases, Spotify was unable to find tracks, such as “Blame it on the Boogie” by the Jacksons when I was trying to import “Jacksons – Blame it on the Boogie.” Once I added the article, “The,” (“The Jacksons – Blame it on the Boogie,”), it was able to consistently add their tracks to the playlist.
Roughly 5% of the songs for any particular year could not be found. Some were related to the artist name being different; Bobby Bland in the original data, vs Bobby “Blue” Bland on Spotify. Others are due to the song simply not being available. Still others are related the songs being removed from Spotify; Neil Young and Joni Mitchell, notably, pulled their songs from the platform in protest. Young’s exit from the platform also affected tracks by Crosby, Stills, Nash and Young. Finding these tracks involves some prioritization, and manual search.
Sometimes, Spotify chose the wrong version of the song, pulling the album track, rather than the single edit. These can be spotted by transforming the time data from the original source (in m:ss format) and Spotify (in seconds, with “s” appended) into decimal time, and then calculating the difference. Anything more than 5 seconds or so indicates a potential mismatch. I haven’t come up with an easy way of matching based on times, so this is a manual process, and requires some prioritization; say, top ten hits with a delta of more than 10 seconds. In some cases, the album mix is all that’s available. More of a great song is a small price to pay.
I would imagine that some songs are just incorrect. I haven’t yet done the analysis to determine if I can easily find those tracks and correct them.
Next Steps/Other Ideas:
The data stretches from 1890 to 2014. I will work my way back from the 1950s, creating playlists decade by decade, always looking for ways to improve the process. Once that process is complete, I can create further enrich that data, and create more thematic chart lists. There is a genre field in the source data, so I could generate, say, a playlist of all the Jazz songs that have hit the charts, since the 1960s. Further enriching the data from a source like MusicBrainz would allow the playlists based on other criteria. I would love to be able to incorporate All-Music’s “Styles” tags to generate playlists, but there’s no API access to them; that would be a very involved and time consuming project. On the other hand, who knows what I’ll find to help enrich my spreadsheet once I start looking. For example, here’s a list of music APIs: https://rapidapi.com/blog/top-free-music-data-apis/
Finally, it might be worthwhile to store the data in a MySQL database, It’s pretty slow and unwieldy in the current form, and that would give me an excuse to use and learn SQL.
Several years ago, I read about a study that showed that people tend not to listen to new music as they got older.
The peak age for discovering new music, the [Deezer] results suggested, was 24. This is when 75% of respondents said they listened to 10 or more new tracks a week, and 64% said they sought out five new artists per month. After this, though, it seems people’s ability to keep up with music trends peters off.
I was curious what “new” music I’d been listening to over the last decade and a half I’ve been tracking it, so I did a little analysis, looking at artists that I hadn’t listened to the years before; counting the raw number of new artists, and then noting the new artists that had 75 or more plays.
Year
New Artists
New Obsessions
Notes
2022
551
Breadth, no depth
2021
175
Larkin Poe, Rura
Blues, and a new Celtic band
2020
339
Sharon Jones & the Dap Kings
Great R&B
2019
491
Tyler Childers, Bothy Band, Lunasa, Dervish
Lots of Celtic this year
2018
367
Cachao
From the Complete Cuban Jam Sessions
2017
615
Tom Eaton
New Age; great Tai Chi music
2016
398
Savages, Early Rise, Baroness, Device, Godsmack, Die Antwoord
Experimentation with heavier rock than usual. And Die Antwoord. What can we say about Yolandi and Ninja?
2015
386
Lou Donaldson, Ike Quebec, Kenny Burrell, Donald Byrd, Jimmy Smith, John Renbourn, Brian Blade
This was the year I created my Blue Note Records playlists.
2014
287
Martin Denny
Exotica
2013
235
Civil Wars
Folk
2012
420
Luciana Souza
Latin vocal jazz
2011
256
2010
58
2009
214
2008
223
Jay Farrar, Alejandro Escovedo, Iron & Wine/Calexico
Folk/Americana
John’s “New” Music, 2008 – 2022
I reckon one could argue that none of this counts as new music, since most of the genres were not unknown to me. But my dalliances with Metal in 2016, Irish Traditional music in 2019, Exotica in 2014 and Blue Note Records in 2015 were, by and large, new sounds for me. Certainly I would argue that this shows a willingness to expand beyond the Journey, Foreigner and Styx of my formative years.
Notes about the raw data: This analysis was done from the 300,000 record dataset of my music listening habits, going back to 2007. I have removed duplicate tracks from that day I accidentally played REO Speedwagon’s “High in Fidelity” 18 times in a row, or the two days worth of Wilco when I turned off the amp, but Spotify was still churning through the playlist on my PC. I’ve also normalized artist names, so that Pat Metheny Group, Pat Metheny and Pat Metheny and John Scofield are rolled up as Path Metheney.
A poem I wrote about my father in 1994, shared here in recognition of his birthday. I’m not sure what he would have thought about this poem; he appreciated more the poetry of the long sloping lines of a 64 Galaxie 500.
Tool & Die John Lyon
Calipers and micrometers, cradled by the red felt lining the half opened drawers of the wooden toolbox that belonged to his father, wait to measure the tolerances of parts that must work together without touching.
And his corrugated space smells of the sweet oil sliding down the bit, smoking as metal bites into metal, digging towards the core, extruding the sharp helix that can tempt blood from my young fingers.
We hide behind masks, he and I, as he draws a molten bead along the cold unparted edges, the inscrutable panes protect our dark eyes. We must not look directly at such couplings.
Even here, among the jagged edges and melting surfaces, kindness lays down in the teeth. The blade, oiled to cut softly through the angle iron eases itself down under his sure fingers , chewing gently through the 90º angles, 6″ at a time.
And there are no shadows here; the cold fluorescent lights illuminate every square inch of my father’s workshop. The only darknessess are the fears lying beneath his clean work shirt, beating against the pencils and rulers he carries in his breast pocket.
Today, lonely for my father, I saw a log, or branch, long, bent, ragged, bark gone. I felt lonely for my father when I saw it. It was the log that lay near my uncle’s old milk wagon.
Some men live with a limp they don’t hide, stagger, or drag a leg. Their sons often are angry. Only recently I thought: Doing what you want… Is that like limping? Tracks of it show in sand.
Have you seen those giant bird- men of Bhutan? Men in bird masks, with pig noses, dancing, teeth like a dog’s, sometimes dancing on one bad leg! They do what they want, the dog’s teeth say that.
But I grew up without dog’s teeth, showed a whole body, left only clear tracks in sand. I learned to walk swiftly, easily, no trace of a limp. I even leaped a little. Guess where my defect is!
Then what? If a man, cautious, hides his limp, somebody has to limp it. Things do it; the surroundings limp. House walls get scars, the car breaks down; matter, in drudgery, takes it up.
On my father’s wedding day, no one was there to hold him. Noble loneliness held him. Since he never asked for pity his friends thought he was whole. Walking alone he could carry it.
He came in limping. It was a simple wedding, three or four people. The man in black, lifting the book, called for order. And the invisible bride stepped forward, before his own bride.
He married the invisible bride, not his own. In her left breast she carried the three drops that wound and kill. He already had his bark-like skin then, made rough especially to repel the sympathy
he longed for, didn’t need, and wouldn’t accept. So the Bible’s words are read. The man in black speaks the sentence. When the service is over, I hold him in my arms for the first time and the last.
After that he was alone and I was alone. Few friends came; he invited few. His two-story house he turned into a forest, where both he and I are the hunters.
This is another one of those poems that has much more resonance now that it did then
Driving Through Tennessee Charles Wright
It’s strange what the past brings back. Our parents, for instance, how ardently they still loom In the brief and flushed Fleshtones of memory, one foot in front of the next Even in retrospect, and so unimpeachable.
And towns that we lived in once, And who we were then, the roads we went back and forth on Returning ahead of us like rime In the moonlight’s fall, and Jesus returning, and Stephen Martyr And St. Paul of the Sword …
— I am their music, Mothers and fathers and places we hurried through in the night: I put my mouth to the dust and sing their song. Remember us, Galeoto, and whistle our tune, when the time comes, For charity’s sake.
