Modelling an all-time greatest musical playlist

The popularity of Triple J's annual Hottest 100 has made my wonder what my favourite songs of all time are and whether I could come up with a list based on some actual data. The information I have to use is my iTunes data since 2005. Being only 8 years of my life, this data set is limited. But with any luck (that is, if the assumptions hold true) the following algorithms will stay appropriate into the future and require only minor tweaking. What we're trying to do is come up with a method that will tell me, from my listening habits in iTunes, what my favourite songs are. Whether you actually listen to your favourite songs more than others is a debate for another time.

iTunes doesn't tell you when songs were played, just how many times, so the useful parameters we can export for each song are "Play Count" (p) and "Date Added". If we add up all the individual play counts, we get the "Total Play Count" for the entire collection (P). Date Added can be turned into the number of days the song has been in the collection - time (t). We also know the number of songs in the collection now (N) and at various times in the past when I've exported the data.

First cut:
An easy first-cut model is to simply divide each song's play count by its time in the collection and order the songs by this rate of play. As a first attempt this may seem logical, however the problem is that it is heavily biased towards newer songs. You're likely to listen to a song a few times after you add it before it slips back into your various playlists. It also doesn't take into account that there are more songs in the collection now than at the start.

What we need to do is come up with an equation that tells us how many times a song is expected to have been played depending on when it was added. We can then compare this number to how many times it was actually played and order the songs by this ratio.

Second cut:



This second version suffers from the same biasing problem as the first, but does take into account that the number of songs in the collection is changing over time. This is important as if you assume that you listen to music for about the same amount of time each day, then the more songs you have in your collection, the less likely you are to randomly hear the same song twice. Hence, songs that are played regularly when the collection is small should not be treated in the same way as songs played with the same frequency when the collection is large. N₀ is the number of songs in the collection at t₀. This model assumes that the number of songs in the collection grows linearly over time (A and B are constants) - that is, the same number of songs are added each month. This is about right for my collection. The integration is left as an exercise for the reader (hint, you get a log function).

Third cut:



This final version takes into account that when you add new songs to your collection that you like, you are likely to listen to them quite a lot, independently of the number of songs that are already there - that is, they get added to a "new songs" playlist. The novelty of a new song eventually wears off, so the way we've modelled this is to use an exponential factor. You can tweek the coefficients (C and D) by thinking about the "half life" of a new song. The integration is left as an exercise for the reader (hint, you get a log function and an exponential).

The equation now contains two components - the first modelling the number of plays expected through random play and the second the impact of adding new songs to the collection. The model suggests that I play the same number of songs each year (apart from a barely perceptible increase due to the exponential factor) and it seems to work pretty well. This model won't work if and when I swap over to streaming music, as opposed to owning it, as my major form of music consumption, but for now it's holding up. Having played around with the coefficients, the list as it stands is below. It pretty much represents upbeat songs I go running with and songs my 2-year old likes - for whatever reason, he likes Korean pop music! I have to think that the novelty of Psy will wear off over time, but Hall and Oates, they'll never die.

Gangnam Style	PSY
I Remember	Deadmau5 and Kaskade
ABC News Theme Remix	Pendulum
You Make My Dreams	Hall & Oates
Shooting Stars	Bag Raiders
This Boy's In Love	The Presets
Get Shaky	Ian Carey Project
Monster	BIGBANG
From Above	Ben Folds
Banquet	Bloc Party

The 27 Club

Brian Jones, Jimi Hendrix, Janis Joplin, Jim Morrison, Kurt Cobain, and this week tragically Amy Winehouse, all died at the age of 27. This coincidence has spawned the notion of the 27 Club - a club whose members are influential musicians who died at the age of 27. But is there anything statistically significant about this club? Should musicians fear turning 27?

There are a few things that we should note before starting this investigation:

1. It's quite clear that it is not true to say that more influential musicians die at the age of 27 than at any other age. If this were true, musicians would be dying all the time, and the facts that our radios are filled with golden oldies and John Farnham keeps touring are testament to this. 
2. We will never find enough data to fully test out any theory we come up with. For example, what defines an "influential" musician? One approach might be to scour wikipedia and find the death dates of every artist who had a top ten hit in the last 50 years. I leave this as an exercise for the reader.

What we will do for this article is redefine the club to be for musicians who have died through misadventures with drugs. And what do you know, wikipedia has an article dedicated to just this topic - a list of celebrities who had drug-related deaths. Looking at just the 100 musicians in the list, we get the following distribution for lifespans of musicians who died through drug misadventure.

This distribution has an average of 36, a median of 34 and a mode of 28. All pretty close to 27. Maybe there's more to this than we thought. This can be compared to Australian Bureau of Statistics data for Australian males between 2007 and 2009. It's clear that musicians who have drug-related deaths are dying at a much younger age than Australian males. Note the caveat here, the musicians aren't dying of old age - these are only musicians who have drug-related deaths.


It's easy enough to come up with a theory for this. Musicians - indeed society at large - are most likely to start being exposed to drugs at the age of 20. In any case, before this time there aren't many musicians popular enough to have a wikipedia page or a public influence strong enough to be accepted into the 27-Club. The number of deaths seems to drop off after about the age of 40, but this is not because they are all becoming family oriented and leading a clean life. The number drops because there are less musicians alive at 40 to die. This is the same explanation for why less people in the general population die at the age of 95 than do at 80 - not that many people live long enough to die at 95.

The following chart displays the probability of dying within the next five years given that you have survived to now. For example, if you are currently in the 60-64 year age range, this chart shows the probability of dying in the 65-69 year age range. For musicians who have drug-related deaths, between the ages of 20 and 65, the chance of dying in the next 5 years is between 25-50%. Within the noise of the small sample set, its about the same for each 5 year category between 20 and 65, perhaps increasing slightly with age. This suggests that for influential musicians, if they are going to die through drug use, they have roughly the same chance of dying within the next 5 years no matter how old they currently are, perhaps a little higher if they're older. The probability of death doesn't peak at 27, and conversely, just because you have survived till now doesn't mean you have a greater chance of surviving the next five years. This is similar to the distribution of cricket batting scores, for those interested.

The peak at 25-29 in the previous chart occurs only because the musicians have not died before that age and suggests there is nothing supernatural about the number 27 (despite the curse of 27). Note that for Australian males, the data stops at 100 so the 100+ bar represents dying above this age. For the musicians, the data stops at 70-74, hence its peak at 100%.




Note again that this analysis is for musicians who die drug related deaths. It doesn't suggest that musicians have shorter lifespans than the average Australian male - it does however suggest that musicians who die via drugs have shorter lifespans. This could be because if you are going to die a drug-related death you are likely to die young because taking drugs is a risky endeavour, or it could be because by the age of 75 you have probably stopped doing drugs and so your death doesn't appear in the chart.

So is there anything to the 27 club? Not really. We've shown (albeit with just one rather incomplete article on wikipedia) there is nothing magical about the age of 27 - indeed 28 seems more interesting - and the peak in drug-related deaths around this age is quite predictable. Far more musicians die at an older age of other causes just like the rest of us, and somehow Keith Richards is still alive. The club exists because humans like to associate meaning to patterns. We're very good at pattern recognition. Our ability to associate the seasons with animal migrations, and the stars in the sky with when to plant vegetables, gave us an advantage throughout evolution. However, false pattern recognition doesn't get us killed and so we often spot patterns where they don't exist - for example, we see faces in the World Trade Center disaster, or the Virgin Mary at the beach. Some even believe that because of this, we have created religion to explain patterns we can't explain.

A nod here to the like-minded Bespoke Blog and its article Morbid Statistics & The 27 Club which I found quite coincidentally as I was finishing up this article. 

Visualising the Music Universe

Every now and then I like to post data visualisations, and this one comes from one of my favourite web applications, Last.fm. Last.fm remembers what songs you play on your iPod (or whatever music player you use). Given that we now have an electronic device ban in our workplace, I'm giving their online streaming radio a go. (Ask me what I think about this ban and I'll give you a forthright answer, offline...) In any case, last.fm is a data lovers dream. The following visualisation shows my listening habits in 2009. The planets represent the top tags given to songs I listened to throughout the year - generally, these tags are song genres. Planet size is proportional to how much of that tag I listened to. The moons represent my top artists of 2009, with the light side showing how much I listened to the artist this year and the dark side showing last year's listening. Moons that are clustered together suggest that those artists are similarly tagged. The orbits show the correlation between the artist and the tag - artists with a closer orbit are more strongly associated with that tag than artists on the outer orbits.



Very funky, even if sometimes the moon is larger than the planet, and the small planet "Old People" appears in my solar system - you'll need to check out the high resolution picture to make out the smaller planets and moons.

Geek Pop 2010

This year’s Geek Pop festival launches on 12 March. Previously an online-only event, the festival is in its third year and is now adding live music to the programme, with gigs in Bristol and London.

Geek Pop is a celebration of science-inspired music and geek culture, featuring artists from around the globe. More than 30 artists are signed up to perform across its virtual and physical stages in 2010, with music from every set available to download for free.

Sponsored by Computer Geeks and the British Science Association, the Bristol live gig at Cube Microplex will kick off proceedings on 11 March, just ahead of the virtual launch. Geek Pop crew member Jim Bell is organising the Bristol event. “It’s been a huge undertaking this year,” he says. “But we’ve got some great geeky acts booked and there’s no doubt that adding live gigs to the programme has really helped get the word around.”

Many artists involved this year have been writing and recording new material specifically for the festival. Canadian hip-hop artist Baba Brinkman, known for his unique fusion of rap and evolution (or all things...), will be unveiling his rationalist anthem “Off That” at Geek Pop. And returning for the second year in a row, UK-based Spirit of Play will be performing brand new and specially tailored science songs at both the online event and London gig at The Miller on 18 March.

According to Festival Organiser, Hayley Birch, the festival has evolved rapidly since 2008. “In the first year, we only had about ten artists and none of the music was specifically written for us,” she says. “It’s great to see we’re establishing ourselves as an annual event and especially that we’re giving creative inspiration to musicians to make music about science.”

Previous years saw musicians perform online across the Tetrahedron and Reproductive stages, and Tesla Tent. This year, organisers commissioned Bristol-based illustrator Sam Church to re-design the site and have added another stage, The Comical Flask, where acts will be entertaining audiences with nerdy, science-based comedy.

From 12 March, festival-goers can experience the virtual festival from the comfort of their own homes by typing visiting the geekpop homepage. Further details of the Bristol and London live gigs, including how to buy tickets, are available. Given March 14th is pi day, I think the festival's timing is apt!

I spoke to Hayley Birch in 2008 about their inaugural festival - check out that interview in Ep 94: The Geek Pop Virtual Music Festival.

Keyboard cat tops the charts

Internet memes are fascinating. The term meme refers to ideas and cultural phenomena that spread through society through imitation. The term was first used by Richard Dawkins in his book The Selfish Gene to discuss how evolution could work with cultural phenomena such as beliefs, fashion and music. He argues that memes are cultural analogues to genes, in that they self-replicate, mutate, can be inherited and respond to selective pressure.

The concept of Internet memes relates to this original definition of meme, and refers to the spread of ideas across the internet. Colloquially, internet memes are essentially in-jokes. And like any good in-joke, the meme will often start out as not particularly funny, but after you have seen it 1000 times, it takes on a life of its own, often evolving to something completely unpredictable. Lolcats is an example of a meme that I don't think is all that funny - however, I am quite partial to Rick Rolling, and I Like Turtles is awesome!

I was recently putting together my annual list of most played songs on my ipod for the last year when I realised that I am clearly susceptible to internet memes. Not only was the list Rick Rolled (with no less than three Rick Astley songs) but the top song was one I learnt of through my favourite internet meme, Keyboard Cat. Keyboard Cat consists of 1984 footage of "Fatso" the cat playing the keyboard, and was uploaded to youtube as charlie schmidt's "cool cat". The gag is to append footage of the cat to video of people doing stupid things (falling over, getting hit in the head etc.) - the cat is essentially playing the person off stage, much like getting the hook in the days of vaudeville.

One of the best Keyboard Cat videos - and probably my favourite video ever on youtube - has Keyboard Cat with Hall and Oates playing their song You Make My Dreams. The video starts with a segment of Desperate Lives, a 1982 movie starring Helen Hunt showing the effects of drug use - Keyboard Cat plays off the overdosing Hunt as a cautionary tale against drug use. Then the music video starts, and this is when I discovered the song - check it out!



2009 saw a minor comeback for the song, and even though I have no evidence to back this up, I am going to put it out there that Keyboard Cat is the reason for its renewed success. Here's You Make My Dreams from the movie (500) Days of Summer - video here.



And for a complete understanding of Keyboard Cat, check out the excellent Know your Meme series of videos.

Ep 117: Science of Superheroes - Mystique (X-men)

Ever wondered whether it is scientifically possible to become a superhero?

In a new series of podcasts, Dr Christopher Pettigrew (aka Dr Boob*) and I are going to tackle this question. Chris is a post-doctoral researcher at the Department of Biochemistry in University College Cork, and in these podcast episodes - which we will publish more than a few times a year - we will uncover whether it is possible now to possess the powers of superheroes, and if we can't, whether in the near future we could engineer ourselves to become superheroes.

The first superhero we are tackling is Mystique from X-Men. X-men get their powers from an "X gene" that normal humans do not possess, and Mystique is a shapeshifter who naturally looks blue. Actress Rebecca Romijn portrayed Mystique in the X-Men films - I know I clearly remember the blue body-paint...

Mystique has a number of powers including:

• The ability to change skin colour;
• The ability to shape-shift - that is, change form;
• She can impersonate other voices;
• She can rapidly grow her hair.

Within nature, chameleons are able to change their skin colour to match their environment. There are also technologies under current development, such as metamaterials, that can be used to make something look invisible. Through a combination of genetic manipulation to activate melanocytes (and possibly chromatophores), and the use of surface coatings, it is not unforeseeable that we could develop human chameleons. The difficulty here lies in whether we can make a skin colour change a conscious decision - how can you wire up the body such that skin colour responds your thoughts?

The challenge of being able to impersonate another person's voice should be easy enough to conquer in the near future through a combination of electronics and simple mimicry. It is also possible to foresee rapid hair growth - this could be accomplished by rapid protein synthesis, such as in spider webs.

The biggest difficulty comes with the shape-shifting - how can one change their 3D shape?

Tune in to the podcast here (or press play below) to discover what scientific techniques we came up with to tackle the problem of scientifically engineering Mystique:



A few extra notes to explain some of the random comments in the show:

• Iva Davies is the front man of Australian band Icehouse;
• Shane Warne, Greg Matthews and Graham Gooch are all cricketers who advertised the hair-loss company Advanced Hair;
  

Let us know your thoughts on how we could scientifically engineer Mystique. We rated this a 7.5 out of 10 possibly for the next 200 years - if someone really wanted to, notwithstanding the ethical concerns along the way.

Also let us know which superheroes you would be interested in us tackling.

* From here on in, Chris will be referred to as Dr Boob - this nickname stems from the fact that Chris's PhD and some of his post-doctoral work has been into the study of breast cancer - yes, someone who is actually changing the world!

This makes me want to count!

If it were possible that Feist could get any cuter, here it is. In this clip, she sings her song 1234 with the cast of Sesame Street. Brilliant - surely this will get the next generation into mathematics!



Oh why not, here's one more! This is Sesame Street's take on Bruce Springsteen's Born to Run - in this case, Born to Add!

Ep 109: Tongan blowholes and whales

The Kingdom of Tonga is an archipelago in the South Pacific Ocean comprising 169 islands stretching over a distance of about 800 kilometres. It is the only sovereign monarchy in the Pacific and takes pride in its claim that it was never colonised.

I recently spent a wonderful two weeks in Tonga - the islands completely live up to their billing as the Friendly Islands - Captain Cook gave Tonga this moniker after his first visit in 1773 when he was treated to various festivals. It was only later that it was revealed local chiefs wanted to kill Cook but could not agree whether to attack by day or night.

This is the first in a series of podcasts about Tonga that include my own recordings from Tonga and also interviews with experts in the scientific areas we tackle. In this show, we look at:

1. The Mapu a Vaea blowholes - these blowholes are created by the ocean pounding into the coastal rock and moving through natural tunnels creating a fountain;
2. Tongan singing - not much science here, but it's beautiful!
3. Whale behaviour and migration - I chat to Scott Portelli, an award winning photographer and diver who runs whale-watching tours in Tonga. Scott recently won the prestigious Scuba Diver AustralAsia - Through the Lens Underwater Photography Competition with an outstanding photo from Tonga - see more of Scott's photos on his webpage. Scott runs Swimming with Gentle Giants, a company which conducts whale diving tours each year between August and October off the islands of Vava'u in northern Tonga. I chat to Scott about:
• his experiences swimming with whales,
• when to swim with whales,
• whale behaviour and migration patterns,
  
• the oceanic animals of Tonga,
  
• a little bit of whaling politics,
• the threats to whales, including whaling, global warming and pollution, and
  
• the fact that I once ate whale...

There are very few places left so get booking if you would like to swim with whales this year.

Stay tuned for more on Tonga in upcoming podcasts, in which we will talk about, and experience, the intoxicating drink Kava, the Stone-Henge of the South Pacific, the effects of global warming and rising sea-levels on Tonga, the local animals and more on whaling science and politics.

Listen to this podcast here:

And see below for some very cool traditional dance (also here if you can't see the video):

Ep 105: Music and Intelligence

This week's podcast is about a recent study that correlated intelligence with music choice - it won our new regular prize Correlation of the Week!

You can read more about this study in the original Mr Science post on the topic put out in March 2009 - Correlation of the Week: Intelligence and Music Preference

Listen to his podcast here:

Correlation of the Week: Intelligence and Music Preference

The other day I heard loud distorted music approaching from a hotted-up 1986 Holden Calais with mag-wheels and a ridiculously loud sub-woofer and thought:

1. I wonder what this guy is compensating for, and;
2. His music taste probably says a lot about his intelligence.

Well, the study has been done. Before we jump into it, it's worth saying that the author of this study, Virgil Griffith from California Tech, makes no claims about correlation equalling causation. He just presents his results and allows us draw our own conclusions. His method of correlating music with intelligence involved:

1. Find the ten most frequent "favourite music" descriptions at every US college via that college's Network Statistics page on Facebook;
2. Download the average SAT/ACT score from CollegeBoard for students attending those colleges;
3. Correlate the Facebook music results with SAT/ACT results and draw your own conclusions on music taste and intelligence.
   

The artist associated with the highest intelligence, by a clear margin, is Beethoven, whilst some rapper by the name of Lil Wayne seems to be loved by those less blessed in their mental faculties. The study also showed that Counting Crows, Sufjan Stevens, Radiohead and Ben Folds Five appealed to big brains whilst very disappointingly, for me anyway, Beyonce is at the other end of the scale. According to the data, people who listen to "indie" music are the smartest, and the genres come out:

Soca < Gospel < Jazz < Hip Hop < Pop < Oldies < Reggae < Alternative < Classical < R&B < Rap < Rock < Country < Classic Rock < Techno < Indie

It's tempting to think that intelligence has a direct impact on music choice, but this is probably not true - I know plenty of research scientists into Britney Spears. And the reverse - that music-choice influences your intelligence - doesn't make sense either, even though you may be occasionally tempted to think that listening to mindless dance-music makes you stupid. Could there be some drivers that influence both intelligence and music choice? Possibly. You can imagine that socio-economic factors and what you are exposed to whilst growing-up would influence the music you like and how well you do at school. Your parents would be a big influence too - I just can't shake Wet Wet Wet... There are countless possibilities that are best mulled over at the pub.

Whatever the case, I'm heartened by the results! We've already done a story on visualising music tastes using Last.fm, and most of my favourite artists are in the top half of the table with indie my favourite genre. For more on science and music taste, check out the podcast we put out in 2006 - one of the very first Mr Science Show episodes down the phone to China Radio International - called Can Scientists Predict Your Music Taste which looks at how web applications such as Last.fm and Pandora recommend songs to you based on your listening habits.

Griffith's study on music and intelligence comes on the heels of his "books and intelligence" study, in which he correlated book tastes with intelligence again using Facebook data. Harry Potter is the most popular book with The Bible second (for some reason, The Bible and The Holy Bible are different books). Some of the results include:

• Lolita is the smartest book;
• The Zane series of books are the dumbest - check them out, they seem funny and erotic...
  
• The smartest religious book is The Book of Mormon whilst the dumbest religious book is The Holy Bible;
• The dumbest philosophy book is The Five People You Meet In Heaven with the smartest philosophy book Atlas Shrugged.

For more on the book study, check out booksthatmakeyoudumb. And for more on the music study, see musicthatmakesyoudumb. The following picture is one of the ways Griffith visualised his results. See where your favourite artists lie.

So congratulations to Virgil Griffith and your study on music tastes and intelligence, you have won Correlation of the Week - the Flying Spaghetti Monster would be proud!

Counting down to Geek Pop

In less than one day now, the Geek Pop 09 Music Festival will kick off.

Geek Pop is a free online music festival that brings together science-inspired artists from around the globe in a gleeful celebration of geek culture. The festivities kick off on the 6th March (UK time) and runs until the 15th as part of National Science and Engineering Week in the UK.

What is great for us geeks who don't reside in the UK is that it is a virtual festival - it is completely online. Sometime later on today the Geek Pop site will be transformed and we’ll have access to geeky music from all over the world, as well as interviews, lyrics for every song and geek chic festival merchandise. There will also be a festival highlights podcast.

To find out a little bit more about Geek Pop, check out the Episode 94 of my podcast, in which I spoke to festival organiser Hayley Birch about the festival, where the idea came from and what type of music we can look forward to.

And check out this youtube advertisement for Geek Pop by molehillmedia - very amusing...



See you there, virtually that is...

Ep 94: The Geek Pop Virtual Music Festival

Geek Pop is the world’s only sci-pop festival - a free online music event featuring songs about science. The festival brings together science-inspired artists from around the globe in a gleeful celebration of geek culture. In 2009, Geek Pop will take place between 6-15th March.

This week on the podcast I spoke to Hayley Birch, the organiser of Geek Pop, about the festival, where the idea came from and what type of music we can look forward to.

You can subscribe to Geek Pop updates by sending an email to news@geekpop.co.uk with the subject SUBSCRIBE ME RIGHT UP. Or register your attendance at the Facebook event.

We've looked at the various scientific aspects of music in the past on Mr Science, just check out our music label.

Listen to his podcast here:

And remember to tell us your science highlights from 2008 to go into the running for some sciencey prizes. Answers will also contribute to our year-in-review podcast early in 2009. Let us know here.

Last.fm, data mining and mashups

I've recently been putting together a Guide to Web 2.0 for The Helix Magazine and one of the most interesting aspects has been exploring the various mashups and applications of Last.fm.

Last.fm is a brilliant online music service and currently my favourite "web 2.0" application. By downloading a plugin for itunes (or whatever music player you have) that "scrobbles" each song you play (that is, tells Last.fm what you are listening to), a picture of your music taste builds up, and people with similar listening tastes are found. Artists are recommended to you according to your tastes, charts of your songs built up and "radio stations" perfectly tailored to you can be streamed online. But it is better than radio as there are no ads and you like every song.

By the way, I am westius on Last.fm.

Millions of songs are scrobbled every day by Last.fm users. This data helps Last.fm develop a massive database of user music preferences, and because of it's API, it is possible to access Last.fm information and develop interesting tools.

As users can tag their music with genres that they think aptly describe their songs and artists, it is possible to determine your own tag cloud of musical preferences. Using an excellent script at anthony.liekens.net, I came up with my own tag cloud, as you can see here.

It is possible from such tag clouds to examine how listeners fall into different categories through a process known as Data Mining. Data mining is essentially the process of sorting through enormous amounts of data and picking out the relevant stuff. Using principal components analysis - a mathematical technique which reduces multidimensional data sets to lower dimensions for analysis - and k-means clustering - an algorithm to cluster n objects into k groups - Liekens came up with 5 broad groups of Last.fm listeners:

1. Electronic/pop
2. Rock
3. Indie
4. Metal
5. Hip-hop
   

Clearly this list does not reflect everyone on Last.fm (where are the classical music listeners?), but it does reflect the majority. I was surprised that Indie is a group in itself and am intrigued by the bundling of electronic and pop together - there are some tweaks to the maths you can make that could come up with different groups, and better results might be possible with a bigger data set . Hip-Hop listeners were the most clearly defined group. You can read more about the maths and how these groups are separated in the original article.

Another interesting thing you can do is compare your music tastes to your friends. This pic is a difference cloud comparing my music tastes with that of my good friend intranation. We have a roughly 40% similarity in music genre tastes, with the green tags those that I have more of in my collection, and the red those genres that intranation listens to more than me. No real surprises there.

Mashups are all the rage at the moment. The term refers to web applications that combine data from more than one source into a single integrated tool. For instance, domain, an Australian real-estate site, adds data from Google Maps to provide location information. My current favourite Last.fm mashup is idiomap. idiomap is a digital music magazine that personalises its content according to your interests in music, which it learns from your Last.fm profile. It gives you stories and reviews of the artists and genres you like, helps you discover new music and mashes in video and audio from youtube and other sources. idiomag aggregates music articles from over 100 different sources. You can also tweak the articles you like so if you receive something you don't like, you won't get it again. I subscribe to the RSS feed of my personalised idiomap magazine and so far its been great and has included reviews of music DVDs of artists I like and schedules of when bands will be playing and appearing on TV. Good stuff.

I will probably put out a few more blogs like this as I explore this world of mashups. And for podcast listeners, yes hopefully I will get one of them out soon too!

The science of Eurovision

It is one of my favourite times of year, and I'm not even European.

The Eurovision Song Contest to Australians is a strange mix of bad 80's music, songs about "joy", "love" and "unity" (it's a good drinking game to take a shot every time one of those words are said), amazingly good looking hosts with amusing English skills, scantily dressed Eastern Europeans and reality TV winners from Western Europe. For the first time in my life, living in the UK I get a chance to vote for the winner and watch it live instead of having to ignore radio reports (of course it's all over the news) till the Sydney Sunday evening replay.

The voting of Eurovision is a complex interaction of politics and voting blocks. Each country votes in a popular vote, in which they can not vote for themselves, and each country has equal voting power. The voting is often based on politics and the whole system is a complex interaction of objects (countries) who interact with each other by giving each other points. A statistical analysis of the system can then give some insight in the nature of the interactions. For example, it can show whether certain countries form cliques that always vote similarly, or whether a country's voting is "in tune" with that of the whole group.

A team of Oxford scientists recently performed statistical tests on data from between 1992 and 2003 to uncover what is behind the voting. The team simulated a "random song contest", in which each country assigns its points randomly to 10 other countries, and compared these results to the actual data.

One of their tests looked at voting relationships over time. If, for example, country A gives and/or receives points from another country B over a long period of time, then we can deduce that in some way the musical tastes of the two countries are related. Carrying out the same analysis between country A and all other countries in turn will show whether country A thinks the same as the rest of Europe.

Another test observes the number of countries to which a given country A has awarded points and from which it has also received points. If a country has many such "reciprocal links", then one might deduce that its musical taste harmonises well with that of Europe in general. They also tested whether the ways countries voted could be correlated with each other. They looked at whether two countries that have both received and/or awarded points to a third country are likely to give or receive points from each other.

And the results? The UK is in tune with the rest of Europe, while France is a slight outsider. The cliques that were uncovered were Greece and Cyprus, the UK and Ireland, and the Nordic countries. Also, more surprising pairings such as Croatia and Malta, which are not geographically close, were found.

Read more in the Plus article United Kingdom - twelve points, and don't forget to vote!

Listen to this show here (with small, less than 10%, Eurovision music clips - yes I own some Eurovision albums....)

Science through song

They say that music is a very mathematical pursuit. Here at Mr Science, we have written about science and music many times.

The MASSIVE database is a website that contains information on over 2500 science and mathematics songs. Some songs are for children, others for professors. Some are by professional recording artists, others recorded in garages. The site is maintained by Greg Crowther, who is affiliated with the University of Washington, Science Groove, and the Science Songwriters' Association. MASSIVE is part of the US National Science Foundation's National Science Digital Library.

My personal favourite science song? She Blinded me with Science by Thomas Dolby — and you can find this song in the database. Another way of tracking down science songs is by doing a search for science at LastFM.

Music and Science on the Brain

Music has an indisputable ability to trigger powerful emotions. It is frequently associated with memories of the past, and hearing just a short clip of a song can often trigger feelings from deep within the subconscious. It is also used in various therapies, can add considerable depth to a movie or film clip, and can have a substantial effect on your mood, even the first time you hear a song. What is it about music that conjures up such feelings?

It is undeniable, yet largely inexplicable, that music can evoke emotions from your past, whether it conjures up memories from school, good times or lost loves. However, the mechanism within the brain that allows this to occur is relatively unknown. Traditionally, the fields of music and biology have not overlapped, and a deep understanding of the neurological effects of music still awaits us. One of the problems is that the emotional effect of music is very subjective – one song can be experienced in many different ways by many different people. Some may associate memories with the song, the environment in which it is played effects how people respond, and simply the personality and mood of the listener may make them predisposed to feel a certain way about certain pieces of music and musical styles. In summary, songs that affect some people, may not affect others – there is a cultural effect.

Notwithstanding this, a researcher at the University of New South Wales has worked out a few basic mathematical features of music that influence our mood.

“'Among other things,” said Dr Emery Schubert, “loudness, tempo and pitch have a measurable impact on people’s emotional response to music,'”

His study involved 66 volunteers who listened to four classical compositions and moved a mouse over a computer screen to indicate how they felt when they were listening to the songs. He found that arousal is associated with a composition’s loudness and to a lesser extent its tempo. Schubert stated that along with the idea that songs written in a major key are happy songs, and those in a minor key are sad songs, happiness is associated with a rising pitch and an increased number of instruments.

However, Schubert is aware that he has only highlighted a number of broad factors that contribute to music’s effect on our emotions.

”While we know that some musical parameters predict some emotions with a degree of certainty, musical features interact in complex ways, as do listener responses. Before we can compose musical emotions by numbers, we need to convert human experience and cultural knowledge variables into numbers, too. It will be some time before we can do this. What we've shown is that it is already possible to locate and quantify some of these emotions with some precision.”

Dissonance is another factor that is unpleasant to listeners and can create feelings of fear. It may also be intrinsic to music as infants as young as 4 months old show negative reactions. It has been found that varying degrees of dissonance causes increased activity in the paralimbic regions of the brain, which are associated with emotional processes.

Another recent experiment measured the brain activity while listeners were played music they chose that made them feel good and had emotional value for them. Activity was seen in the reward/motivation, emotion, and arousal areas of the brain. This result suggests a connection between the pleasure of music and the pleasures induced by food, sex, and drugs, which target these same areas.

Music can also effect hormone levels within the body, lowering levels of cortisol (associated with stress), and rising levels of melatonin (associated with sleep). This suggests music can help with relaxation. It also causes the release of endorphins, which help relieve pain.

Everyone has felt chills up their spine when listening to a piece of music. Emotions stimulate a region of the brain called the hypothalamus. Neurobiologist Jaak Panksepp found that people more often feel chills or goose bumps when listening to music when the music evokes a sad feeling or is compounded by a sad memory, as opposed to happy feelings or positive memories. He thinks this may be due to evolution – this response may be similar to those our ancestors felt when they heard the cry of a lost loved one bringing about a desire for close physical contact and keeping families together. It is known that songs mimicking the sounds of mourning and waling evoke feelings of sadness.

Cementing the fact that music has a powerful effect of the brain is a disorder called musicogenic epilepsy. People with this condition are mentally deficient, yet most are excellent musicians – some are even known as “musical savants” who have extraordinary musical talent. On the other hand, less than 1% of the population suffer from amusia, a condition that means that they can literally not recognise a melody, no matter how simple is it.

So however music works on us, it seems that it must have an important function, otherwise it would not have evolved. Perhaps an appreciation of music, like broad shoulders, may demonstrate fitness to a potential mate – singing or playing an instrument well requires dexterity and good memory. Or perhaps it is something we need to keep our brain stimulated with its complex patterns. Whatever its reason and however it works, music is fundamental to our society and something for us all to enjoy, even if we don’t all enjoy the same stuff.

Listen to this show here

What do ears do?

What do ears do? Do we really need them? How do they work, and what else do they do but help us hear?

Listen to this show here

Can Scientists Predict your Music Taste?

Has it come to this? Can science now predict something as personal as the types of music that someone likes? This week we take a look at some of the advances being made into helping us broaden our music appreciation, and predict the unpredictable.

Listen to this mp3 here