I'm going to try and avoid ranting about Israel and Palestine because there's much more heat than light right now. But I want to recommend some background reading that seems useful, and it's historical/background stuff rather than partisan:
I also want to point to a more "one-sided" piece (in the sense that it criticises one "side" specifically - I've no idea about the author's actual motivations): Five Israeli Talking Points on Gaza - Debunked. I recommend it because it raises some interesting points about international law and the like, and we in the UK don't seem to hear these issues filled out on the radio.
Also this interview with Ex-Israeli Security Chief Diskin. Again I don't know Diskin's backstory - clearly he's opposed to the current Israeli Prime Minister (Netanyahu), but the interview has some detail.
As usual, please don't assume anyone is purely pro-Palestine or pro-Israel, and don't confuse criticism of Israel/Hamas with criticism of Judaism/Islam. The topic is hard to talk about (especially on the internet) without the conversation spiralling into extremes.
There's a poster produced by the UK government recently that says:
1 in 3 rape victims have been drinking. Know your limits.
I can imagine there are people in a design agency somewhere trying to think up stark messages to make the nation collectively put down its can of Tennents for at least a moment, and it's good to dissuade people from problem drinking. But this is probably the most blatant example I've ever seen of what people have been calling "victim blaming".
If your friend came to you and said they'd been raped, would you say "You shouldn't have been drinking"? I hope not. And not just because it'd be rude! But because even when someone is a bit tipsy, it's not their fault they were raped, it's the rapist's fault.
It sounds so pathetically obvious when you write it down like that. But clearly it still needs to be said, because there are people putting together posters that totally miss the point. They should also bear in mind that a lot of people like to have a drink on a night out, or on a night in. (More than half of women in the UK drink one or two times a week, according to the 2010 General Lifestyle Survey table 2.5c) So it's actually no surprise AT ALL that 1/3 of rape victims have been drinking. What proportion of rape victims have been smoking? Dancing? Texting?
(By the way there's currently a petition against the advert.)
On the other hand, maybe it's worth thinking about the other side of the coin. People who end up as convicted rapists - some of them after a fuzzy night out or whatever - how many of them have been drinking? Does that matter? Yes, it matters more, because rape is an act of commission, and it seems likely that in some proportion of rapes a person went beyond reasonable bounds as a result of their drinking.
So how about this for a poster slogan:
1 in 3 rapists have been drinking. Know your limits.
(I can't find an exact statistic to pin down the number precisely - here I found an ONS graph which tells us in around 40% of violent crimes, the offender appears to have been drinking. So for rape specifically I don't know, but 1 in 3 is probably not wide of the mark.)
So now here's a question: why didn't they end up with that as a slogan? Is it because they were specifically tasked with cutting down women's drinking for some reason, and just came up with a bad idea? Or is it because victim-blaming for rape just sits there at a low level in our culture, in the backs of our minds, in the way we frame these issues?
In mainland Britain, you are never more than 34 miles from a pub.
This and other geo-factoids available from my new web service. (I've named it "Feet From A Rat" in tribute to this hoary old urban legend.)
I've been awarded a 5-year research fellowship! It's funded by the EPSRC and gives me five years to research "structured machine listening for soundscapes with multiple birds". What does that mean? It means I'm going to be developing computerised processes to analyse large amounts of sound recordings - automatically detecting the bird sounds in there and how they vary, how they relate to each other, how the birds' behaviour relates to the sounds they make.
Why it matters:
What's the point of analysing bird sounds? Well...
One surprising fact about birdsong is that it has a lot in common with human language, even though it evolved separately. Many songbirds go through similar stages of vocal learning as we do, as they grow up. And each species is slightly different, which is useful for comparing and contrasting. So, biologists are keen to study songbird learning processes - not only to understand more about how human language evolved, but also to help understand more about social organisation in animal groups, and so on. I'm not a biologist but I'm going to be collaborating with some great people to help improve the automatic sound analysis in their toolkit - for example, by analysing much larger audio collections than they can possibly analyse by hand.
Bird population/migration monitoring is also important. UK farmland bird populations have declined by 50% since the 1970s, and woodland birds by 20% (source). We have great organisations such as the BTO and the RSPB, who organise professionals and amateurs to help monitor bird populations each year. If we can add improved automatic sound recognition to that, we can help add some more detail to this monitoring. For example, many birds are changing location year-on-year in response to climate change (source) - that's the kind of pattern you can detect better when you have more data and better analysis.
Sound is fascinating, and still surprisingly difficult to analyse. What is it that makes one sound similar to another sound? Why can't we search for sounds as easily as we can for words? There's still a lot that we haven't sorted out in our scientific and engineering understanding of audio. Shazam works well for music recordings, but don't be lulled into a false sense of security by that! There's still a long way to go in this research topic before computers can answer all of our questions about sounds.
What I am going to do:
I'll be developing automatic analysis techniques (signal processing and machine learning techniques), building on starting points such as my recent work on tracking multiple birds in an audio recording and on analysing frequency-modulation in bird sounds. I'll be based at Queen Mary University of London.
I'll also be collaborating with some experts in machine learning, in animal behaviour, in bioacoustics. One of the things on the schedule for this year is to record some zebra finches with the Clayton Lab. I've met the zebra finches already - they're jolly little things, and talkative too! :)
Here's an update to my own personal data about how long it takes to get academic articles published. I've also augmented it with funding applications too, to compare how long all these decisions take in academia.
It's important because often, especially as an early-career researcher, if it takes one year for a journal article to come out (even after the reviewers have said yes), that's one year of not having it on your CV.
So how long do the different bits take? Here's a bar-chart summarising the mean durations in my data:
The data is divided into 3 sections: first, writing up until first submission; then, reviewing (including any back-and-forth with reviewers, resubmission etc); then finally, the time from final decision through to publication.
Firstly note that there are not many data points here, so for example I have one journal article that took an extremely long time after acceptance to actually appear, and this skews the average. But it's certainly notable that the time spent writing generally is dwarfed by the time spent waiting. And particularly that it's not necessarily the reviewing process itself that forces us all to wait - various admin things such as typesetting seem to take at least as long. Whether or not things should take that long, well, it's up to you to decide.
Also - I was awarded a fellowship recently, which is great - but you can see in the diagram, that I spent about two years repeatedly getting negative funding decisions. It's tough!
This is just my own data - I make no claims to generality.
Agh, I just got caught out by a "silent" change in the behaviour of scipy for Python. By "silent" I mean it doesn't seem to be in the scipy 0.12 changelog even though it should be. I'm documenting it here in case anyone else needs to know:
Here's the simple code example - using scoreatpercentile to find a percentile for some 2D array:
import numpy as np
from scipy.stats import scoreatpercentile
scoreatpercentile(np.eye(5), 50)
On my laptop with scipy 11.0 (and numpy 1.7.1) the answer is:
array([ 0., 0., 0., 0., 0.])
On our lab machine with scipy 13.3 (and numpy 1.7.0) the answer is:
0.0
In the first case, it calculates the percentile along one axis. In the second, it calculates the percentile of the flattened array, because in scipy 12 someone added a new "axis" argument to the function, whose default value "None" means to analyse the flattened array. Bah! Nice feature, but a shame about the compatibility. (P.S. I've logged it with the scipy team.)
Someone on the Linux Audio Users list asked how they could analyse a load of FLAC files to work out if it was true for their music collection, that bass frequencies below about 150 Hz (say) tended to be centre-panned. Here's my answer.
First of all, coincidentally I know that Pedro Pestana published a nice analysis of exactly this phenomenon, at the AES 53rd conference recently. He actually looked at hundreds of number-one singles to determine the relationship between panning and frequency in the habits of producers/engineers for popular tracks. The paper isn't open access unfortunately but there you go.
So anyway here's a Python script I just wrote: script to analyse your audio files and plot the distribution of panning per frequency. And here's how it looks when I analyse the excellent Rumour Cubes album:
(Just to stress, this is a simple analysis. It simply looks at the spectral representation of the complete mix, it doesn't infer anything clever about the component parts of the mix.)
See any patterns? The pattern I was looking for is a bit subtle, but it's right down at the bottom below 100 Hz (i.e. 0.1 kHz on the scale): the bass tends to "pinch in" and not get panned around so much as the other stuff.
This analysis of Lotus Flower by Radiohead (by Daniel Jones) shows the effect more clearly.
This is what's generally observed, and widely known in mixing engineer "folklore": pan your bass to the centre, do what you like with the rest. Not everyone agrees on the reasons: some people say it's because the bass can cause the needle to skip out of vinyl records if it's off-centre, some people say it's because we can't really perceive the spatialisation very well at low frequencies, some people say it's just to maximise the energy in the mix. I have no comment on what the reasons might be, but it's certainly folk wisdom for various audio people, and empirically you can test it for yourself by analysing some of your music collection.
NOTE: Code and image updated 2014-02-08, thanks to Daniel Jones (see comments below) for spotting an issue.
This week I was learning about Gaussian Processes, at the very nice Gaussian Processes Winter School in Sheffield. The term "Gaussian Processes" refers to a family of techniques for inferring a smooth surface (1D, 2D, 3D or more) from a set of sampled noisy data points. Essentially, it's an advanced and mathematically very sound type of regression.
Don't get confused by the name, by the way: your data doesn't have to be Gaussian, and Gaussian Process regression doesn't always produce smooth Gaussian-looking results. It's very flexible.
As an example, here's a first pass I did of analysing the frequency trajectories in a single recording of birdsong.
I used the "GPy" Python package to do all this. Here's their GPy regression tutorial.
I do want to emphasise that this is just a first pass, I don't claim this is a meaningful analysis yet. But there's a couple of neat things about the analysis:
- It can combine periodic and nonperiodic variation (by combining periodic and nonperiodic covariance kernels). Here I used a standard RBF kernel plus a periodic kernel which repeats every 1 syllable, and another periodic kernel which repeats every 3 syllables, which reflects well the patterning of this song bout.
- It can represent variation across multiple levels of detail. Unlike many other regressions/interpolations, sometimes there are fast wiggles and sometimes broad curves.
- It gives you error bars, which are derived from a proper Bayesian posterior.
So now here's my second example, in a completely different domain. I'm not a geostatistician but I decided to have a go at reconstructing the hills and valleys of Britain using point data from OpenStreetMap. This is a fairly classic example of the technique, and OpenStreetMap data is almost a perfect for the job: it doesn't hold any smooth data about the surface terrain of the Earth, but it does hold quite a lot of point data where elevations have been measured (e.g. the heights of mountain peaks).
If you want to run this one yourself, here's my Python code and OpenStreetMap data for you.
This is what the input data look like - I've got "ele" datapoints, and separately I've got coastline location points (for which we can assume ele=0):
Those scatter plots don't show the heights, but they show where we have data. The elevation data is densest where we have mountain ranges etc, such as central Scotland and in Derbyshire.
And here are two different fits, one with an "exponential" kernel and one with a "Matern" kernel:
Again, the nice thing about Gaussian Process regression is that it seamlessly handles smooth generalisations as well as occasional patches of fine detail where needed. How good are the results? Well it's hard to tell by eye, and I'd need some official relief-map data to validate it. But from looking at these two, I like the exponential-kernel fit a bit better - it certainly gives an intuitively appealing relief map in central Scotland, and it gives visually a bit less blobbiness than the other plot. However it's a bit more wrong in some places, e.g. an overestimated elevation in Derbyshire there (near the centre of the picture). If you ask an actual geostatistics expert, they will probably tell you which kernel is a good choice for regressing terrain shapes.
The other thing you can see in the images is that it isn't doing a very good job of predicting the sea. Often, we dip down to altitude of zero at the coast and then pop back upwards after. No surprises about this, for two reasons: firstly I didn't give it any data points about the sea, and secondly I'm using "stationary" kernels, meaning there's no reason for the algorithm to believe the sea behaves any differently from the land. This is easy to fix by masking out the sea but I haven't bothered.
So altogether, these examples show some of the nice features of Gaussian Process regression, and, along with the code, that the GPy module makes it pretty easy to put together this kind of analysis in Python.
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