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Rapists know your limits

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?

Wednesday 23rd July 2014 | feminism | Permalink / Comment

In mainland Britain, you are never more than 34 miles from a pub.

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.)

Sunday 8th June 2014 | openstreetmap | Permalink / Comment

I have been awarded a 5-year fellowship to research bird sounds

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.

zebra finches

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! :)

Tuesday 18th March 2014 | science | Permalink / Comment

How long it takes to get my articles published - update

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.

Monday 17th March 2014 | science | Permalink / Comment

Python scipy gotcha: scoreatpercentile

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:


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.)

Friday 14th February 2014 | IT | Permalink / Comment

How to analyse pan position per frequency of your sound files

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.

Friday 7th February 2014 | sound | 3 Comments
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